Authors & Works cited in this section (citations below):

Aguera y Arcas, Blaise. 2024. What is Life?
Allada, Ravi & Chung. “Circadian Organization of Behavior and Physiology in Drosophila.
Altay, Meniz, Y. Altay & Otto. “Parasitic Behavior of Self-replicating Molecules.
Ameta, Sandeep et al. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical
Amit, Daniel J. Modeling Brain Function: The World of Attractor Neural Networks.
Anderson, P.W. 1972. “More is Different.” 
Andrews, Steven S. et al. 2023. “Design patterns of biological cells
Asad, Muhammad. 1954/2000. The Road to Mecca.
Ashwin, Peter et al. “Network attractors and nonlinear dynamics of neural computation
Baedke, Jan et al. “Unknotting reciprocal causation between organism and environment
Baluska, Frantisek et al. Cellular and evolutionary perspectives on organismal cognition:
Baluska, Frantisek & Mancuso. 2021. “Individuality, self and sociality of vascular plants
Banerjee, Shiladitya et al. “The Actin Cytoskeleton as an Active Adaptive Material
Barabasi, Daniel et al. 2023. “Neuroscience Needs Network Science”
Barron, Andrew, Halina & Klein. “Transitions in cognitive evolution
Bechtel, & Bich. Rediscovering Bernard and Cannon: Restoring the Broader Vision of
Bechtel, W. & Bich. “Eating and Cognition in Two Animals without Neurons: Sponges and
Bechtel, William & Bich. “Using neurons to maintain autonomy: Learning from C. elegans.
Beer, Randall D. 2023. “On the Proper Treatment of Dynamics in Cognitive Science
Benkovic, Stephen & Hammes-Schiffer. “A Perspective on Enzyme Catalysis
Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.
Bich, L. & Skillings. There Are No Intermediate Stages: Organizational View on Development
Bich, L. et al. “Understanding Multicellularity: The Functional Organization of the Intercellular
Bich, Leonardo. 2024. Biological Organization.
Bottaccioli, F & A. “The suggestions of ancient Chinese philosophy and medicine for
Brembs, Bjorn. 2021. “The brain as a dynamically active organ.”
Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?”
Buzsaki, Gyorgy. 2019. The Brain from Inside Out.
Buzsaki, G. & Tingley. Cognition from the Body-Brain Partnership: Exaptation of Memory
Ciaunica, Anna et al. “The brain is not mental! coupling neuronal and immune cellular
Cisek, Paul & Kalaska. “Neural Mechanisms for Interacting with a World Full of ... Choices
Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality.
Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.
Cortes-Garcia, David et al. “The evolution of reproductive characters: an organismal-relational
Cruz-Rosas, Hugo et al. 2020. “Molecular shape as a key source of prebiotic information.
Davidi, Dan et al. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical,
Di Paolo, Ezequiel. Picturing Organisms and Their Environments: Interaction, Transaction,
Di Paolo, Ezequiel. 2018. “The Enactive Conception of Life.”
DiFrisco, James & Gawne. “Biological agency: a concept without a research program
DiFrisco, J. & Jaeger. “Homology of process: developmental dynamics in comparative biology
Dixon, James. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure
Drukarch, B. & Wilhelmus. “Thinking about the action potential... physical principles guiding
Du, Xing et al. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and
Ebitz, R. Becket & Hayden. “The population doctrine in cognitive neuroscience
Edelman, Gerald, Gally & Baars. 2011. “Biology of consciousness.”
El-Hani, C.N. & Nunes-Neto. “Life on Earth Is Not a Passenger, but a Driver: Explaining
Emlen, John et al. 1998. “How organisms do the right thing: The attractor hypothesis
Fabregas-Tejeda, A. & Sims. On the prospects of basal cognition research becoming fully
Fang, Xiaona et al. 2019. “Nonequilibrium physics in biology.
Favela, Luis. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment.
Favela, Luis & Amon. 2023. “Reframing Cognitive Science as a Complexity Science
Favela, Luis H. et al. 2021. “Empirical Evidence for Extended Cognitive Systems.
Favela, Luis. “What is next for affordances? Taking brains seriously in organism-environment
Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.”
Fields, Chris et al. “Morphological Coordination: A Common Ancestral Function Unifying
Fletcher, Daniel & Phillip Geissler. 2009. “Active Biological Materials.
Fotowat, H. et al. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies:
Frauenfelder, H. et al. Myoglobin: The hydrogen atom of biology and a paradigm of complexity
Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction.
Fusco, Giuseppe & Minelli. “Multiple developmental pathways in organisms with
Garaffa, Luigi. 2025. “Plasticity-Led (Not First) evolution: A Matter of Causal Relevance
Gardel, Margaret L. “Living matter–nexus of physics and biology in the 21st century.
Ginsburg, Simona & Jablonka. 2021. “Evolutionary transitions in learning and cognition
Gonzalez de Prado, & Saborido. Biological Purposes Beyond Natural Selection: Self-Regul
Good, Aaron. 2022. American Exception: Empire and the Deep State.
Gozen, Irep et al. 2022. “Protocells: Milestones and Recent Advances
Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition.
Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings
Henzler-Wildman, Katherine & D. Kern. 2007. “Dynamic personalities of proteins.”
Heylighen, Francis. “Relational Agency: A New Ontology for Coevolving Systems
Heylighen, F. et al. Chemical Organization Theory...General Modeling Framework for Self-
Hopfield, J.J. & Tank. “‘Neural’ Computation of Decisions in Optimization Problems.
Huang, Sui et al. 2025. “The end of the genetic paradigm of cancer”
Inagaki, H. et al. “Discrete attractor dynamics underlies selective persistent activity in the frontal
Irani, Martin & Alderson. Tuning Criticality through Modularity in Biological Neural Networks
Jaeger, J. et al. “Naturalizing relevance realization: why agency and cognition are fundamentally
Jaeger, J. et al. “Naturalizing relevance realization: why agency and cognition are fundamentally
Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do
Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems
Jekely, Gaspar et al. “Reafference and the origin of the self in early nervous system evol
Jiang, Ting-Ting & Li. “Review on the systems biology research of Yin-deficiency-heat
Juarrero, Alicia. Context Changes Everything: How Constraints Create Coherence.
Kahana, Amit et al. “Attractor dynamics drives self-reproduction in protobiological catalytic
Kalkman, David. New problems for defining animal communication in informational terms
Kandel, Eric R. et al. (eds.) Principles of Neural Science, 6th Edition.
Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind.
Kauffman, Stuart & Roli. “Beyond the Newtonian Paradigm: A Statistical Mechanics of
Keenan, Jesse M. 2025. North: The Future of Post-Climate America.
Keijzer, Fred. “Moving and sensing without input and output: early nervous systems
Keijzer, Fred. 2021. “Demarcating cognition: the cognitive life sciences
Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.
Kelty-Stephen, D. & Mangalam. “Turing’s cascade instability supports the coordination of
Kelty-Stephen, D. et al. A Tutorial on Multifractality, Cascades, and Interactivity for Empirical
Khona, Mikail & Ila Fiete. 2022. “Attractor and integrator networks in the brain.
Kirschner, Marc, John Gerhart & Tim Mitchison. 2000. “Molecular ‘Vitalism’”.
Korenic, Andrej et al. “Symmetry breaking and functional incompleteness in biological systems.
Kosc, Thomas et al. 2024. “Thermodynamic consistency of autocatalytic cycles
Krakauer, John et al. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias
Kristan, William B., Jr. 2016. “Early evolution of neurons.”
Lala, Kevin. 2025. “A developmentalist’s view of inheritance.
Lala, Kevin et al. Evolution Evolving: The Developmental Origins of Adaptation and
Levin, M. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from
Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process
Levin, M. “Darwin’s agential materials: evolutionary implications of multiscale competency
Li, Yiwei et al. “The cell as matter: Connecting molecular biology to cellular functions
Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.
Lyon, Pamela et al. 2021. “Reframing cognition: getting down to biological basics
Ma, Tao et al. “Bridging the gap between traditional Chinese medicine and systems biology
Mangalam, M. et al. “From Turing to Gibson: Implications of Affordances for the Sciences of
Marshall, Peter “Towards a Biologically Coherent Account of the Brain and How it Develops
Matange, Kavita et al. 2025. “Biological Polymers: Evolution, Function, and Significance
McCarthy, Daniel. 2025. “The New Right’s New Deal.”
Menatti, Laura, Bich & Saborido. “Health and environment from adaptation to adaptivitity
Milnor, John. 1985. “On the Concept of Attractor.”
Milnor, John W. 2006. “Attractor”.
Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.
Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave us Free Will.
Mittal, Divyansh & Narayanan. 2024. “Network motifs in cellular neurophysiology
Morowitz, Harold J. 1992. Beginnings of Cellular Life
Mueller, Felix et al. 2022. “A prebiotically plausible scenario of an RNA-peptide world
Needleman, D. & Dogic. “Active matter at the interface between materials science and cell
Newman, Stuart et al. 2006. “Before programs: The physical origination of multicellular forms
Newman, Stuart “Form, Function, Agency: Sources of Natural Purpose in Animal Evolution
Noble, S. et al. The tip of the iceberg: A call to embrace anti-localizationism ... neuroscience
Osiurak, Francois & Federico. “Affordance and Tool Use: A Neurocognitive Approach
Page, Scott. 2011. Diversity and Complexity.
Papo, David et al. 2017. “Editorial: On the relation of dynamics and structure in brain networks
Pascal, R. & Pross. “Toward the Physicalization of Biology: Seeking the Chemical Origin of
Pattee, H.H. 2007. “Laws, Constraints and Modeling Relation – History and Interpretations
Pessoa, Luiz. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven
Pillsbury, M. The Hundred-Year Marathon: China’s Secret Strategy to Replace America
Pilorz, Violetta et al. 2018. “The role of the circadian clock system in physiology
Potter, H. & Mitchell. Beyond Mechanism–Extending Our Concepts of Causat... in Neurosci
Raja, Vicente & Gramann. 2025. “Ecological Resonance Is Reflected in Human Brain
Ratcliff, W. et al. “Experimental evolution of an alternating uni- and multicellular life cycle
Rizos, Iris et al. Life cycle strategies in free-living unicellular eukaryotes: Diversity, evolut
Rolls, Edmund T. 2009. “Attractor networks.”
Rosslenbroich, Bernd et al. 2024. “Agency as an Inherent Property of Living Organisms
Sanborn, Adam et al. 2025. “Noise in Cognition: Bug or Feature?
Schlichting, Carl. “Plasticity and Evolutionary Theory: Where We Are and Where We Should
Schulze-Makuch, D. et al. “The Rise of Complexity: Pavilion Lake Microbialites Suggest
Shapiro, James A. 2023. “Evolutionary Change Is Naturally Biological and Purposeful
Shefferson, Richard et al. 2017. The Evolution of Senescence in the Tree of Life.
Snell-Rood, Emilie & Ehlman. 2021. “Ecology and Evolution of Plasticity.
Sole, Ricard et al. 2024. “Fundamental constraints to the logic of living systems
Sporns, Olaf. 2022. “The complex brain: connectivity, dynamics, information.”
Starhawk & Valentine. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing,
Sultan, Sonia 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms
Tang, Shiping. “Coordination of Pathways in Metazonas: An Integrated Framework
Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.”
Tang, Shiping. The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA
Te Vrugt, Michael & Wittkowski. “Metareview: a survey of active matter reviews
Toman, Jan & Flegr. “Stability-based sorting: The forgotten process behind ... evolution
Treur, Jan. On Structure, Dynamics, and Adaptivity for Biological and Mental Processes: a
Tsao, Thomas & D. Tsao. “A topological solution to object segmentation and tracking
Van Oudenaarden, A. & Theriot. “Cooperative symmetry-breaking by actin polymerization in
Varley, Thomas et al. “Identification of brain-like complex... architectures in Xenopus
Walsh, Denis M. “Evolutionary Foundationalism: The Myth of the Chemical Given
Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability
Wang, Richard Liangchen. 2025. “Life is chemistry plus information.
Wang, Siyu et al. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex
Warren, W. H. “Information is Where You Find It: Perception as an Ecologically Well-Posed
Watson, Richard A. & Szathmary. 2015. “How can evolution learn?
Watson, Richard et al. “Evolutionary Connectionism: Algorithmic Principles Underlying
Wellmann, Janina. 2024. Biological Motion: A History of Life.
West-Eberhard, Mary Jane. 2021. “Foreword: perspective on ‘plasticity’
Westlin, C. et al. Improving the study of brain-behavior relationships by revisiting basic
Wimsatt, William. Re-Engineering Philosophy for Limited Beings: Piecewise Approximations
Withagen, Rob. The Gibsonian movement and Koffka’s Principles of Gestalt Psychology
Witvliet, Daniel et al. Connectomes across development reveal principles of brain maturation
Wong, Michael et al. “On the roles of function and selection in evolving systems
Yin, Henry. 2020. “The crisis in neuroscience.”
Yurchenko, Sergey. “Is information the other face of causation in biological (conscious)
Yuste, Rafael et al. 2024. “Neuronal ensembles: Building blocks of neural circuits.
Zhang, GuangJun & Levin. “Bioelectricity is a universal multifaced signaling cue in living

Citations collected in 2025 (works listed above):

"Helmholtz [in 1859] found that the axons of nerve cells conduct electricity much more slowly than wires do, and they do so by means of a novel, wavelike action that propagates actively at various speeds up to approximately 90 feet per second!” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 76.


“Voltage-gated channels generate action potentials that carry information within neurons, while chemical transmitter-gated channels transmit information between neurons (or between neurons and muscle cells) by generating synaptic potentials in postsynaptic cells.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 98.


“In reviewing our results [from the study of Aplysia a giant marine snail by using combinations of weak tactile signals and strong shocks to the tail to mimic habituation, sensitization, and classical conditioning], I could not help being reminded of the two opposing philosophical views of mind that had dominated Western thought from the seventeenth century onward–empiricism and rationalism.....

“Neither field [psychoanalysis or biology as career choices for Kandel] could resolve the conflict between the empiricist and rationalist views of mind as long as the resolution required a direct examination of the brain. But examining the brain was just what we had begun to do. In the gill-withdrawal reflex of this simplest of organisms, we saw that both views had merit–in fact, they complemented each other. The anatomy of the neural circuit is a simple example of Kantian a priori knowledge [reflex reactions], while changes in the strength of particular connections in the neural circuit reflect the influence of experience [for long term memory]. Moreover, consistent with Locke’s notion that practice makes perfect, the persistence of such changes underlies memory.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. pp. 202-3.


“This anatomical change [from long-term sensitization or habituation] is expressed in several ways. Bailey and Chen found that a single sensory neuron has approximately 1300 presynaptic terminals with which it contacts about 25 different target cells–motor neurons, excitatory interneurons, and inhibitory interneurons. Of the 1300 presynaptic terminals, only about 40 percent have active synapses, and only these synapses have the machinery for releasing a neurotransmitter. The remaining terminals are dormant. In long-term sensitization, the number of synaptic terminals more than doubles (from 1300 to 2700), and the proportion of active synapses increases from 40 percent to 60 percent. In addition, there is an outgrowth from the motor neuron to receive some of the new connections. In time, as the memory fades and the enhanced response returns to normal, the number of presynaptic terminals drops from 2700 to about 1500, or slightly more than the initial number. This residual growth presumably is responsible for the fact, first discovered by Ebbinghaus, that an animal can learn a task more readily a second time. In long-term habituation, on the other hand, the number of presynaptic terminals drops from 1300 to about 850, and the number of active terminals diminishes from 500 to about 100–an almost complete shutdown of synaptic transmission.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. pp. 213-4.


“The synaptic potential between the sensory and motor neurons lasts only milliseconds, yet we had observed that a shock to Aplysia’s tail enhances glutamate release and synaptic transmission for many minutes. How does this come about? As my colleagues and I focused on the question, we noticed something curious. The strengthening of the synaptic connection between the sensory and motor neuron is accompanied by a very slow synaptic potential in the sensory cell, one that lasts for minutes rather than the milliseconds typical of synaptic potentials in the motor neuron. We soon found that the shock to Aplysia’s tail activates a second class of sensory neurons, one that receives information from the tail. These tail sensory neurons activate a group of interneurons that acts on the sensory neuron from the siphon. It is these interneurons that produce the remarkably slow synaptic potential....

“We found that the interneurons activated by a shock to Aplysia’s tail release a neurotransmitter called serotonin. Moreover, the interneurons form synapses not only on the cell body of the sensory neurons but also on the presynaptic terminals, and they not only produce a slow synaptic potential but also enhance the sensory cell’s release of glutamate onto the motor cell. In fact, we could simulate the slow synaptic potential, the enhancement of synaptic strength, and the strengthening of the gill-withdrawal reflex simply by applying serotonin to the connections between the sensory and motor neurons.

“We called these serotonin-releasing interneurons modulatory interneurons because they do not mediate behavior directly; rather, they modify the strength of the gill-withdrawal reflex by enhancing the strength of the connections between sensory and motor neurons.

“These findings caused us to realize that there are two kinds of neural circuits important in behavior and learning: mediating circuits, which we had characterized earlier, and modulating circuits, which we were just beginning to characterize in detail. Mediating circuits produce behavior directly and are therefore Kantian in nature. These are the genetically and developmentally determined neuronal components of the behavior, the neuronal architecture. The mediating circuit is made up of the sensory neurons that innervate the siphon, the interneurons, and the motor neurons that control the gill-withdrawal reflex. With learning, the mediating circuit becomes the student and acquires new knowledge. The modulating circuit is Lockean in nature; it serves as a teacher. It is not directly involved in producing a behavior but instead fine-tunes the behavior in response to learning by modulating–heterosynaptically–the strength of synaptic connections between the sensory and motor neurons. Activated by a shock to the tail, a completely different part of the body than the siphon, the modulating circuit teaches Aplysia to pay attention to a stimulus to the siphon that is important for its safety.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. pp. 222, 223.


“Thus, even though I had long been taught that the genes of the brain are the governors of behavior, the absolute masters of our fate, our work showed that, in the brain as in bacteria, genes also are servants of the environment. They are guided by events in the outside world. An environmental stimulus–a shock to an animal’s tail–activates modulatory interneurons that release serotonin. The serotonin acts on the sensory neuron to increase cyclic AMP and to cause protein kinase A and MAP kinase to move to the nucleus and activate CREB. The activation of CREB, in turn, leads to the expression of genes that changes the function and the structure of the cell....

“Repeated stimulation causes protein kinase A and MAP kinase to move to the nucleus, where protein kinase A activates CREB-1 and MAP kinase inactivates CREB-2. Thus long-term facilitation of synaptic connections requires not only a switching on of some genes, but also the switching off of others.

“As these exciting findings were emerging in the laboratory, I was struck by two things. First, we were seeing the Jacob-Monod model of gene regulation applied to the process of memory storage. Second, we were seeing Sherrington’s discovery of the integrative action of the neuron [excitation and inhibition] carried to the level of the nucleus. I was amazed by the parallels: on the cellular level, excitatory and inhibitory synaptic signals converge on a nerve cell, while on the molecular level, one CREB regulatory protein facilitates gene expression and the other inhibits it. Together, the two CREB regulators integrate opposing actions.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 264.


“As a result of a prior stimulus, the sensory cell’s nucleus has sent dormant messenger RNA (mRNA) to all axon terminals. Five [artificial] pulses of serotonin at one terminal convert a prion-like protein (CPEB [=CREB?]) that is present at all synapses into a dominant, self-perpetuating form. Dominant CPEB can convert recessive CPEBs to the dominant form. Dominant CPEB activates dormant messenger RNA. The activated messenger RNA regulates protein synthesis at the new synaptic terminal, stabilizes the synapse, and perpetuates the memory.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 274.


“We soon found that in the sensory neurons of the gill-withdrawal reflex, the conversion of CPEB from the inactive, non-propagating form [a protein that also exists as a prion in another form that is crucial for activating mRNA that builds more synapses to strengthen long term memory] to the active, propagating form is controlled by serotonin, the transmitter that is required for converting short- to long-term memory. In its self-perpetuating form, CPEB maintains local protein synthesis. Moreover, the self-perpetuating state is not easily reversed.

“These two features make the new variant of the prion ideally designed for memory storage. Self-perpetuation of a protein that is critical for local protein synthesis allows information to be stored selectively and in perpetuity at one synapse, and not, Kausik [a colleague] soon discovered, at the many others that a neuron makes with its target cells.

“Beyond discovering a new prion’s relevance to the persistence of memory or even to the functioning of the brain, Kausik and I had found two new biological features of prions. First, a normal physiological signal–serotonin–is critical for converting CPEB from one form to another. Second, CPEB is the first self-propagating form of a prion known to serve a physiological function–in this case, perpetuation of synaptic facilitation and memory storage. In all other cases previously studied, the self-propagating form either causes disease and death by killing nerve cells or, more rarely, is inactive.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. pp. 273-5.


“He [John O’Keefe at University College, London in the 60s or 70s] found that neurons in the hippocampus of the rat register information not about a single sensory modality–sight, sound, touch, or pain–but about the space surrounding the animal, a modality that depends on information from several senses. He went on to show that the hippocampus of rats contains a representation–a map–of external space and that the units of that map are the pyramidal cells of the hippocampus, which process information about place. In fact, the pattern of action potentials in these neurons is so distinctively related to a particular area of space that O-Keefe referred to them as ‘place cells’....

“Since space involves information acquired through several sensory modalities, it raised the questions: How are these modalities brought together? How is the spatial map established? Once established, how is the spatial map maintained?” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 282.


“The Gestalt psychologists argued that our coherent perceptions are the end result of the brain’s built-in ability to derive meaning from the properties of the world, only limited features of which can be detected by the peripheral sensory organs. The reason that the brain can derive meaning from, say, a limited analysis of a visual scene is that the visual system does not simply record a scene passively, as a camera does. Rather, perception is creative: the visual system transforms the two-dimensional patterns of light on the retina of the eye into a logically coherent and stable interpretation of a three-dimensional sensory world. Built into neural pathways of the brain are complex rules of guessing; those rules allow the brain to extract information from relatively impoverished patterns of incoming neural signals and turn it into a meaningful image. The brain is thus the ambiguity-resolving machine par excellence?” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 296.


“The brain does not simply take the raw data that it receives through the senses and reproduce it faithfully. Instead, each sensory system first analyzes and deconstructs, then restructures the raw, incoming information according to its own built-in connections and rules–shades of Immanuel Kant!

“The sensory systems are hypothesis generators.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 302.


“... other aspects of visual perception–motion, depth, form, and color–are segregated from one another and conveyed in separate pathways to the brain, where they are brought together and coordinated into a unified perception. An important part of this segregation occurs in the primary visual area of the cortex, which gives rise to two parallel pathways. One pathway, the ‘what’ pathway, carries information about the form of an object: what the object looks like. The other, the ‘where’ pathway, carries information about the movement of the object in space: where the object is located. These two neural pathways end in higher regions of the cortex that are concerned with more complex processing.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 302.


“The binding problem is thought to be resolved by bringing into association temporarily several independent neural pathways with discrete functions.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 304.


“... for some representations of space the brain typically uses egocentric coordinates (centered on the receiver), encoding, for example, where a light is relative to the fovea or where an odor or touch comes from with respect to the body.... For other behaviors, like memory for space in the mouse or in people, it is necessary to encode the organism’s position relative to the outside world and the relationship of external objects to one another. For these purposes the brain uses allocentric coordinates (centered on the world).” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 308.


“O’Keefe found that as an animal walks around an enclosure, some place cells fire action potentials only when that animal moves into a particular location, while others fire when the animal moves to another place. The brain breaks down its surroundings into many small, overlapping areas, similar to a mosaic, each represented by activity in specific cells in the hippocampus. This internal map of space develops within minutes of the rat’s entrance into a new environment.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 309.


“It is in large part because of selective attention that internal representations do not replicate every detail of the external world and sensory stimuli alone do not predict every motor action.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 311.


“Thus, when a mouse is forced to pay a lot of attention to a new environment, by having to learn a spatial task at the same time that it is exploring the new space, the spatial map remains stable for days and the animal readily remembers a task based on knowledge of that environment.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 312.


“[William] James wrote: ‘We feel sorry because we cry, angry because we strike, afraid because we tremble, and not that we cry, strike or tremble because we are sorry, angry or fearful, as the case may be.’ According to this view, emotions are cognitive responses to information from bodily states mediated in good part by the autonomic nervous systems. Our everyday experience confirms that information from the body contributes to emotional experience....

“With time it became clear, however, that the James-Lange [Danish psychologist Carl Lange] theory explains only one aspect of emotional behavior. If physiological feedback were the only controlling factor, emotions should not outlast physiological changes. Yet feelings–the thoughts and actions in response to emotion–can be sustained long after a threat has subsided. Conversely, some feelings arise much more rapidly than changes in the body. Thus there may be more to emotions than the interpretation of feedback from physiological changes in the body.

“An important modification of the James-Lange view has come from the neurologist Antonio Damasio, who argues that the experience of emotion is essentially a higher order representation of the bodily reactions and that this representation can be stable and persistent. As a result of Damasio’s work, a consensus is emerging on how emotions are generated. The first step is thought to be the unconscious, implicit evaluation of a stimulus, followed by physiological responses, and finally by conscious experience that may or may not persist.” Kandel, Eric. 2006. In Search of Memory: The Emergence of a New Science of Mind. NY: W.W. Norton. p. 341.


“Ecosex is the new paradigm for many people our age: taking the Earth as our powerful lover and treating her huge energies with gentleness and respect.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 21.


“A relationship may be valuable simply because it affords pleasure to those involved; there is nothing wrong with sex for sex’s sake. Or it might involve sex as a pathway to other lovely things–intimacy, connection, companionship, even love–which in no way changes the basic goodness of the pleasurable sex.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 27.


“When it is not safe, accepted, or welcomed to say, ‘No thank you’ to sex, building a sex-positive culture can become impossible.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 53.


“Much of this conflict [rape, drugging dates, child molestation, etc.] is the consequence of our absurd cultural insistence that in sex, men should be the initiators and women the withholders. Thus, some people learn that they are supposed to be pushy and others that saying anything but no is, well, slutty.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 54.


“The cultural ban on having sex with your friends is an inevitable offshoot of a societal belief that the only acceptable reason to have sex is to lead to a monogamous, marriage-like relationship. We believe, on the other hand, that friendship is an excellent reason to have sex, and that sex is an excellent way to maintain a friendship....

“With practice, we can develop an intimacy based on warmth and mutual respect, much freer than desperation, neediness, or the blind insanity of falling in love–that’s why the relationships between ‘friends with benefits’ are so immensely valuable. When we acknowledge the love and respect and appreciation that we share with lovers we would never marry, sexual friendships can become not only possible but preferred. So while you’re worrying that your sexual desire could cost you your best friend, the more experienced slut could be wondering why you are the only friend they’ve never fucked.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 62.


“One of the newer terms in the poly lexicon, relationship anarchy, refers to a lifestyle decision not to take one partner as a ‘primary’ and others as ‘secondaries’ (or any hierarchy of that kind) but instead to maintain each relationship as separate and to make as few rules as possible.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 63.


“Sex as audition happens because most people have no script for sexual intimacy in the mid-range between complete stranger and total commitment.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 64.


“If you are a single person in any open sexual lifestyle, you must pay attention to how you are getting your sexual, emotional, and social needs met. You can do this in an infinite variety of ways. The important thing is to be aware of your needs and wants so you can go about getting them met with full consciousness. If you pretend that you have no needs for sex, affection, or emotional support, you are lying to yourself, and you will wind up trying to get your needs met by indirect methods that won’t work very well.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 64.


“People new to polyamory tend to spend a lot of energy defining their boundaries. They usually focus more at first on what they don’t want their partner to do.... However, as partnerships become more sophisticated at operating the boundaries of their relationship, they tend to focus more on what they would enjoy and then strategize about how they can make it safe.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 66.


“A basic precept of intimate communication is that each person owns their own feelings. No one ‘makes’ you feel jealous or insecure–the person who makes you feel that way is you. No matter what the other person is doing, what you feel in response is determined inside you. Even when somebody deliberately tries to hurt you, you make a choice about how you feel. You might feel angry or hurt or frightened or guilty. The choice, not usually conscious, happens inside you....

“On the other hand, when you own your feelings, you have lots of choices. You can talk about how you feel, you can choose whether or not you want to act on those feelings, you can learn how to understand yourself better, you can comfort yourself or ask for comfort. Owning your feelings is basic to understanding the boundaries of where you end and the next person begins and the perfect first step toward self-acceptance and self-love.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. pp. 88, 89.


“Communities based on sex and intimacy work best when everybody has respect for everybody’s relationships, which includes not only lovers but also children, families of origin, neighbors, exes, and so on.” Hardy, Janet W. & Dossie Easton. 2017. The Ethical Slut, Third Edition. California: Ten Speed Press. p. 94.


“Aggregativity. The condition of a system property in which it can legitimately be said that it is ‘nothing more’ than the properties of its parts, justifying nothing-but-ism. For this to be true, roughly, the system property must not depend upon the mode of organization of the system’s parts. (One productive way of defining a property as emergent is to say that it does depend upon the mode of organization of the parts, so aggregativity can be regarded as the opposite of emergence.)” Wimsatt, William. 2007. Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Harvard UP. p. 353.


“When we look at a painting, for example, we explore it with a series of quick eye movements (saccades) that redirect the fovea to different objects of interest in the visual field. The brain must take into account these eye movements in the course of producing an interpretable visual image from the light stimuli in the retina.

“As each saccade brings a new object onto the fovea, the image of the entire visual world shifts on the fovea. These shifts occur several times per second, such that after several minutes the record of movement is a jumble. With such constant movement, visual images should resemble an amateur video in which the image jerks around because the camera operator is not skilled at holding the camera steady. In fact, however, our vision is so stable that we are ordinarily unaware of the visual effects of saccades. This is so because the brain makes continual adjustments to the images falling on the retina after each saccade.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael E. Goldberg & Robert H. Wurtz, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 582.


“Finally, there is a second potential disruption of vision produced by saccades: a blur as the saccade sweeps the visual scene across the retina. The blur is not seen, however, because neuronal activity in a number of visual areas is suppressed around the time of every saccade.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael E. Goldberg & Robert H. Wurtz, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 587.


“Large changes in the visual scene that occur outside the focus of attention are often missed until the subject directs attention to them, a phenomenon referred to as change blindness.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael E. Goldberg & Robert H. Wurtz, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 588.


“Laboratory studies of reflexes in animals from the 1950s and onward demonstrated that descending motor pathways and afferent sensory pathways converge on common interneurons in the spinal cord. Later research in intact animals and in humans engaged in normal behavior confirmed that the neural circuitries in the spinal cord take part in conveying and shaping the motor command to the muscles by integrating descending motor commands and sensory feedback signals.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Jens Bo Nielsen & Thomas M. Jessell, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 762.


“Most reflex pathways involve internneurons. One such reflex pathway is that of the flexion-withdrawal reflex, in which a limb is quickly withdrawn from a painful stimulus. Flexion-withdrawal is a protective reflex in which a discrete stimulus causes all the flexor muscles in that limb to contract coordinately. We know that this is a spinal reflex because it persists after complete transection of the spinal cord.

“The sensory signal of the flexion-withdrawal reflex activates divergent polysnaptic reflex pathways. One excites motor neurons that innervate flexor muscles of the stimulated limb, whereas another inhibits motor neurons that innervate the limb’s extensor muscles. This reflex can produce an opposite effect in the contralateral limb, that is, excitation of extensor motor neurons and inhibition of flexor motor neurons. This cross-extension reflex serves to enhance postural support during withdrawal of a foot from a painful stimulus. Activation of the extensor muscles in the opposite leg counteracts the increased load caused by lifting the stimulated limb. Thus, flexion-withdrawal is a complete, albeit simple, motor act.

“Although flexion reflexes are relatively stereotyped, both the spatial extent and the force of muscle contraction depend on stimulus intensity. Touching a stove that is slightly hot may produce moderately fast withdrawal only at the wrist and elbow, whereas touching a very hot stove invariably leads to a forceful contraction at all joints, leading to rapid withdrawal of the entire limb. The duration of the reflex usually increases with stimulus intensity, and the contractions produced in a flexion reflex always outlast the stimulus.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Jens Bo Nielsen & Thomas M. Jessell, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. pp. 770-772.


“Sensory feedback and descending motor commands interact at common spinal neurons to produce voluntary movements....

“When separate stimuli are reduced in intensity to just below threshold for evoking a synaptic potential, combining the stimulations at appropriate intervals makes the synaptic potential reappear. This provides evidence of convergence of the sensory fibers and the descending pathways onto common interneurons in the reflex pathway....

“Direct evidence that sensory feedback helps to shape voluntary motor commands through spinal reflex networks in humans comes from experiments in which sensory activity in length- and force-sensitive afferents has suddenly been reduced or abolished....

“Stretch reflex pathways can contribute to the regulation of motor neurons during voluntary movements and during maintenance of posture because they form closed feedback loops. For example, stretching a muscle increases activity in spindle sensory afferents, leading to muscle contraction and consequent shortening of the muscle. Muscle shortening in turn leads to decreased activity in spindle afferents, reduction of muscle contraction, and lengthening of the muscle.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Jens Bo Nielsen & Thomas M. Jessell, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 773.


“Reciprocal innervation is useful not only in stretch reflexes but also in voluntary movements. Relaxation of the antagonist muscle during a movement enhances speed and efficiency because the muscles that act as prime movers are not working against the contraction of opposing muscles.

“The [Ia, particular type of interneuron] inhibitory interneurons receive inputs from collaterals of the axons of neurons in the motor cortex that make direct excitatory connections with spinal motor neurons. This organizational feature simplifies the control of voluntary movements, because higher centers do not have to send separate commands to the opposing muscles.

“It is sometimes advantageous to contract both the prime mover and the antagonist at the same time. Such co-contraction has the effect of stiffening the joint and is most useful when precision and joint stabilization are critical. An example of this phenomenon is the co-contraction of flexor and extensor muscles of the elbow immediately before catching a ball.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Jens Bo Nielsen & Thomas M. Jessell, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 775.


“All movements activate receptors in muscles, joints, and skin. Sensory signals generated by the body’s own movements were termed proprioceptive by Sherrington, who proposed that they control important aspects of normal movements....

“A similar situation exists in the walking systems of many animals; sensory signals generated near the end of the stance phase initiate the onset of the swing phase. Proprioceptive signals can also contribute to the regulation of motor activity during voluntary movements, as shown in studies of individuals with sensory neuropathy of the arms. These patients display abnormal reaching movements and have difficulty in positioning the limb accurately because the lack of proprioception results in a failure to compensate for the complex inertial properties of the human arm.

“Therefore, a primary function of proprioceptive reflexes in regulating voluntary movements is to adjust the motor output according to the changing bio-mechanical state of the body and limbs. This adjustment ensures a coordinated pattern of motor activity during an evolving movement and compensates for the intrinsic variability of motor output.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Jens Bo Nielsen & Thomas M. Jessell, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 779.


“Scientists have been intrigued with the neural mechanisms of locomotion since the beginning of the 20th century, when pioneering work ... showed that the isolated spinal cord of the cat is able to generate the basic aspects of locomotor activity and subsequently that this capacity was intrinsic to the spinal cord. Throughout the 20th century, major advances were made in detailing both the rhythm- and pattern-producing capacities of the spinal cord, leading ultimately to the groundbreaking concept of a central pattern generator for locomotion in the spinal cord.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 783.


“CPGs [central pattern generators] have now been identified and analyzed in many rhythmic motor systems, including those controlling over-ground locomotion, swimming, flying, respiration, and swallowing, in both invertebrates and vertebrates.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. pp. 791-2.


“Mechanoreceptors in the skin, including some nociceptors [for pain], have a powerful influence on the CPG for walking. One important function of these receptors is to detect obstacles and adjust stepping movements to avoid them. A well-studied example is the corrective reaction to stumbling in cats.

“A mild mechanical stimulus applied to the dorsal part of the paw during the swing phase produces excitation of flexor motor neurons and inhibition of extensor motor neurons, leading to rapid flexion of the paw away from the stimulus and elevation of the leg in an attempt to step over the object. Because this corrective response is readily observed in spinal cats [where connections to the brain have been severed or paralyzed], it must be produced to a large extent by circuits entirely contained within the spinal cord.

“One of the interesting features of the corrective reaction is that corrective flexion movements are produced only if the paw is stimulated during the swing phase. An identical stimulus applied during the stance phase produces the opposite response–excitation of extensor muscles that reinforces the ongoing extensor activity. This extensor action is appropriate; if a flexion reflex were produced during the stance phase, the animal might collapse because it is being supported by the limb. This is an example of a phase-dependent reflex reversal. The same stimulus can excite one group of motor neurons during one phase of locomotion while activating the antagonist motor neurons during another phase.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. pp. 798-9.


“Although the basic motor patterns for locomotion are generated in the spinal cord, the initiation, selection, and planning of locomotion require activation of supraspinal structures, including the brain stem, the basal ganglia, cerebellum, and cerebral cortex. Supraspinal regulation of stepping provides a number of behavioral modifications that cannot be mediated by spinal circuits alone. These include the voluntary initiation of locomotion and the regulation of speed; postural regulation, including weight support, balance, and interlimb coordination; and the planning and execution of anticipatory modifications of gait, particularly visually guided modifications.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. pp. 799-800.


“The locomotor networks in the spinal cord require a command or start signal from supraspinal regions to initiate and maintain their activity. The major neuronal structure involved in the initiation in vertebrates is a region in the midbrain called the mesencephalic locomotor region (MLR).... Tonic electrical stimulation in this area in the resting animal increased postural tonus so that the animal stood up and then started to walk. As the intensity of stimulation rose, the speed of locomotion increased and alternating gaits switched to synchronous gaits such as gallop or bound.

“Later studies with electrical stimulation confirmed the presence of the MLR in all vertebrates, suggesting that the MLR is evolutionarily conserved from the oldest vertebrates to humans....

“Another brain area that evokes locomotion when stimulated is the subthalamic locomotor region (to be distinguished from the subthalamic nucleus). This region includes nuclei in the dorsal and lateral hypothalamus involved in various homeostatic features such as regulating feeding. Neurons in these areas project to neurons in the reticular formation and bypass the PPN and CNF, suggestiong a parallel pathway for initiating locomotion, possibly driven by the need to find food.

“The excitatory signals from CNF and PPN are relayed indirectly to the spinal cord by way of neurons in the brain stem reticular formation, which provide the final command signal to the locomotor networks in the spinal cord.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 800.


“An important aspect of locomotor control is the regulation of posture. This general term encompasses several types of behavior, including the production of the postural support on which locomotion is superimposed, the control of balance, the regulation of interlimb coordination in quadrupeds, and the modification of muscle tonus required to adapt to locomotion on slopes or during turning. In addition, anticipatory changes in posture precede changes in voluntary gait modifications, and compensatory changes in posture follow unexpected perturbations.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 802.


“Experiments in intact cats trained to step over obstacles attached to a moving treadmill belt show that precision locomotion is associated with considerable modulation of the activity of numerous neurons in the motor cortex. Other neurons in the motor cortex show a more discrete pattern of activity and are activated sequentially during different parts of the swing phase. The activity of these cortical neurons correlates with the periods of modified muscle activity required to produce the gait modifications in a similar manner to what occurs during reaching. Such subpopulations of neurons may serve to modify the activity of the groups of synergistic muscles required to produce flexible changes in limb trajectory.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 805.


“A major function of the cerebellum is to correct movement based on a comparison of the motor signals sent to the spinal cord and the movement produced by that motor command.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 806.


“During locomotion, the motor command (the central efference copy), the movement (the afference copy, via the DSCT), and the state of the spinal networks (the spinal efference copy, via the VSCT) are integrated within the cerebellulm and expressed as changes in the pattern of rhythmical discharge of Purkinje cells in the cerebellar cortex and neurons in the deep cerebellar nuclei. These signals from the deep cerebellar nuclei are then sent to the motor cortex and the various brain stem nuclei where they modulate descending signals to the spinal cord to correct any motor errors.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Trevor Drew & Ole Kiehn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 806.


“Given this close proximity of planning- and execution-related activity, even at the level of individual neurons, a major question is why planning-related neural activity does not immediately initiate a movement. What prevents the movement from being executed prematurely? It does not appear that planning-related activity simply fails to exceed a minimum threshold required to initiate the movement or that there is a separate overt braking mechanism that must be released to allow the movement to begin.

“A different way to interpret neural processing during the planning and execution of reaching that might provide answers to such questions comes from a dynamical-systems perspective. The idea is that cortical motor circuits form a dynamical system whose distributed activity patterns evolve in time as a function of their initial state, input signals, and stochastic neural response variability (‘noise’). Activity patterns during different stages of planning and execution thus reflect different states of the network, including a specific state during the delay period that can prepare the movement but not activate muscles.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Steven H. Scott & John F. Kalaska, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 833.


“There is a major feature of neural organization which is not included in connectionist models but which can act synergistically with parallel feedback and connectivity to greatly enhance computational power. This feature is that the biological system operates in a collective analog mode, with each neuron summing the inputs of hundreds or thousands of others in order to determine its graded output. An analog system is made powerful in computation by its ability to adjust simultaneously and self-consistently many interacting variables. Although very fast, analog summation is inevitably less accurate than digital summation. This compromise is not critical, however, in perceptual tasks formulated as optimization problems. The computational load of rapidly reducing this sensory input to the desired ‘good’ solution is already immense; inaccuracies and uncertainties are already present and the computational load is meaninglessly increased by high digital accuracy. Parallel analog computation in a network of neurons is thus a natural way to organize a nervous system to solve optimization problems.” Hopfield, J.J. & D.W. Tank. 1985. “‘Neural’ Computation of Decisions in Optimization Problems.” Biological Cybernetics. 52:141-152. p. 142.


“When compared to modern digital general purpose computers constructed with conventional silicon integrated circuits (VLSI), the ‘neural’ computational circuits we describe have qualitatively different features and organization. In VLSI the use made of analog calculations in [= “is”?] minimal. Each logic gate will typically obtain inputs from two or three others, and a huge number of independent binary decisions are made in the course of a computation. In contrast, each nonlinear neural processor (neuron) in a collective analog computational network gets inputs from tens or hundreds of others and a collective solution is computed on the basis of the simultaneous interactions of hundreds of devices.” Hopfield, J.J. & D.W. Tank. 1985. “‘Neural’ Computation of Decisions in Optimization Problems.” Biological Cybernetics. 52:141-152. p. 142.


“The tripartite state is comprised of three elements–the public state (i.e., the democratic state), the security state, and the deep state. The public state consists of those institutions that we learn about in high school civics classes and study in political science–the visible and formally organized institutions that comprise our elected federal, state, and local governments as well as the civil service bureaucracies associated with them. The security state is comprised of those institutions in charge of maintaining ‘security’ domestically and internationally. Notable security state organizations include the Pentagon, the Central Intelligence Agency, and the Federal Bureau of Investigation.

“The deep state is a more nebulous thing. In a 2015 article, I sparsely defined the deep state as ‘an obscured, dominant, supranational source of antidemocratic power.’ Back in 2013, the New York Times defined the deep state as ‘a hard-to-perceive level of government or super-control that exists regardless of elections and that may thwart popular movements or radical change’.... The institutions that exercise undemocratic power over state and society collectively comprise the deep state. The deep state is an outgrowth of the overworld of private wealth. It includes, most notably, the institutions that advance overworld interests through the synergy between the overworld and the underworld–as well as the national security organizations that mediate between them. Collectively, the dominance of deep state has diminished US democracy to such an extent that it is justified to describe ours as a deep state system and to speak of the tripartite state.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 5-6.


“One may conceive of political machines [formerly often found in large US cities] as the organizations which–in miniature–provide the best historical analogy to the current hypertrophied American deep state.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 7.


“The decline of US democracy has given rise to three crises to which the deep state system cannot adequately respond. The first crisis is the ever-present risk of nuclear omnicide–the extinction of humanity, by humanity. The second is the crisis of global climate change. The third is the crisis of inequality wherein a tiny minority owns most of the world’s wealth while globally tens of thousands of people die daily from lack of adequate access to food, potable water, and/or basic healthcare.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 11.


“The consensual aspects of hegemony are essential. Therefore, it can be said that any empire must constantly endeavor to maintain its hegemony....

“The US-led world order has been preserved and extended with varying degrees of consent along with covert or overt coercion, but always with the strategic goal of maintaining American hegemony. That the US has striven for imperial hegemony–i.e., hegemony in the pursuit of empire–is a foundational assumption of this work. The forces that compel the US to pursue empire are of key significance.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 15.


“The weakness of most mainstream liberal analysis perhaps stems from a reluctance to acknowledge the lawlessness and violence to which US foreign policy habitually resorts. Additionally, there seems to be a taboo against materialism–i.e., against critiques of capitalism. It is an open question whether this is due to anticommunism or post-Cold War triumphalism or the influence of capitalist-endowed foundations. The result is that the zeitgeist of academic approaches often obscures the anti-democratic totalizing effects of the corporate overworld upon foreign policy, international organizations, and global civil society.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 19-20.


“In short, the mainstream of the liberal traditional in IR [international relations]–like American political science in general–is too credulous about official narratives, too sanguine about the autonomy of international institutions, and too reluctant to apply materialist analysis when it is warranted. In particular, the mainstream does not acknowledge the extent to which militarism, covert/paramilitary violence, state lawlessness in foreign policy, and exploitative international institutions are all of a piece–essential aspects of the US-managed global capitalist system.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 20.


“Rather, the point is that there are normative conventions against approaches that utilize holistic critiques of (A) capitalism, (B) imperialism, and/or (C) the lawfulness of the state. Such taboos may serve to preclude or marginalize scholarship with considerable explanatory and predictive power. If it turned out that the political power of economic elites was the decisive factor that lay at the heart of some of social science’s most persistent problematics, the marginalization of thusly informed scholarship greatly handicaps US social science..... In case it needs to be stated, this book hits the taboo trifecta: the international and domestic lawlessness of the state is driven by the corporate rich whose interests are advanced by US imperialism.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 22.


“In other words, the US came to have a tripartite state system in which the overworld-directed deep state came to dominate over the public state and the security state.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 41.


“Tunander asserts that the dual state consists of a ‘democratic state’ operating according to legal prescriptions and a ‘security state’ which is more authoritarian and which exercises sovereignty most directly in cases of emergency.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 45; reference: Tunander, Ola. 2009. “Democratic State vs. Deep State – Approaching the Dual State of the West” Wilson, Eric. (ed.) Government of the Shadows: Parapolitics and Criminal Sovereignty. London: Pluto Press.


“For much of American history, it was the dignified Madisonian insitutions which governed, by and large. The modern institutional form of America’s ‘efficient’ institutions emerged in the aftermath of World War II, most decisively when President Truman signed the National Security Act of 1947. This legislation centralized control of the military under a newly created Secretary of Defense. The act also established the CIA, set up a new Joint Chiefs of Staff, and created the National Security Council. also under Truman, the National Security Agency was founded. Given the lasting import of these acts, Glennon uses the term Trumanite to describe America’s ‘efficient’ governing institution which is a network consisting of the hundreds of executive branch officials who make national security policy, i.e., the national security state.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 47-8; reference: Glennon, Michael J. 2016. National Security and Double Government. Oxford UP.


“Out of 668,000 civilian Defense Department employees, only 247 are politically appointed.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 49.


“Although they [Those from the “Trumanite” side of government, the efficient side for national security] believe in American exceptionalism, they are not ideologues. Rather, they strive to be rationalists–sober, responsible, neither too creative nor too predictable, and, most importantly, never naive. Given that national security is their charge, it is unsurprising that Trumanites must always appear to be tough.

“In reference to the Trumanite mindset, Glennon quotes C. Wright Mills who wrote, ‘[T]his cast of mind defines reality as basically military.’ Thus, the incentive structures within the Trumanite network encourage members to support wars in order to protect their professional and political credibility. With security defined in military rather than diplomatic terms, argues Glennon, the costs of underprotection must be internalized by the network. This creates powerful incentives to exaggerate actual threats and to create imaginary threats.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 50.


“During this era [the Cold War], the Trumanite network has been held together by the same things that, according to Bagehot, held Britain’s efficient institutions together. Specifically, these are: loyalty, collective responsibility, and most significantly, secrecy. The Trumanites work at rarified locations within the offices of powerful institutions like the Pentagon or CIA headquarters. They cannot speak about their work with friends or family members. Officials with access to classified information must sign nondisclosure agreements which require them to submit anything they write to prepublication review if it pertains to their work. Since information is power in the network, Trumanites are ‘both information gluttons and information misers.’” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 51; reference/subquote: Glennon, Michael J. 2016. National Security and Double Government. Oxford UP.


“So salient was this feature [greater immorality and the atrophy of private conscience] of society’s elite strata that Mills described it as ‘structural immorality.’ Unlike Habermas and the pluralists, Mills surmised that state secrecy and the triumph of propaganda allowed the elite to game and beguile the population. ‘Responsible interpretation of events’ was replaced by ‘the disguise of events,’ abetted by a ‘maze of public relations.’ Thusly did Mills identify a political system that was assuming ever more ‘holographic’ qualities as the state came to be defined by its ‘enemies’–or, rather, by the interminable, ever-present specter of allegedly existential crises in the form of communist/terrorist conspiracies.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 91; reference: Mills, C. Wright. 1956(2000). The Power Elite. Oxford UP.


“The notion of a supranational deep state component of a tripartite state provides a theoretical construct with which to address the decisive power wielded by elites whose interests dominate the security state, the public state, and the economy–and thus society at large.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 94.


“Questions of structure vs. agency loom large in the social sciences.... In short, the notion put forward here and elsewhere is that structure vs. agency is a false dichotomy. It is not a question of class or conspiracy which reproduces and manages overworld hegemony. Rather, it is class and conspiracy.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 94-5.


“As with Plato’s Magnesia [from The Republic], US elites benefit from the legitimacy conferred by the ostensibly open constitutional order while a deep state collectively functions as the nocturnal council to whom the city (i.e., the nation-state) is really entrusted. In this way, US politico-economic elites enjoy the benefits of living in a society with a considerable–if declining–degree of democratic legitimacy while simultaneously retaining unacknowledged authoritarian agency.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 106.


“The problem is not that liberalism values freedoms, rights, and the rule of law; the problem is that liberalism insists that freedoms, rights, and the rule of law define Western political systems. According to Tunander, this myopia has made liberal political science into ‘an ideology of the ‘sovereign,’ because indisputable evidence for the existence of the ‘sovereign’ [...] is brushed away as pure fantasy or ‘conspiracy’‘....

“Throughout the history of liberalism, there has been a contradiction between the liberal ideal of public sovereignty under the rule of law and the dictates of ‘security.’” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 114; reference/subquote: Tunander, Ola. 2009. “Democratic State vs. Deep State – Approaching the Dual State of the West” Wilson, Eric. (ed.) Government of the Shadows: Parapolitics and Criminal Sovereignty. London: Pluto Press. p. 68.


“American imperialists understood that US plans for global hegemony required primacy in the aerospace industry. But US aerospace predominance was going to be difficult to achieve without massive and profitable firms. The US could have embarked upon the creation of a national R&D division that employed the services of the best engineers and gave them access to government funding and facilities. But this was anathema to the corporate American overworld. Following the exposure of massive World War I profiteering, there were calls throughout the country and in Congress to nationalize the arms industry. But as with most substantial progressive reforms, this effort was crushed in top-down fashion by corporate American forces. For all the right’s rhetoric about government inefficiency, it seems that what the corporate overworld truly fears is efficiency in the public sector.... So for the mid-twentieth century corporate American hive-mind, a nationalized aerospace industry would have been a horrifying prospect.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 121-2.


“Orthodox historiography seems to encourage a posture of studied naivety about America’s rich elites and their domination of the state. Obviously, the pronouncements of policymakers are not typically framed in terms of profit or of commercial interests’ dominance over the US and the world. For mainstream journalists, historians, and social scientists, it is considered gauche to attribute elite actions to elite class interests. In other words, it is bad form to assume that the motives behind elite schemes and strategies derive from unstated, class-conscious imperatives such as (1) accruing ever more wealth and power and (2) maintaining their hegemony over society. If one is too unflinching in attributing the actions of wealthy and powerful people to a desire to aggrandize their wealth and power, one is a materialist–i.e., a Marxist–and thus beyond the pale.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 124.


“Economic insecurity and deprivation are key components of a hierarchical society. If they are eliminated, and if literacy and education are widespread, the elites have to deal with a population that is not as easily mesmerized by power and not compelled by necessity to submit to subjugation and exploitation in exchange for material security. Thus, an independent society with no underclass would likely have the wherewithal to topple the hegemony of its rentier class. It is not difficult for elites to grasp this by extrapolation. And elites in every classical, feudal, and capitalist civilization have essentially the same job description: They work to reproduce their own hegemony over society. Their class interests, elite education, and vast wealth allow them to organize and overcome the collective action problems that overwhelm non-elites.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 128.


“Meanwhile, right-wing forces were mobilizing in response to the legitimacy crisis that had peaked in 1968. In reaction, corporate lawyer and future Supreme Court Justice Lewis Powell penned his infamous 1971 manifesto: ‘Confidential Memorandum: Attack on the American Free Enterprise System,’ more commonly known as the ‘Powell Memo’.... One might even describe it as a deep state declaration of independence from democracy. The political activism of the 1960s challenged the hegemony of the superficially liberal power elite that C. Wright Mills began exposing in the mid-1950s. The 1970s rise in right-wing activism stemmed from elements that, prior to the 1960s, had been enjoying a near monopoly on power in the US. When progressive activism threatened the power elite’s monopoly on power, the response–besides a slew of assassinations–was a massive surge in right-wing elite activism, a counterrevolutionary crusade which fundamentally altered American politics and society.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 145.


“While differing in tone and content from the Powell Memo, the Trilateralists overlapped with the nascent New Right in their fear and disdain for the progressive currents of the 1960s. It is noteworthy that the Trilateralist perpsective came to dominate the Democratic Party during the Carter, Clinton, and Obama administrations. Thus, the Trilateralists–perhaps best described as the left-wing of the right-wing American political class–came to dominate the ‘left’ side of the US political system, i.e., the Democratic Party.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 146-7.


“While the two leaders would seem to be diametrically opposite in the US political system, there are striking parallels between the political fates of both John Kennedy and Richard Nixon. Both presidents failed to accommodate the antecedents of today’s neoconservative and neoliberal factions of the deep state. Kennedy had outraged the militarist ‘Prussian’ faction of the political establishment by refusing to commit the US to fighting wars during the Bay of Pigs fiasco, the crisis in Laos, the Berlin Crisis, the Cuban Missile Crisis, and in Vietnam where Kennedy had formalized a protracted (for political reasons) withdrawal process after initially boosting the US presence in the country. Kennedy also established back-channel talks with Castro and Khrushchev about improving US-Cuban relations and ending the Cold War, respectively. Nixon earned the enmity of these same forces by seeking detente and arms control with the Soviet Union, recognizing China, pursuing ‘Vietnamization’ and the eventual end of the war, and by calling for the removal of US troops from South Korea.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 147.


“In May of 1962, Kennedy told the US Chamber of Commerce,

“It costs the united States $3 billion a year to maintain our troops and our defense establishment and security commitments abroad. If the balance of trade is not sufficiently in our favor to finance this burden, we have two alternatives: one, to lose gold, as we have been doing; and two, to begin to withdraw our security commitments....

“The closing of the gold window was the culmination of a series of ad hoc responses to monetary crises faced by US policymakers. US officials knew that their decision was a power move to address US problems in such a way as to harm other countries’ interests. This was famously and most succinctly expressed at the time by US Treasure Secretary John Connolly when he told G-10 attendees in Rome, ‘The dollar is our currency, but it’s your problem.’ That said, the implications of the change were not fully understood at the time. Michael Hudson was the first economist to accurately assess the fundamental change after the Nixon Shock. Hudson first published Super Imperialism: The Economic Strategy of American Empire in 1972. The book explained how the US, by ending gold convertibility, forced the rest of the world to hold US Treasury bills as the basis for their cash reserves. Other countries recycled their surplus dollars by buying US Treasury securities, thereby financing US budget deficits. In effect, the new global financial system obliged other countries to pay for US military spending whether they wanted to or not.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 150, 153; subquote: Scott, Peter Dale. 1971. “The Vietnam War and the CIA-Financial Establishment.” Remaking Asia: Essays on the American Use of Power. Selden, Mark (ed.) Pantheon Books. p. 119.


“Although commonly believed to be related to the Arab–Israeli conflict and the Yom Kippur War specifically, the unprecedented 1973 spike in oil prices was orchestrated by the US to serve as economic statecraft against Western Europe and Japan.... Beginning in 1972, the US had been formulating plans for recycling the concomitant flood of petrodollars through private US banks. Nixon’s ambassador to Saudi Arabia stated that the motivation behind the policies was to deal a severe blow to Japanese and European economies, rather than to establish a new financial world order....

“Eventually, the high oil prices led to a huge accumulation of petrodollars by OPEC countries. Other governments had wanted these funds recycled through the IMF. Owing to US dominance in the Persian Gulf region, it was instead decided that the banks of the Atlantic world (led by American firms, naturally) would be the conduits for petrodollar recycling. To facilitate this, the US proceeded to use its power to abolish ‘financial repression’—the system of capital controls put in place by Bretton Woods. Given that the untethered dollar was the new form of global reserves, ‘liberating’ international financial markets like this served to preserve US financial dominance. The basis of US hegemony shifted from being a constellation of one-to-one power relationships and into being a structural, market-based type of power. The system was shored up throughout the decade by secret deals struck between Saudi Arabia and US Treasury secretaries whereby the Saudis would use their petrodollars to purchase US Treasury bills at special auctions. Additionally, US and Saudi officials arrived at a deal in which the kingdom agreed to extend its practice of requiring US dollars as payment fo oil sales.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 155, 157.


“Created by the Wall Street overworld and invaluable to its creators, the CIA acted decisively to pursue US hegemony and act according to a conception of the national interest that was congruent–if not identical–to the class interests of the corporate overworld. The presidency was also forced to accommodate such forces, but with the added imperative for the president to act as a democratic statesman seeking the approval of the American electorate. Then as now, politicians and the media could not diagnose the problems. The failure can be attributed to a Cold War superstructure in which empire was not honestly acknowledged or grappled with, in part because the imperial project had been legitimized by mythical references to the empire’s antithesis and to the empire’s ‘defensive’ strategy–communism and containment, respectively. Belying liberal democratic myths about public sovereignty and the rule of law, the exceptionist pursuit of empire was driven by the pinnacle of American wealth and power. In this context, the state’s crimes or ‘abuses’ at home and abroad are much easier to comprehend, as are the media’s otherwise inexplicable 1970s vacillations between being the public’s watchdog and being the lapdog of official Washington, so to speak.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 164-5.


“The November 1975 ‘Halloween Massacre’ was the denouement of Watergate as structural deep event. The ‘massacre’ consisted of a number of major personnel changes in Ford’s administration....

“With the benefit of hindsight, the end result of the shake-up was an administration that had moved so far to the right that it irreparably changed both political parties. The Republicans purged, essentially, the ‘liberal’ Rockefeller wing of the party and became predominately neo-conservative, a shift that was crystallized with Reagan’s election. The Democrats became, gradually, a neoliberal party and the new home to Rockefeller Republicans. The next Democratic presidential nominee was the Rockefeller-backed Jimmy Carter.... After the right-wing takeover, begun in earnest with the Halloween Massacre and sanctified with Reagan’s election, progressives were left with no real influence in the economic or foreign policy realms.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 166-7.


“... the election of Ronald Reagan was a milestone. It represents the end of a contentious period in which the public state coexisted uneasily with the national security state as components of a visible political system which operated in tandem with a deep political system. Reagan’s election marked the ascension of deep political forces to a position of sovereignty. Practically speaking, what emerged was an exceptionist tripartite state comprised of (1) a feckless public state, (2) a sprawling security state, and (3) the anti-democratic deep state to which they are subordinated. This consolidation and institutionalization of top-down power was such that US governance could thereafter be described as a deep state system.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 190.


“Historically, top-down, deep political power has been brought to bear by diverse non-state entities. In previous eras of US history, this was accomplished through outfits like the Pinkerton National Detective Agency–a private firm that, beginning in the mid-nineteenth century, famously provided clients with a variety of services including strike-breaking, infiltration, intelligence gathering, and counterintelligence. The Pinkertons often worked with the wealthy overworld of its day, in addition to receiving government contracts. Since World War II, numerous types of organizations have been created to carry out legitimate and illegitimate activities on behalf of the state and/or overworld actors. Some of these are descendants, so to speak, of the Pinkertons–i.e., private intelligence or even paramilitary firms. These have included Wackenhut, Booz Allen, SAIC, Stratfor, and Blackwater. Officials in these companies may retain and utilize high-level security clearances.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 247-8.


“[Peter Dale] Scott’s four structural deep events are: the Kennedy assassination, Watergate, Iran-Contra, and 9/11. These events have served to facilitate key historical developments that have weakened and threatened democracy in the US.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 252.


“... historian Alfred McCoy points out that US postwar geopolitical primacy rested upon control of both axial ends of the world island–the Eurasian landmass. By controlling Western Europe (especially Germany) and East Asia (especially Japan), Americans created an international capitalist system over which the US reigned hegemonic. Trade and capital have flowed across two oceans with the US as the center of gravity, providing the global reserve currency and an historically unrivaled network of military bases concentrated in and around key strategic locations.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 255; reference: McCoy, A. 2017. In the Shadows of the American Century: The Rise and Decline of US Global Power. Chicago: Haymarket Books.


“Subsequently [after 2019], things have gotten worse [for human civilization and its crises]. Since World War II, the US-led capitalist world order has presided over many technological and scientific advances. Tragically, the hegemonic American state has all too often been the decisive actor in managing the march of human civilization. US elites have run the world in such a way as to preclude the application of Enlightenment principles toward human progress. America’s self-celebrated liberal institutions have failed the US and the rest of humanity. Humanity has had to exist in a world order most decisively shaped and dominated by the US. In part, this book has sought to address the deficiencies of liberal social science in explaining and understanding such problems. Of particular concern is the high crime blindness of social science. As Ola Tunander points out, ‘Liberal political science has been turned into an ideology of the [deep state], because undisputable evidence for [its existence] is brushed away as pure fantasy or conspiracy.’ In other words, the modern social scientist has made manifest The Zhuangzi’s astute Daoist pronouncement: ‘The sage is the sharpest tool of empire; he is not a means of bringing light to the empire.’” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 270-1; subquotes: Tunander, Ola. 2009. “Democratic State vs. Deep State – Approaching the Dual State of the West” Wilson, Eric. (ed.) Government of the Shadows: Parapolitics and Criminal Sovereignty. London: Pluto Press. p. 68; Graham, A.C. 2001. Chuang-Tzu: The Inner Chapters. Indianapolis: Hackett Publishing Co. p. 208.


“The tripartite theory of the state and the concept of exceptionism have been developed herein to offer a means of understanding and explaining important historical and political realities. These matters include unadjudicated elite criminality, the ceaseless US pursuit of global dominance, and the prevailing regime’s inability to address major crises–namely: economic inequality, ecological destruction, and the threat of nuclear omnicide. Empire, America’s pursuit of global dominance, is at the root of these problems. Through its defaults, liberal political science has in effect been shaped into ‘an ideology of the deep state’.... In other words, US political scientists implicitly presuppose the rule of law, transparency, and a Weberian state that holds a monopoly on legitimate violence. Given current political and historical realities, these methods are insufficient. They do not allow scholars to illuminate political orders characterized by elite criminality, widespread secrecy, and a cloaked illiberal state. As stated in the first chapter, the philosophy applied in this book is that the problem should define the methodology, not vice versa.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. p. 272.


“Meanwhile, the major problem for the corporate rich is that they have accumulated so much wealth that it is difficult for them to find sufficient opportunities for investment. This leads capital to seek returns in financial speculation, military/war spending, and privatization of the public domain. All three of these predatory, rent-seeking avenues of moneymaking entail accompanying efforts to dominate politics and society to facilitate these types of economic activities.” Good, Aaron. 2022. American Exception: Empire and the Deep State. NY: Skyhorse Publishing. pp. 279-280.


“Many recent results do not appear to be compatible with the classical distinctions between perceptual, cognitive, and motor systems....

“Because the brain’s functional architecture originally evolved to serve the needs of interactive behavior, and was strongly conserved during phylogeny, we believe an ethological foundation may be more appropriate for understanding neurophysiological data about voluntary sensorimotor behavior compared to frameworks inspired by studies of advanced human abilities.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 270.


“According to classical views, cognition is separate from sensorimotor control. However, a hall mark executive function, decision making, does not appear to be localized within particular higher cognitive centers such as the primate prefrontal cortex. Instead there is growing evidence that decisions, at least those reported through action, are found within the same sensorimotor circuits that are responsible for planning and executing the associated actions.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 273.


“Throughout evolutionary history, organisms and their nervous systems have been preoccupied by almost constant interaction with a complex and ever changing environment, which continuously offers a potentially bewildering variety of opportunities and demands for action. Interaction with such an environment cannot be broken down into a sequence of distinct and self-contained events that each start with a discrete stimulus and end with a specific response, similar to the isolated trials we typically use in many psychological or neurophysiological experiments. Instead, it involves the continuous modification of ongoing actions through feedback control, the continuous evaluation of alternative activities that may become available, and continuous tradeoffs between choosing to persist in a given activity and switching to a different one.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 275.


“Perhaps the best known example [of the circularity of stimulus and response] is the work of the eminent psychologist Jean Piaget, who suggested that the abstract cognitive abilities of adult humans are constructed upon the basis of the sensorimotor interactions experienced as a child. This is supported by a variety of neural studies, which include the classic experiments of Held & Hein, who found that the visual behavior of newborn kittens did not develop properly unless they were allowed to exert their own active control upon their visual input.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 276; references: Piage, J. 1954. The Construction of Reality in the Child. NY: Basic Books; Held, R. & Hein, A. 1963. “Movement-produced stimulation in the development of visually guided behavior. J. Comp. Physiol. Psychol. 56(5):872–76.


“From this perspective [that perception is more interested in specifying the parameters of potential and on-going actions rather than trying to build a representation], processing in the parietal cortex and reciprocally connected premotor regions is not exclusively concerned with descriptive representations of objects in the external world but primarily with pragmatic representations of the opportunities for action that those objects afford. Indeed, parietal activity in both monkeys and humans is often stronger when objects are within reach.

“Several groups have developed these ideas further. For example, Fagg & Arbib have suggested that the PPC represents a set of currently available potential actions, one of which is ultimately selected for overt execution.... It is also similar to the proposal that the brain begins to prepare several actions in parallel while collecting evidence for selecting between them, a view that is strongly supported by neurophysiological studies of decision making.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 277; reference: Fagg, A.H. & Arbib, M.A. 1998. “Modeling parietal-premotor interactions in primate control of grasping.” Neural Netw. 11(7-8):1277-1303.


“This general hypothesis [the affordance competition hypothesis] is directly inspired by the work of Gibson, Ashby, Goodale & Milner, Arbib, and many others mentioned above. It begins with a distinction between two types of problems that animals behaving in the natural environment continuously face: deciding what to do and how to do it. We can call these the problems of action selection and action specification.” Cisek, Paul & John F. Kalaska. 2010. “Neural Mechanisms for Interacting with a World Full of Action Choices.” Annual Review of Neuroscience. 33:269-298. 10.1146/annurev.neuro.051508.135409. p. 277; references (partial): Ashby, W.R. 1965. Design for a Brain: The Origin of Adaptive Behavior. London: Chapman and Hall; Goodale, M.A., Milner, A.D. 1992. “Separate visual pathways for perception and action.” TINS 15(1):20–25.


“The term ‘organization’ generally refers to the structure of relations between the parts of a given system or of a subsystem of a larger system, be they components, their activities, or processes....

“Systems that do not involve differential causal roles for their components are not organized.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 5, 6.


“A constraint C can be defined as a material structure that harnesses a process P by reducing its degrees of freedom so that:

“(1) at a time-scale characteristic of P, C is locally unaffected by P;
“(2) at this time-scale C exerts a causal role on P, that is, there is some observable difference between free P, and P under the influence of C....”

“The notion of constraint so formulated allows us to distinguish between two orders of ‘causes’ in natural systems: processes and those constraints that make those processes possible.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 17, 18.


“In a living system, understood in terms of a causal regime of closure of constraints, a multiplicity of constraints contributes in different ways to the maintenance of their organization.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 19.


“However, this circularity at the level of constraints [closure of constraints] should not be confused with any cycle of activities at the level of processes. Cycles are captured by a different circularity that is also known as closure of processes or operational closure, which stands for the recursion between the operations of the components of a system: a closed network of operations in which all the actions of the components have an effect inside the system. To realize closure of constraints, and therefore a self-maintaining organization, what is important is not only that the results of the activities of parts remain within the system, but that for any component its production process can be traced within the system. The circularity realized by closure of constraints encompasses not only the activities of the components but their conditions of existence, provided by their participation in the organized system they continuously realize. While the notion of cycle, or operational closure, says nothing about the origin of the components, organizational closure points to their internal generation as well as to the properties they need to satisfy in order to contribute to self-production, that is, to be able to participate in processes of production – transformation and degradation – of other components. This is a feature that is not shared by other circular networks such as abiotic water cycles or self-maintaining systems such as dissipative structures like hurricanes and whirlwinds.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 21.


“The idea of regulation is considered [within organizational theory up to time of writing] as an additional, not definitory, feature of biological organizations.... I will argue that the role of regulatory control in biological systems is instead deeper and concerns every activity carried out by biological systems, not only those related to response to perturbations. We cannot think of closure without also considering regulation.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 23.


“This [the up-to-then too narrow basic notion of closure of constraints] is due to three types of problems, which concern, specifically, its biological grounding, the capability to account for the integration between components, and for change (adaptive, physiological, developmental, etc.).

“With regards to biological grounding, the notion of closure of constraints selects from the set of relations realized in biological systems the generative ones involved in the production of components.... In actual biological systems, the basic constraints involved in a regime of closure are not always functioning, or functioning whenever their substrates and energy are available. Their activities are constantly controlled: inhibited, activated, and modulated....

“Let us consider the second problem: integration. Closure emphasizes the mutual dependence between components for their production but does not account for how their activities are also mutually dependent so that components are integrated into a system that maintains itself as a cohesive whole....

“... change has been often screened off from their [those of organization theorists] accounts as extrinsic to a biological organization and not strictly required for it to function.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 23, 24, 25.


“... advocates of the organizational framework have introduced into their account and developed the notion of regulatory control, characterized as an activity carried out by a special type of constraints: control constraints.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 26.


“Control constraints are a special type of constraint that are dynamic and do not operate on production or repair processes but on the activities of other constraints: They are second-order constraints.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 27.


“For closure to be realized and to be viable, the activity of each constraint C depends on the operations of at least one regulatory constraint R, which in turn depends on C for its existence.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 29.


“This causal regime [where each constraint contributes to the maintenance of (some of) the conditions under which the whole network can exist; a self-determining organization where the existence of the constitutive constraints are mutually determined within and by the organization itself] can ground teleology because it establishes a circular relationship between the existence and activity of a living system. According to this view, a living system is what it does – it is a cause and effect of itself.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 34.


“The core idea of the organizational account of functions is that in a regime of self-maintenance that realizes closure of constraints and, therefore, is inherently teleological, functional attributions are justified in terms of the contributions of traits to the maintenance of the system that harbors and produces them.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 38.


“To address this concern [organization issues in biology that concern cross generation processes such as reproduction], advocates of the organizational framework have provided a possible yet controversial way to functionally ground cross-generation functions such as reproduction. In their view, reproductive traits are functional because they are produced by the biological organization of the parents at some point in their life cycle, and they contribute to reestablishing that very organization in the off-spring. The main idea is that if a given system possesses an organization realizing closure because of its causal and material connection with a previous system possessing the same organization, then both systems can be considered as temporal instances of the same encompassing organization.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 40.


“In this view [an organizational view of information or functional influence], to say that a signal is functional means that it contributes to the maintenance of the current organization of the sender, without necessarily appealing to its evolutionary history. Given two systems, A and B, realizing regulated closure of constraints, according to an organizational-influence account communication implies that (1) a receiver B responds to a signal emitted by the sender A, and (2) that a signal is a sender’s trait that by triggering some response in a receiver B, contributes to maintain the organization of A that, in turn, is responsible to produce and maintain the signal trait itself.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. p. 47; credits concept to: Frick, R., L. Bich & A. Moreno. 2019. “An organsational approach to biological communication.” Acta Biotheoretica. 67(2):103-128.


“In principle, closure is not incompatible with forms of dependence [e.g. symbiosis], and a system can be self-maintaining in the sense that it realizes closure even though it is not independent from other systems or its environment.... In this view, subsystems contribute to one another’s conditions of existence by mutually controlling their functional processes in such a way as to achieve closure. This very general idea allows one not only to understand living systems such as organisms as cohesive entities (i.e. individuals), but also to account for those interactions between different biological systems that are necessary for the maintenance of the systems involved, without the need to put into question core notions such as closure.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 56, 57.


“However, the organizational framework can deploy two possible strategies to consider and better characterize the environment: an adaptivity strategy and an ecosystems strategy....

“Adaptivity is defined by Di Paolo as the capability of a system, such as an organism, to remain viable in its environment by regulating itself....

“This account [an organizational account of ecological functions by Nunes-Neto et al., 2014) proposes the thesis that ecological interactions between organisms can realize a form of collective closure between organisms, whose self-maintaining regime goes beyond the individual organisms. This interspecies collective regime of organizational closure is realized by means of mutual constraints exerted by groups of organisms on one another’s external boundary conditions. It is different from the regime of closure realized within living organisms because in principle it does not involve regulatory control modulating the operation of its parts. It involves only basic constraints exerted by different ecological communities on the environmental flux of matter and energy crossing the larger system.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 58, 59-60; reference: Nunes-Neto, N., A. Moreno & C.N. El Hani. 2014. “Function in ecology: An organizational approach.” Biology & Philosophoy. 29(1):123-141.


“In this view [an organizational view of ecological interactions], to be included in the system [ecological organization], an entity should be a constraint that is both dependent on other constraints in the system and enabling the activity of other constraints in it. Such constraints can be directly exerted by living organisms but also by abiotic entities. In this view an abiotic item such as fire interacting with vegetation can be a functional component of an ecosystem’s organization if it is subject to closure within that system, that is, if it is both a dependent (i.e. subject to constraints internal to the system) and enabling constraint (i.e. affecting its dynamic and contributing to the maintenance of the system. An item (biotic or abiotic) is external to the system, instead, if it is only a boundary condition not directly dependent on the system.” Bich, Leonardo. 2024. Biological Organization. Cambridge UP. pp. 60-1.


“While development is usually identified with the achievement of an adult form with the capability to reproduce and therefore maintain a lineage, adopting the organizational approach may provide a different strategy, which focuses also on the maintenance of the current organization of the organism. By doing so an organizational approach favors a switch in perspective which consists in analyzing how organisms maintain their viability at each moment of development rather than considering them as going through intermediate stages of a process directed toward a specific goal state.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. P. 241.


“Through metamorphosis an organism might lose the capacity to feed but gain the capacity to sexually reproduce, such as in mayflies, or behavioral complexity found in the larvae might be lost while structural complexity increases in the sessile adult form, such as in tunicates. Tunicates lose complex and energetically expensive structures like a head/brain that become unnecessary once they transform into sessile adults.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. p. 246.


“In a more extreme case, the immortal jellyfish Turritopsis dohrnii is able to reverse its developmental trajectory from medusa back to polyp in response to stress without going through the whole cycle (i.e., through reproduction and the unicellular stage). It does so by going through a different intermediate stage, the cyst, constituted by a cluster of poorly differentiated cells. Is rejuvenation, with or without simplification, a kind of development? If it were the case, it would put into question the very idea of development as a unidirectional or irreversible process.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. pp. 246-7.


“Complex life cycles are probably the most common type across the spectrum of life. This includes sequences of forms that are divided by metamorphosis (like between the caterpillar and the butterfly), by both asexual and sexual reproduction (e.g., corals, and parasitic flatworms, etc.) and transitions between multicellular and unicellular forms (e.g., algae, ferns). Let us think of a life cycle with multiple stages divided by reproduction, where the same type of form doesn’t come back until it has gone through different stages separated by reproduction. It is hard to parse a life cycle like this on an account of development that focuses on development as a unitary process that moves solely toward reproduction. Moreover, it makes it extremely problematic to distinguish development from reproduction.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. p. 248.


“Lichens don’t reproduce to form new lichens; the algal and fungal partners reproduce separately and then disperse and rejoin to form new lichens. Yet they undergo developmental changes at the level of the system as a whole. These systems are contradictory for developmental accounts focused on reproduction.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. p. 249.


“According to the organizational approach, there are no intermediate stages, ones defined by their relation to some future goal state. Every stage is equally important, because the system must build and maintain itself at every point of its existence.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. pp. 249-250.


“The starting point [for organizational theory to address problems about the boundary and definition for development] is the idea that during the life of an organism, what is maintained through the deep and continuous changes of its components is the organization of the whole. The conservation of organization unifies the biological processes an organism undergoes, which includes development, growth, senescence, etc. This idea has been expressed by Di Frisco and Mossio through the notion of organizational continuity, that is, ‘the presence of a continuous causal process linking successive organizational regimes, irrespective of material and functional changes.’ This is the foundational assumption that it is to be adopted in order to understand biological phenomena from an organizational perspective. However, it is a very general notion.” Bich, Leonardo & Derek Skillings. 2024. “There Are No Intermediate Stages: An Organizational View on Development.” From: Organization in Biology. Mossio, Matteo (ed). pp. 241-262. Springer. p. 255; reference: Di Frisco, J. & M. Mossio. 2020. “Diachronic identity in complex life cycles: An organisational perspective. In: Meincke, A.S. & J. Dupre (Eds). Biological identity: Perspectives from metaphysics and the philosophy of biology. pp. 177-199. Routledge.


“On this definition [of influence in communication], signals are traits adapted for causally influencing a receiver. This influence-based definition was able to differentiate signals from cues....

“We define [an influenced based account] a ‘signal’ as any act or structure which alters the behaviour of other organisms, which evolved because of that effect, and which is effective because the receiver’s response has also evolved’” Kalkman, David. 2017. “New problems for defining animal communication in informational terms.” Synthese. 196:3319-3336. 10.1007/s11229-017-1598-2. pp. 3320, 3321.


“... a co-adapted influence definition [of communication] is itself too liberal. It includes within its extension various co-adapted interactions between two organisms that are not genuine cases of communication: namely certain kinds of coercive and reciprocal interactions. Scarantino argues that only by adding an informational component to a co-adapted influence definition can the definition be salvaged.” Kalkman, David. 2017. “New problems for defining animal communication in informational terms.” Synthese. 196:3319-3336. 10.1007/s11229-017-1598-2. p. 3322; reference: Scarantino, A. 2013. “Animal communication as influence-mediated influence.” In: Stegmann, U. (ed.) Animal communication theory: Information and influence. pp. 63-87. Cambridge UP.


“Now, arbitrariness is a property of interactions held by some to be prototypically ‘communicative’. According to one prima facie plausible way of thinking about signals as distinct from other kinds of behaviours or phenotypes, the former involve ‘a distinctive role for relations of involvement between [signals] and other things.’ The idea is that a paradigm signal, as opposed to a non-signal, brings about its effects conventionally as opposed to via its intrinsic properties....

“While adding an arbitrariness criterion to an information-mediated influence definition might seem like a promising way of cordoning off communication from other co-adapted interactions, there is a problem. The problem is that many cases of communication aren’t all that arbitrary. Worse, certain paradigm communication systems, such as the famous waggle dance of the honeybee, don’t seem to be all that arbitrary.” Kalkman, David. 2017. “New problems for defining animal communication in informational terms.” Synthese. 196:3319-3336. 10.1007/s11229-017-1598-2. pp. 3330, 3331; subquote: Godfrey-Smith, P. 2014. “Signs and symbolic behaviour.” Biological Theory. 9(1):78-88.


“We go from a definition of communication that is too liberal to one that is too restrictive....

“It reflects the ‘classical’ approach to categorisation: come up with a list of necessary and sufficient conditions that include all and only instances of communication.

“However, it is no secret that variation characterises many biological phenomena.” Kalkman, David. 2017. “New problems for defining animal communication in informational terms.” Synthese. 196:3319-3336. 10.1007/s11229-017-1598-2. p. 3333.


“Now consider the hydrologic cycle in prebiotic Earth. In simple terms, it amounts to a set of processes that generates, under certain boundary conditions, a cycle of causal relations in which each of these processes contributes to the maintenance of the whole, and is, in turn, maintained by the whole: the sun evaporates water from the Earth surface, forming clouds; when rising to higher layers of the atmosphere these clouds get colder and generate rain; and the rain, in turn, contributes to generate water on the Earth surface once again, which evaporates and re-generates clouds, and so on. This is a geochemical example of a closure of processes.” El-Hani, Charbel Nino & Nei Nunes-Neto. 2020. “Life on Earth Is Not a Passenger, but a Driver: Explaining the Transition from a Physicochemical to a Life-Constrained World from an Organizational Perspective.” History, Philosophy and Theory of the Life Sciences. 10.1007/978-3-030-39589-6_5 pp. 71-2.


“They [Nunes-Neto et al] defined an ecological function as a ‘precise (differentiated) effect of a given constraining action on the flow of matter and energy (...) performed by a given item of biodiversity, in an ecosystem closure of constraints.’” El-Hani, Charbel Nino & Nei Nunes-Neto. 2020. “Life on Earth Is Not a Passenger, but a Driver: Explaining the Transition from a Physicochemical to a Life-Constrained World from an Organizational Perspective.” History, Philosophy and Theory of the Life Sciences. 10.1007/978-3-030-39589-6_5 p. 74; reference: Nunes-Neto, N., A. Moreno & C.N. El-Hani. 2014. “Function in ecology: An organizational approach.” Biology and Philosophy. 29:123-141. p. 131.


“... the CLAW hypothesis [named for authors that marine phytoplanktonic organisms release a sulphur compound that has an impact on global climate, dimethylsulphide (DMS)] proposes that the highest rate of DMS emission to the atmosphere takes place in the warmest, most saline and most intensely illuminated regions of the oceans, and that the DMS released in the ocean is rapidly ventilated to the atmosphere, where it undergoes a series of oxidations, originating cloud condensation nuclei (CCN) for water vapor. CCNs are acidic particles exhibiting properties that make it possible for water vapor molecules to condensate and, thus, to contribute to the formation of clouds over the oceans. Since clouds reflect solar radiation back to space, they tend to cool the planetary surface. As the concentration of clouds over the oceans increases, less solar radiation reaches the surface waters, and this tends – according to the hypothesis – to reduce the heat, salinity and luminosity of the oceanic surface. As a consequence, less DMS is released by the marine phytoplankton and this, in turn, reduces the production of clouds.” El-Hani, Charbel Nino & Nei Nunes-Neto. 2020. “Life on Earth Is Not a Passenger, but a Driver: Explaining the Transition from a Physicochemical to a Life-Constrained World from an Organizational Perspective.” History, Philosophy and Theory of the Life Sciences. 10.1007/978-3-030-39589-6_5 pp. 75-6.


“As microorganisms (including photosynthetic marine microorganisms) appeared and began to use resources in an oxidative atmosphere (such as water, oxygen, nutrients, etc.), they started to establish metabolic interchanges with the consequence that the ecological networks, with mutual dependence between their components, became increasingly relevant to the atmosphere and climate dynamics....

“According to an organizational view – as one possible theoretical perspective to conceptualize the transition we are interested [in] here – a key change happens in the cycling of sulphur atoms and molecules when biological or ecological structures constraining their flow appear. Namely, what initially was merely a closure of processes became a closure of constraints, as these were re-generated by the system itself....

“The functional effect of the marine microbiota is to produce DMS (from DMSP) in the ocean water, which is then ventilated to the atmosphere and suffers from a series of oxidations until the remaining sulphur originates the cloud condensation nuclei, which, in turn, become part of the clouds. The clouds are constraining entities on the flow of sulphur, since they keep the sulphur atoms and molecules as part of their physical structures (rather than as free-floating substances in the atmosphere, with higher degrees of freedom), while they move in the atmosphere. A fraction of these clouds formed over the oceans will move to land and, when conditions for precipitation are fulfilled, they precipitate the sulphur along with the water. Thus, the sulphur atoms and molecules fall on land, reaching soils, lakes and rivers, and are eventually carried back to the oceans, through the rivers. Both the rivers and the rocks along them play a constraining role in this flow, mainly through the mechanical action of river waters on soils and rocks, causing their lixiviation and erosion, which increase the concentration of sulphur in the water. This is just like a channeling, which reduces the degree of freedom of sulphur on land. In the ocean, sulphur will be available to the metabolism of marine organisms, being part of the DMSP – the precursor of DMS – in algae cells, closing, then, the cycle of sulphur.” El-Hani, Charbel Nino & Nei Nunes-Neto. 2020. “Life on Earth Is Not a Passenger, but a Driver: Explaining the Transition from a Physicochemical to a Life-Constrained World from an Organizational Perspective.” History, Philosophy and Theory of the Life Sciences. 10.1007/978-3-030-39589-6_5 p. 77.


“We hold here that four different and independent approaches propose the same general idea, namely, that life influences physicochemical conditions in a way that ultimately contributes to its self-maintenance, although with their respective specificities, with different emphasis or domains of application....

“First, the organizational approach to biological/ecological systems (OABS/OAES), which we have here mobilized as our main epistemological framework, proposes that organisms and ecological systems are organizationally closed systems, i.e., show a closure that is reached by the establishment of mutual dependence between constraints. Thus, organisms and ecological systems do not show a closure that amounts only to some cyclic pattern in a physicochemical flow, i.e., they do not show merely a closure of processes. Constraints in a living system are mutually dependent: the functional effect of each constraint in the system is the cause of at least one other constraint, and, thus, it is also a cause for itself to persist, since contributing to the persistence of another constraint creates, at least in part, the conditions of possibility for its own maintenance....

“Second, Earth System Science (ESS), which can be regarded, as Margulis argued, as Gaia theory by another name, proposes that the Earth biota is strongly integrated with the physicochemical (abiotic) environment in such a way that the biota tends to produce adequate conditions for itself.... Earth System Science, in particular, supports the thesis of a life-constrained world from the point of view of climatology and biogeochemistry, which offer a more global geological and ecological perspective on the interaction between living beings and their physicochemical environment.

“Third, the Biodiversity and Ecosystem Functioning research program (hereafter, BEF), which is mainstream in current ecological science, assumes that the functions of the organisms, populations and communities have an effect on the ecosystem properties, many of which are abiotic properties (like temperature, humidity, etc.)....

“[Fourth] According to the NCT [Niche Construction Theory], organisms actively transform their physicochemical environments and these ecological transformations have a significance for themselves and for their offspring, and also for different species, showing evolutionary significance, as we can see, for instance, in the case of a beaver dam.” El-Hani, Charbel Nino & Nei Nunes-Neto. 2020. “Life on Earth Is Not a Passenger, but a Driver: Explaining the Transition from a Physicochemical to a Life-Constrained World from an Organizational Perspective.” History, Philosophy and Theory of the Life Sciences. 10.1007/978-3-030-39589-6_5 pp. 79, 80.


“Interestingly, when the environment is not overlooked, reduced to a standardised set of boundary conditions, or loosely characterised in terms of context, it is often characterized as pathogenic.” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 4.


“Biological adaptivity is different from biological adaptation, although the uses and meanings of these terms are often overlapping. Adaptation is a central concept in evolutionary biology, which refers to the fit between an organism and its environment caused by natural selection.... To avoid confusion, hereinafter we will use the term adaptivity as the organism’s capability to cope with a changing environment, unless specified otherwise.” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 11.


“... Bich et al. distinguish between ‘stability’ and ‘adaptive regulation.’ Stability is characterised as a passive network property: the system simply ‘absorbs’, as a network, the effects of perturbations or internal variations. It does so by compensating for them through internal reciprocal adjustments between tightly coupled subsystems. As a result, the whole dynamic is maintained in the initial attractor, or it is pushed by the perturbation into a new stable attractor. Adaptive regulation is characterized, instead, as the active modulation of the internal dynamics and behaviour of a system in relation to variations in internal and external conditions. Such modulation is carried out by means of specialized mechanisms that evaluate perturbations and operate accordingly.... In the case of network stability, the organism responds passively to the environment. Regulatory adaptivity, or adaptive regulation enables, instead, the organism to actively engage with the environment through change.” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 12; reference: Bich, L., M. Mossio, K. Ruiz-Mirazo & A. Moreno. 2016. “Biological regulation: Controlling the system from within.” Biology and Philosophy. 31(2):237-265. 10.1007/s10539-015-9497-8.


“Adaptivity allows to make a principled distinction between two ways of conceptualising a relationship between the environment and the organism, and its relation to health. The first is based on stability and the idea of returning to the initial state of the system (or one of its variables) after a perturbation (such as in the generic notion of homeostasis). The second consists in actively bringing forth adaptive changes in the system (such as in the adaptive regulation).” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 13.


“... a simple dictionary definition of environment as: ‘the complex of physical, chemical and biotic factors that act on an organism or ecological community and ultimately determine its form and survival.’” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 13; subquote: Encyclopaedia Britannica. 2020.


“On the adaptivity account, the environment is characterized relationally. It does not constitute a set of independent boundary conditions affecting a system. Moreover, the interaction with the environment is not characterized in negative terms. Adaptivity entails a different approach, focused on engaging with and taking advantage of variability and change, instead of preventing it. Regulatory mechanisms do not only respond conservatively to perturbations that menace the survival of the system or destabilise some variables in the system. A system endowed with adaptive regulatory mechanisms can make decisions on the basis of what it senses in the environment. From this perspective, the interaction with the environment is constitutive of a biological system, which needs to manage positive and negative interactions in such a way as to maintain itself viable.” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 13.


“The case of microbiologically healthier buildings is another example of an adaptive mechanism involving the environment, which could constitute a step forward in coping with COVID-19 and future epidemics and pandemic events. Research on bioinformed design is an important extension of theoretical research on the microbiome, a relevant topic in science and in philosophy of science in the last decades. This approach starts from acknowledging that humans, as well as the spaces they inhabit, are colonized by microorganisms: every one of us ‘aerosolises around 37 million bacteria per hour.’ Living human spaces are inhabited by bacteria and viruses, coming from human bodies, from those of visitors, friends, pets, from outside air, etc. This applies to homes, public buildings, schools, universities, and hospitals.” Menatti, Laura, Leonardo Bich & Cristian Saborido. 2022. “Health and environment from adaptation to adaptivitity: a situated relational account.” HPLS. 44:38. 10.1007/s40656-022-00515-w p. 19.


“According to the new theory (called ‘predictive processing’), reality as we experience it is built from our own predictions.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. xii.


“Contrary to the standard belief that our senses are a kind of passive window onto the world, what is emerging is a picture of an ever-active brain that is always striving to predict what the world might currently have to offer. Those predictions then structure and shape the whole of human experience, from the way we interpret a person’s facial expression, to our feelings of pain, to our plans for an outing to the cinema.

“Nothing we do or experience–if the theory is on track–is untouched by our own expectations.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. xiii.


“That work [that led to the predictive brain hypothesis endorsed by the author] goes by various names including ‘predictive processing,’ ‘hierarchical predictive coding,’ and ‘active inference.’” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 10.


“Making perception turn on prediction has another important benefit too. It enables the brain to process incoming sensory information in a way that is quite remarkably efficient.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 13.


“The great thing about learning to predict by trying to predict is that the world itself is constantly correcting your failures. If I wrongly predict the next word you are about to utter, the next thing that hits my ears is a sound stream corresponding to the correct word. My brain can use that information to try to improve its predictions next time around.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 28.


“We have seen that human experience arises at the meeting point of predictions and sensory evidence. But exactly how those two potent forces meet and balance is flexibly determined by a further factor: the brain’s best estimate of their relative reliability and significance.... This means we need to think not just about our brain’s predictions and the incoming sensory evidence, but also about the way these estimates of precision flexibly alter the balances of power between them.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 38.


“Such studies [of the expectations of pain creating real pain] suggest a complex dynamic in which false expectations, once they get a grip on us, become increasingly resistant to change. This phenomenon of spuriously self-confirming expectations is probably more common than we realize, as when a patient, expecting dentistry to hurt, experiences greater pain than they otherwise would–which then in turn appears to confirm, and thereby cements, their own prior belief.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 43.


“Arising at the crossroads of neuroscience and computer models of the mind, computational psychiatry aims to develop a more insightful and systematic alternative to the standard symptom-based approach. It seeks to understand psychiatric conditions (and psychological diversity more generally) as a reflection of differing balances in the ways our brains process information.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 44.


“Precision variations act rather like a volume control, altering the downstream (post-synaptic) influence of whole populations of neurons. But there is not just one volume control in play but many. There are many such controls because precision is thought to be estimated at all times and for all neuronal populations. Varying estimates of precision alter patterns of post-synaptic influence and so determine what (right here, right now) to rely on and what to ignore. This is also the way brains balance the influence of sensory evidence against predictions.... Expressed like that, the intimacy of precision and attention is apparent. Precision variation is what attention (a useful but somewhat nebulous concept) really is.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 50.


“Since all human experience is constructed from mixtures of expectation, attention, and sensory stimulation, it will never be possible to experience either the world or your own body ‘as it really is.’ Indeed, it rapidly becomes unclear what this could even mean.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 55.


“In the McGurk effect, subjects are shown a video clip where the sound ‘ba-ba’ is played, but the person’s lips are actually moving in the ways they would if they were saying ‘ga-ga.’ Faced with this apparent contradiction, neurotypical subjects tend to merge the two sources of information, and clearly hear ‘da-da.’ The da-da’ perception is a kind of illusion....

“The McGurk effect is diminished–and sometimes entirely absent–in those with autism spectrum condition. This makes sense if these individuals take the incoming sounds at something closer to face value (‘ba-ba’), rather than warping their experience to conform with the guess that best accommodates the accompanying visual information....

“The price of more accurate perception in one context may be a tendency to make costly mistakes in others. No one way of balancing sensory evidence and prior knowledge is going to be perfect for all purposes.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 61, 62.


“Predictive processing may also shed some light on a frequently misunderstood condition–schizophrenia. The psychologist Peter Chadwick describes his own experience of the onset of schizophrenia as involving what he called a ‘step-ladder to the impossible’... As he puts it, ‘I had to make sense, any sense, out of all these uncanny coincidences. I did it by radically changing my conception of reality.’ For example, he started to hear things being said on the radio as if they were spoken directly to him, picking up on what he was already thinking in some inexplicable kind of way.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 62-3; reference/subquote: Chadwick, P.K. 1993. “The Stepladder to the Impossible: A Firsthand Phenomenological Account of a Schizoaffective Psychotic Crisis.” Journal of Mental Health. 2:239-250. p. 239.


“Importantly, predictive brains control action as well as perception, and so the delusional person will actively seek out confirming evidence for their radical hypotheses. As this process unfolds, new information may itself be interpreted differently so as to appear to confirm or consolidate the radical beliefs. The cycle of error thus becomes (yet again) viciously self-protecting. Such pernicious outcomes seem to be the Achilles’ heel of the predictive brain.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 64.


“By making prediction the common root of both perception and action, predictive processing (active inference) reveals a hidden unity in the workings of the mind. Action and perception form a single whole, jointly orchestrated by the drive to eliminate errors in prediction.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 71.


“The core idea [from mid-nineteenth century idea from Hermann Lotze and William James that Clark is supporting here] was that actions come about because we mentally represent the completed effects of the action. In other words, the idea of the completed action is what brings the actual action about. This is sometimes said to reverse a commonsense notion of causality, since instead of the action causing the effect, it is the representation of the effect (the completed action) that causes the action itself to unfold.... This became known as the ‘ideomotor theory of action,’ since the idea (or mental image) of the completed motor action is what brings the actual movements about.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 71.


“They [an outfielder in baseball trying to catch a fly ball] run with their eye on the ball, so that their own movement cancels out any apparent changes in the acceleration of that ball as it flies. By running so as to keep the perceived acceleration of the ball in the sky constant, the outfielder reaches the landing spot at the right time to make the catch.

“This strategy provably affords a fast, cheap-to-compute way of running to intercept the ball. It is a prime example of embodied problem solving because it makes the outfielder’s own movements part of the actual problem-solving process. It is also another example of controlling an action by means of its predicted sensory consequences–the task is solved as long as the outfielder acts to keep their own sensory stimulations within certain bounds. This can be achieved by predicting that the sensory flow will stay within those bounds and minimizing error by moving the body. This is a very robust strategy which automatically compensates for unexpected deviations as might be caused by a sudden gust of wind, since that will immediately cause new and larger prediction errors that will recruit whatever bodily motions are needed to try to counteract it.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 81-2.


“The hidden task of all that training [for a sports personality such as Muhammad Ali], we can now appreciate, is to enable our brains to predict (via a cascade that often starts with a very high-level goal or aim) the many subtle sensory consequences of an unfolding successful action [as a way to prime the expectations for what success would feel like].” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 84.


“Importantly, creatures sensitive to their own error dynamics will automatically seek out good learning environments, preferring ones that are neither too predictable nor too unpredictable.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 93.


“It is this melting pot of influences [cultural influences, evidence and expectations about my current situation and my own current bodily states, idiosyncratic tendencies that might underlie into what emotional state we wander] that the predictive engine inside our heads is seeking to master, when it delivers an experience that I might label as ‘feeling sad’ or ‘feeling anxious.’

“Brains master the melting pot by commanding and combining predictive knowledge concerning the inner states of our own bodies, our current and upcoming actions, and the wider world. this takes us way beyond the old idea of simple physiological signatures for different emotions and into the exciting research arena dubbed ‘interoceptive predictive processing.’ The central idea is that a single kind of process combines inner and outer sources of information, generating a context-reflecting amalgam that is experienced as emotion. For example, a fast-beating heart will have a very different emotional impact on a person who ascribes the cause as recent exercise versus one who fears they are having a sudden heart attack.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 98.


“One important and consistent finding in this area [defending ourselves against positive information] is that chronic depression involves a resistance to updating our negative expectations when confronted with what ought to be good evidence of positive outcomes. This failure to update in the face of good evidence most likely involves abnormally high precision on prior negative beliefs, which in turn robs unanticipated positive information of the power to alter the inner model that is delivering negative anticipations. The highly weighted (hidden) belief that outcomes will be negative acts as what has usefully been described as a kind of ‘cognitive immunization’ to the effects of countervailing positive information, causing us to either avoid gathering, ignore, or otherwise downgrade perfectly good positive evidence–such as genuine evidence that we are liked and valued.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 107-8.


“Predictive brains look inward as well as outward, and it is those inward-looking dimensions that allow human experience to be constantly infused by feeling and emotion. This is because our take on the outside world is in constant two-way communication with information and predictions about our own changing internal physiological states. When this all works in harmony, it keeps us from straying too far from our window of bodily viability, and proactively budgets for our basic bodily needs. But when this system malfunctions and misregulates, it can lead to depression, anxiety, and retreat from the world.

“Bodily prediction helps sculpt an experiential world in which some states and events are simply more attractive (hence more likely to be occupied) than others....

“It is the ability to crunch together inner- and outward-looking sensory information that makes predictive brains such a valuable and life-preserving adaptive asset.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 113, 114.


“Emotion–or so we argued–reflects the changing value of different actions given our bodily state, goals, needs, and projects. It is a kind of marker of our embodied attunement (or lack of it) to the world.

“Moreover, as we also saw, much of the experienced valence of events and states of affairs (the way they present themselves to us as attractive or repellent, as ones to approach or to avoid) seem to reflect ongoing sensitivities to our own error dynamics.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 118.


“The term sentience was used ... to mark a distinction between the capacity to feel and the capacity to think and reason....

“We can now think of sentient beings as those whose neural model of the world is in constant two-way communication with a model of their own changing physiological state.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 119.


“Bodily self-regulation, action, and temporal depth are, predictive processing thus suggests, jointly necessary if there is to be conscious experience at all....

“We detect sentience in creatures (and potentially in robots) whose take on the external world is subtly but pervasively responsive to their brain or control system’s take on their own inner, bodily worlds and their own states of action readiness.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 119.


“For whatever prediction helps construct experience there is a kind of bias. The world as we see and sense it becomes shaped, in part, by our own (conscious and unconscious) expectations.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 130.


“Importantly, Garfinkel found that anxiety was most strongly associated with the combination of low accuracy regarding your own internal state and an inflated sense of that accuracy. This means that you are more likely to suffer anxiety if you are interoceptively inaccurate and yet falsely believe yourself to be very accurate.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 137; reference: Sarah Garfinkel at University of Sussex.


“Which of these ‘ways of hearing’ is closer to the truth [in trying to interpret ‘sine-wave speech,’ acoustically altered speaking]? It depends on what you are trying to do. Are you a sound engineer trying to detect something acoustically odd about a room? If so, then you will give certain aspects of the acoustic evidence extra weight as you try to track down the problem, attending to different possibilities in turn. Or are you at a busy party trying to hear what’s being said against a noisy backdrop? Every scenario requires a different set of discriminations. In predictive processing terms, that means deploying a different set of active predictions and associated precision-weightings. All this suggests that we can never simply experience ‘the way things really are,’ or the ‘true signal from the world.’ Indeed if predictive processing is a good account of perception it is not even clear what they could mean. To perceive is to bring (weighted) predictions to bear on the incoming sensory signals, and experience arises as these twin elements meet.

“That does not mean we can never get things wrong. But it does mean that there is no single way of getting things right.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 141-2.


“The most basic way that we actively construct our world is by selective sampling. We move our body and aim our gaze in ways that reflect what we expect to encounter. In this way, different kinds of animals, and humans with different individual histories, will harvest different sets of stimulations from the very same world. But as we selectively harvest those stimulations, our brains impose structure a second time, processing the sensory information in ways that amplify and dampen, extracting meaningful structure that itself reflects our own prior experience. The ‘predictive keyboard’ is thus not just an active selector, but also an active processor of whatever gets selected.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 143.


“Epistemic (knowledge-improving) actions are chosen not because they are of intrinsic value to us, nor even because they move us closer, physically speaking, to some practical goal. Instead, they may even move us temporarily further away. For example, if I’m driving, I might navigate back to a familiar spot that I know is in entirely the wrong direction, if I happen to know a reliable route from that spot to my destination. This is sometimes called the ‘coastal navigation algorithm,’ since a sailor may navigate to the coast in order to better find their way, even if following the coast is a much longer route. This renders the distinction between epistemic and practical components especially sharp.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 156.


“... practical actions and epistemic actions are determined in exactly the same way, as the predictive brain makes counterfactual predictions about what kinds of futures will result if certain actions are launched.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 158.


“Once a system can compute expected future error, it will automatically seek out the interwoven set of practical and epistemic actions most likely to bring the desired future state about.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 159.


“Consider coalitions of neurons that are already located outside the brain. An increasingly familiar example can be found inside the human gut, where upward of 500 million neurons in the gut wall already relay important information to the spinal cord and the brain. This circuitry helps regulate serotonin and other neuromodulators. The so-called gut-brain is by a long margin the largest cluster of neurons outside the brain, and an essential part of the nervous system....

“For example, gut bacteria manufacture up to 95 percent of the body’s serotonin, which has large impacts on mood and is one of the neurotransmitters implicated in the precision-weighting process.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. pp. 164-5


“Honest placebos [where the person is advised that there is no positive medical value in taking some medicine but where there is nonetheless a positive effect] appear to work by activating subterranean expectations through superficial indicators of reliability and efficacy such as good packaging and professional presentation (foil and blister packs, familiar font, size and uniformity of the pills, and so on). this is because–as we have seen–the bulk of the brain’s prediction empire is nonconscious. That leaves it free to respond to quite superficial indicators such as familiar packaging and delivery by those authoritative people in white coats. Such ceremonial features cause the prediction machinery to start to anticipate symptomatic relief despite our conscious belief that no clinically active substance is being administered.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 185.


“Predictive brains are built of four core elements. The first is a ‘generative model.’ The second are the moment-by-moment predictions that it issues. The third are the ‘prediction errors’ about which we have heard so much–these arise whenever incorrect or incomplete predictions attempt to meet and account for sensory evidence. The fourth are the estimations of ‘precision’ that alter the relative impact of both sensory stimulations and predictions.” Clark, Andy. 2023. The Experience Machine: How Our Minds Predict and Shape Reality. NY: Vintage Books. p. 217.


“As an investigative framework, complexity science integrates features from a number of tried and tested disciplines. The first main contributing field is systems theory, which is the study of abstract organizational principles. A core lesson from systems theory stems from work in cybernetics on the relationship of parts and wholes, especially in the form of feedback and feedforward processes that underlie control. With regard to complex systems, systems theory provides ways to understand how component interactions are crucial for giving rise to system-level activity, that is, as opposed to focusing on the properties of the components alone. Second, is nonlinear dynamical systems theory (NDST), which is a set of mathematical tools for describing and understanding variables that change over time. NDST differs from dynamical systems theory alone in that the former incorporates a broader set of concepts and methods for investigating more exotic phenomena, such as dramatic shifts in behavior and organization via catastrophe flags like hysteresis that are common to systems with many interacting components that give rise to ordered behaviors. Third, is synergetics, which applies another suite of concepts and methods to study systems that exhibit spontaneous processes and structures. Synergetics aims to reveal general principles that underlie such systems and does so through the investigation of contextually defined macroscopic and microscopic spatial and temporal features.” Favela, Luis H. & Mary Jean Amon. 2023. “Reframing Cognitive Science as a Complexity Science.” Cognitive Science. 47:e13280. 10.1111/cogs.13280. p. 2.


“A first concept [for how complexity science can help reframe understandings of natural systems and neuroscience] is emergence, which is typically defined as ‘the whole is more than the sum of its parts’. In the context of cognitive systems, emergence refers to those cognitive phenomena best understood at a spatial and/or temporal scale of organization that does not reduce to its constituent parts. Second is nonlinearity, which, in simplest terms, means that a system’s output is not proportional to its input, such as those resulting from exponential and multiplicative interactions. Third is self-organization, which refers to situations where order occurs without instruction or intervention from outside or within, such as a control unit that directs a central processing unit’s operation. Self-organization can be understood in terms of circular causality, where the macroscale of a phenomenon (e.g., heart pumping) constrain and are constrained by its microscale constituents (e.g., chambers and valves).” Favela, Luis H. & Mary Jean Amon. 2023. “Reframing Cognitive Science as a Complexity Science.” Cognitive Science. 47:e13280. 10.1111/cogs.13280. pp. 2-3.


“A vast body of theoretical and experimental work supports the idea that the brain operates at the tipping point between a phase of runaway excitation and a phase where activity is rapidly extinguished – the critical point. At the critical point, activity propagates through the system optimally, neither building nor fading. This regimen would endow the system with beneficial computational properties that include increased sensitivity to input, increased dynamic range, enhanced information transmission and storage capacity, flexible adaptability, and robustness in the face of noise or fluctuations. Yet, the underlying organizational principles that could drive the brain toward a state of criticality remain to be resolved....

“A revised version of the criticality framework postulates that rather than operating at the critical point, neural networks wander around its vicinity. Based on this view, there exists a broad range of configurations that could enable neural networks to present criticality. While operating in this ‘configurational corridor,’ networks would reap functional critical-like attributes without the need to self-tune to the critical point.” Irani, Martin & Thomas H. Alderson. 2023. “Tuning Criticality through Modularity in Biological Neural Networks.” The Journal of Neuroscience. 43(33):5881-5882. 10.1523/JNEUROSCI.0865-23.2023. p. 5881.


“The word Zayd uses for ‘destiny’ is qisma – ‘that which is apportioned’ – better known to the West in its Turkish form, kismet. And while I sip another cup of coffee, it passes through my mind that this Arabic expression has another, deeper meaning as well: ‘that in which one has a share’....

“‘My share in all that is happening ...’ I think to myself as I lie under the friendly Arabian stars. ‘I – this bundle of flesh and bone, of sensations and perceptions – have been placed within the orbit of Being, and am within all that is happening ... ‘Danger’ is only an illusion: never can it ‘overcome’ me: for all that happens to me is part of the all-embracing stream of which I myself am a part. Could it be, perhaps, that danger and safety, death and joy, destiny and fulfilment, are but different aspects of this tiny, majestic bundle that is I? What endless freedom, O God, has Thou granted to man...’” Asad, Muhammad. 1954/2000. The Road to Mecca. NY: Simon & Schuster. pp. 32, 37.


“For neuroscientists, there is no single dominant guide to discovery as affordances in ecological psychology. However, there are various contenders, such as the Bayesian brain, coordination dynamics, criticality, free-energy principle, Neural Darwinism, and neural reuse.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 6.


“... the primary aim of this book is to defend the following thesis: ecological psychology and neuroscience can be reconciled via complexity science.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 12.


“For Gibson and future ecological psychologists, direct perception is the claim that perception is ‘not mediated by retinal pictures, neural pictures, or mental pictures.’ In a word, it is antirepresentational.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 27; subquote: Gibson, J.J. 1986/2015. The ecological approach to visual perception (classic ed.) NY: Psychology Press. p. 139.


“Ecological information refers to the distributions of energy that surround an organism. They are higher-order properties in that they are necessarily spatiotemporal in nature; that is to say, an organism perceives them in their surrounding space over time....

“Though there may be certain energies (e.g., ambient light) in an environment, they are informative in relation to the environmental properties and the organism doing the perceiving. Environmental properties are the characteristics of perceived surfaces and substances. For example, a surface that can reflect light has the property of high reflectance, and a substance that does not give when pressed has the property of hardness.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 28.


“... ecological information specifies meaningful features of the world for an organism. Specification refers to a correspondence relationship between an organism and the actions it can perform in an environment. Such relationships are invariant by way of their regularity (i.e., lawfulness). That is to say, given particular ecological information, environmental properties, and organism capabilities, there will always be the possibility of certain actions available. Those actions are understood in terms of affordances, which are perceivable opportunities for behavior.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 28.


“The concept of affordances and their ontological status result in three radical consequences for the practice of psychology qua ecological psychology. First, the target of investigation in the psychological sciences is not the isolated organism that passively perceives stimulus from the environment....

“Second, perception-action generates lawful regularities in organism-environment systems, that is, ecological laws. Ecological laws are those patterns, or regularities, that emerge as an organism’s actions are informed and guided by ecological information, such as the information in the optic array generated during walking....

“Furthermore, ecological psychologists are often interested in the critical point whereby features of the environment and characteristics of the organism no longer facilitate affordances, for example, the critical point at which an aperture affords passing through or not based on a participant’s shoulder width. This critical point is described in terms of a A/S ratio, or aperture-to-shoulder ratio. Thus, a well-functioning perceptual system is sensitive to its own action- and body-scaled properties while perceiving what is afforded and what is not....

“The third consequence, and perhaps the most radical, is the resulting understanding of what is meaningful to an organism or not....

“Affordances are not just perceivable opportunities for behavior, as stated earlier. Affordances are directly perceivaable meaningful opportunities for behavior. But how can it be that meaning can be directly perceived and guide behavior? As Gibson questioned and concluded,

“‘How do we go from surfaces to affordances? And if there is information in light for the perception of surfaces, is there information for the perception of what they afford? Perhaps the composition and layout of surfaces constitute what they afford. If so, to perceive them is to perceive what they afford. This is a radical hypothesis, for it implies that the ‘values’ and ‘meanings’ of things in the environment can be directly perceived. Moreover, it would explain the sense in which values and meanings are external to the perceiver.’

“Here, Gibson is drawing attention to a crucial consequence of his conception of organism-environment systems exhibiting lawful perception-action properties. If ecological information specifies affordances, then ecological information also underlies what is meaningful to a perceiver. In other words, ecological information, such as patterns in ambient light, will specify in an invariant (i.e., lawful) way what an organism can do and not do, such as pass through an aperture or not. An aperture that is wide enough for a dog to pass through is meaningful–i.e., in the sense of useful–in a way that one that is too narrow is not. It follows then, that affordances are a source of meaning for organisms, that is, because they are–to put it simply–those features of the world they can do things with.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 31-33; subquote: Gibson, J.J. 1986/2015. The ecological approach to visual perception (classic ed.) NY: Psychology Press. p. 119; italics in original.


“Even on theoretical grounds, affordances, for example, do not work if the organism is not embodied–let alone if the organism is not situated in an environment.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 35.


“This book is not the first attempt to reconcile ecological psychology and neuroscience. As such, I do not claim to provide the best or only way to tell the story of what can be called ‘ecological neuroscience’ or ‘Gibsonian neuroscience.’ Still, given that there have been so few attempts, the area is open to interpretation and development in ways other, more established, areas are not. Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 69.


“Neural reuse is appealing to ecological psychologists in large part due to the ‘massive redeployment hypothesis.’ The massive redeployment hypothesis proposes that while brains have areas with specialized activity (i.e., neural circuits), those same activities underlie a variety of behavioral and cognitive functions. Here comes the part ecological psychologists like: It is because a brain is in a body that is in an environment that the same brain areas can be ‘redeployed’ for an assortment of functions. This is because different body configurations and environmental settings will provide conditions for the same brain area to contribute to different outcomes. As Anderson puts it, ‘neural, behavioral, and environmental resources [are] reused and redeployed in support of any newly emerging ... capacities.’ In view of this, neural reuse can be understood as offering a number of features appealing to ecological psychologists, including, but not necessarily limited to the significance of embodiment to constraining and structuring behavior and cognitive capacities, the significance of environmental situations in constraining and structuring behavior and cognitive capacities, and its rejecting of brain localization and classic treatments of modularity....

“Neural reuse can be understood as a form of population thinking in both of the senses presented: one, as referring to the evolutionary pressures driving the selection of neural groups, and two, as identifying the scale of neural structure and function most relevant to perception-action activities. When viewed in that light, the popularity of neural reuse among ecological psychologists is readily understood as a continuation of the interest in population thinking exemplified earlier by Reed.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 78; references: Anderson, Michael L. 2014. After phrenology: Neural reuse and the interactive brain. MIT Press; Reed, E.S. 1996. Encountering the world: Toward an ecological psychology. Oxford UP.


“P(H|D) = P(D|H) X P(H)/ (P/D)...

“In addition, Bayes’ theorem serves as the foundation of a variety of theories aimed at encompassing more general neural and psychological structures and functions, for example, active inference, Bayesian brain, predictive coding, and predictive processing....

“...at the core of all applications of Bayesianism is the idea of error minimization. In Bayes’ theorem, an ideal system strikes a balance between expectations (i.e., the hypothesis; H) and states of affairs (i.e., data; D), or a minimal error between the two. As a theory of brains and cognition, active inference builds from this core commitment to claim that ‘all facets of behavior and cognition in living organisms follow a unique imperative: minimizing the surprise of their sensory observations’”. Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 81; subquote: Parr, T., G. Pezzulo & K.J. Friston. 2022. Active inference: The free energy principle in mind, brain, and behavior. MIT Press. p. 6; italics in original).


“As an even broader theory of brains and cognition, one that also aims to explain life, the free-energy principle (FEP) also builds from the core commitment of error minimization in Bayes’ theorem to claim that ‘any self-organizing system that is at equilibrium with its environment must minimize free energy,’ or ‘the bounds or limits [of] the surprise on sampling some data, given a generative model’.... Like active inference, FEP treats cognition as fundamentally being about minimizing discrepancies between expectations (i.e., priors) and states of affairs (i.e., data). But unlike active inference, the FEP also treats living organisms as fundamentally being about minimizing discrepancies, where such ‘discrepancies’ are defined in terms of entropy and the tendency of living organisms to resist disorder.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 81; subquote: Friston, K. 2010. “The free-energy principle: A unified brain theory? Nature Reviews Neuroscience. 11(2):127-138. 10.1038/nrn2787. p. 127.


“The problem is that Bayesianism, methodologically speaking, does not provide a proper account of the nature of intelligence in living organisms.

“As a colleague and I have argued elsewhere, Bayesianism suffers from two major shortcomings as a theory of neural and psychological phenomena. First, its calculations and models are predominantly linear. This is a problem because neural and psychological systems are predominantly nonlinear. Second, when such models attempt to incorporate nonlinearities, they typically do so in terms of noise that is defined as being random and unstructured. This is a problem because while noise is widespread is [sic, = “in”?] neural and psychological phenomena, it tends to be deterministic and structured, which is contrary to how it is depicted in Bayesianism. Accordingly, any framework that models or explains organism-environment systems during affordance events as linear systems that exhibit only random and unstructured noise will be false.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 82.


“To integrate ecological psychology and neuroscience under the single framework of complexity science is to treat its targets of investigative interest as being complex systems whose understanding requires an interdisciplinary strategy. Specifically, it means supplementing or replacing their current concepts, methods, and theories with those of complexity science, which is already inter-disciplinary through and through.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 87.


“Synergetics is a framework for investigating systems with many parts that interact at various spatial and temporal scales. A number of features distinguish synergetics from other frameworks that investigate system-level phenomena. First, it focuses on spontaneous processes and structures, specifically, self-organization. Second, its aim is to, “unearth general principles (or laws) underlying self-organization irrespective of the nature of the individual parts of the considered systems”. In other words, a primary goal of synergetics is to discover general laws of the ways systems self-organize. Third, it conceptualizes systems in terms of macro- and microscopic spatial and temporal scales in a contextual manner. Specifically, there is no absolute ‘macro-‘ scale that applies to all investigations; what counts as ‘macro-‘ and ‘microscopic’ depends on the research question. This leads to the fourth and final distinguishing feature of synergetics: research is guided by the conceptualization and application of order and control parameters.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 96; subquote: Haken, H. 2016. “The brain as a synergetic and physical system.” In: Pelster, A. & G. Wunner (Eds). Self-organization in complex systems: The past, present, and future of synergetics. pp. 147-163. Springer. p. 150, italics in original).


“The slaving principle refers to the idea that the order parameter determines the activity of the system’s parts. Note that the slaving principle is not the idea that the order parameter determines the control parameters. This significant difference leads to the second commitment: circular causation.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 98.


“In the philosophical literature, five features can be commonly considered necessary for emergence: downward causal influence, novelty, relationality, supervenience, and unpredictability. The scientific literature, however, typically does not use ‘emergence’ to refer to all of those five features. I have argued that in the mind sciences, especially the cognitive and psychological sciences, ‘emergence’ is often used interchangeably with ‘interaction-dominant dynamics’.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 99.


“Interaction dominance contrasts with component dominance. A system’s dynamics are component dominant when the system-level dynamics are reducible to the additive and linear relationship of the dynamics the components have if separated and added together. One indication that a system is component dominant is if perturbations to one part of the system stay localized, where ‘local’ can be in temporal or spatial terms. A system’s dynamics are interaction dominant when they exhibit nonlinear feedback among the interactions of their parts, such that it is the continual interactions of the parts that facilitate the system-level dynamics. One indication that a system is interaction dominant is if perturbations to one part of the system do not remain local but reverberate throughout. As with synergetics, the kind of causation at work here is one of circularity: the system-level dynamics and the parts simultaneously structure each other’s dynamics. Like cybernetics, feedback is crucial in interaction-dominant systems. However, unlike cybernetics, the feedback is not in the service of prescribed outcomes for purposes of control. As complex systems, interaction-dominant systems are context-dependent such that varying contexts can alter the nature of the parts during interactions.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 99-100.


“Historical variation refers to the notion that fluctuations in a system are influenced by its previous states. This type of variation contrasts with logical variation, the central underlying assumption of most standard data analysis methods used in the mind sciences. Methods such as standard linear statistics treat differences between measurements as discreet from each other, such that variation is not influenced by history. In this way, given enough observations (e.g., coin tosses), measurements will adhere to the central limit theorem and fall along a Gaussian distribution. There is no doubt that linear statistics are useful when assessing all sorts of phenomena. However, when it comes to complex systems where historical variation is the rule and not the exception, reliance on methods that are ahistorical regarding their data will surely result in distorted or false conclusions. Remember, linearity underlies logical variation in that it is assumed that enough data points will fall along a Gaussian bell-shaped distribution.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 100.


“If a system’s dynamics are the result of linear (i.e., additive) processes, then it follows that the effects of perturbations will be localized in its individual components. This is because linear systems have minimal, if any, interactions, such that the system-level dynamics are the result of additive relationships among its relatively independent parts. On the other hand, if a system’s dynamics are the result of nonlinear (e.g., multiplicative) processes, then it follows that the effects of perturbations will not be localized and will percolate throughout the system due to interaction-dominant dynamics.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 102.


“Whereas cybernetics focused on predefined or prespecified outcomes of systems with feedback, systems theory centered on systems that organized without direct intervention or instruction from an outside source or central controller. Synergetics takes this approach a step further and explores general rules that result in self-organized behavior. Like fractals, self-organization seems to be ubiquitous in nature.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 103.


“What does universality have to do with the mind sciences? It is becoming evident that as more detailed data is obtained in the cognitive, neural, and psychological sciences–from finer spatial and temporal recordings of small-scale neuroanatomy to larger-scale social coordination–the more it appears that they exhibit universal features. Many natural systems ranging from the geographical to the biological exhibit fractal branching patterns and ratios, for example, rivers and neurons. It is even the case that nonliving and living systems can exhibit the same universal dynamics. Sandpiles and neuronal networks, for example, can exhibit the same correlation length among their sand-based and neuron-based avalanches [exponent of relationship between size of avalanches and their frequency].” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 104.


“One universality class that is gaining traction in the life sciences is the critical phenomenon of self-organized criticality [SOC]. SOC refers to the behaviors of a system at different spatial and temporal scales that tend to organize and exhibit phase transitions near critical states. SOC systems have interactions between components across scales that yield coherent global patterns of organization. Because these interactions are in constant flux and occur across scales, the dynamics of SOC systems occupy a wider range of temporal and spatial scales than is typical of comparable systems. As a result, research suggests that SOC is widespread in cognitive systems, from neuronal dynamics to temporal estimation.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 104-5.


“... datasets comprised of gene expressions are paradigmatic cases of high-dimensional data as there are seemingly innumerable relationships among genes, different temporal scales, etc. In a more technical sense, data are high dimensional when the number of features, or variables observed (p), are larger than the number of observations, or data points (n). This contrasts with low dimensional data, that is, when the number of observations (n) far outnumber the features (p).” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 107-8.


“Dimensionality reduction, in the simplest terms, is a data processing strategy that attempts to cut down on the number of a dataset’s features without losing valuable information.... As with any data processing or analysis techniques, one must be aware of the limitations of dimensionality reduction.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 108.


“The term ‘situated’ is used here to refer to the general claim that intelligent systems are always embodied–i.e., are constituted, in part, by a physical body–and embedded–i.e., are constituted, in part, by their environments. When intelligent systems are situated, it is also common for them to be distributed (e.g., an airplane-pilot-co-pilot system), extended (e.g., a human and their iPhone), and enactive (e.g., an animal’s sensorimotor system). Thus, here ‘situatedness’ refers to these clusters of intelligent system-level constitutions and organizations.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 120.


“NExT can be put into practice via a theoretical program for empirically investigating and understanding minded organism-environment systems by means of six hypotheses....

“Hypothesis 1: The organism-environment system is the privileged spatiotemporal scale of description to understand mind.
“Hypothesis 2: Neural population dynamics generate relevant states.
“Hypothesis 3: Mind is based on low-dimensional neural dynamics.
“Hypothesis 4: Body organizes into low-dimensional synergies to generate relevant states.
“Hypothesis 5: Mind fundamentally emerges at low-dimensional scales of organism(neural, body)-environment activity.
“Hypothesis 6: The NeuroEcological Nexus Theory [NExT] explains the architecture of the mind by means of a finite set of universal principles.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 122-3.


“... it is worth going into some detail about the three foundational points of Neural Darwinism:

“1. Developmental selection leads to primary repertoire.
“2. Experiential selection to yield secondary repertoire.
“3. Reentry....

“The primary repertoires are those morphological features that develop early in an organism’s life, such as the general layout of the body and early outgrowths of neural networks.... Increased novelty in the environment of the organism occurs after the earlier stages of development and once the organism leaves the controlled environment of the womb or egg. Accordingly, with the ability to successfully cope with environments of increased complication come decreases in inherited morphology. This is especially true for neurons, for the more inherited a capacity is, the less that capacity can cope with a novel factor.

“Once the basic genotype has been expressed in a particular environment (i.e., primary repertoire), the secondary repertoire goes into effect. The secondary repertoire accounts for experiential selection via changes in synaptic strength and network organization (i.e., neural plasticity). Based upon morphologically constrained behavioral experiences, the corresponding neural activity will be strengthened or weakened. Once an organism’s primary repertoires are in the process of expression in an environment, epigenetic development and alterations take place as a result of the experiences had by the organism. A human who plays the piano for many years, for example, will strengthen connectivity in neuronal groups associated with finger dexterity. The behavior resulting from interactions with the environment induces effects upon neuronal coordination and organization. Interacting with the environment does not cause changes to larger anatomical structures of the brain (e.g., cerebellum, frontal lobes, etc.), but they do cause changes of varying strengths and weaknesses at smaller scales (e.g., neural networks and synapses). These connections begin to develop into neuronal groups called maps. These maps are groupings of populations whose signals have been strengthened by environment-influenced behaviors....

“The third foundation of Neural Darwinism is reentry, or reentrant signaling. Reentry is the dynamic process whereby an organism’s cognitive and behavioral capacities are supported by anatomically distant maps in the brain, which are linked by reciprocal signals that coordinate (via synchronization and integration) with each other and the physical dimensions of the body and world with a high degree of spatiotemporal accuracy.... Although the primary repertoire is genetically inherited and can be thought of as ‘prespecified,’ its expression and the secondary repertoire are not. Organisms are selectionist systems that develop via experience. For that reason, reentry is a process that synchronizes and integrates signals simultaneously from multiple neuronal populations, which are themselves receiving signals from the body and world.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 126-7.


“Another crucial feature of reentry is that it contributes to the degenerate nature of the components and activities that underlay behavior and cognition. With regard to the brain, degeneracy refers to the ability of structurally different neuronal circuits and maps to give rise to the same function or output. Degeneracy is constant throughout an organism’s life because experiential selection (i.e., secondary repertoires) is ongoing. This means that during an organism’s lifetime, various structures will give rise to similar capacities, for example, consciousness, motor movements, and visual perception. As is made evident by the preceding examples, ‘structures’ is used broadly to include neuronal as well as behavioral and bodily configurations. A consequence of treating all of those structures as degenerate is that their various combinations can underlay the same or similar capacities. For example, different neuronal structures in the same environment could give rise to the same capacity.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 127.


“With this introduction to Neural Darwinism at an end, I am poised to state what a neural population is within the NExT framework. A neural population is a neuronal group that has been selected for over species development timescales (i.e. primary repertoire), individual organism lifetime (i.e., secondary repertoire), and moment-to-moment experience timescales (i.e., reentry), which have been selected for due to their contributions to synchronized and integrated brain-body-environment structures that have facilitated evolutionary advantages. In that way, neural populations are defined by their links to well-specified function.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 128.


“... NExT defines mesoscale as describing the organizational characteristics and dynamics of those neural populations that contribute to organism-environment system functions, like affordance events....

“The macroscopic refers to those target phenomena of interest, such as the brain’s contributions to intelligence and goal-directed behavior.”\

“There are multiple virtues to emphasizing the topological features at the mesoscale–one is that it facilitates the ability to identify neural network properties by abstracting away–to a certain degree-from the details of individual neurons that are not difference makers to targets of investigation.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 128, 129, 130.


“The significance of circular causality and its contribution to the contextual nature of organism-environment systems and the definition of their structure-function relationships cannot be stressed enough.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 129.


“Hypothesis 3 claims that the relevant neural population dynamics are low-dimensional. Specifically, they are low dimensional in a way that is identifiable via manifold theory.... Topological manifolds are the applicable kind with respect to Hypothesis 3. Topology is the mathematical study of the properties of objects that are maintained despite changing the shape of the object (e.g., stretching or twisting), without compromising the object’s integrity (e.g., cutting or ripping)....

“Specifically, a manifold is a topological space that is locally homeomorphic to Euclidean space of a given dimension.

“In short, the manifold hypothesis is the claim that very high dimensional datasets have much lower dimensional manifolds that capture their principal structure.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 131, 132.


“The neural manifold hypothesis is the claim that very high dimensional datasets–specifically, in the form of neural population dynamics–have much lower dimensional manifolds that capture their principal structure–i.e., ‘neural modes’–that generate specific behaviors. Motivated by increasing evidence, the neural manifold hypothesis claims that the spatiotemporal scope of neural activity causally related to and/or constitutive of a range of phenomena (e.g., motor control) may seem incredibly large but is in fact confined to a much smaller scale. In other words, while the activity of large numbers of neurons during any given task may seem to indicate high degrees of freedom, the subspace of relevant activity actually spans only a few variables....

“One way to understand the aim of the neural manifold hypothesis is as a method for identifying neural modes that cause and constitute various behavioral and cognitive activity. To that end, and in line with NExT Hypothesis 2, it is hypothesized that those neural modes are found at the mesoscale of population activity.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 133.


“In the context of organism-environment system activity, the degrees of freedom problem–also known as Bernstein’s problem and referred to as the motor equivalence problem–refers to the idea that for a body (i.e., motor system) to accomplish controlled movements, there are far more degrees of freedom in that system than are needed to successfully execute the action.... In other words, if natural selection is not wasteful, then an explanation needs to be provided as to why there are so many redundant degrees of freedom for any given action.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 136.


“There are a number of broad classes of motor control theories, such as those that emphasize reflex, motor programming, and dynamic action. By way of some of these theories, specific solutions to the degrees of freedom problem have been proposed. One is the equilibrium-point hypothesis, in which the brain indirectly controls motor actions via modifying neurophysiological states that influence but are independent from biomechanical variables. Another is optimal control theory, where tasks specify ‘costs’ in the form of accuracy and effort, and movements are selected by the motor system to minimize those costs via feedback. Hypothesis 4 of NExT is informed by a synergies-based solution to the degrees of freedom problem.

“In terms of bodily control of action, synergies are functional assemblies of parts (e.g., neurons, muscles, tendons, etc.) that are temporally constrained to act as a single unit.... It is because the body is a soft-assembled system that it can reconfigure (to varying degrees) into synergies based on task requirements. Systems are ‘soft assembled’ when their material constitution is not rigidly constrained so they can configure and reconfigure themselves into functional coordinative structures–or synergies–in a context-sensitive manner.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 136-7.


“This description of a synergy may lead one to mistakenly think that any isolatable system that is conducting a task is a ‘synergy.’ While such a labeling can be done loosely, it would, strictly speaking, be an incorrect application of the term. The accurate idea of a synergy is intended to explain how natural, biological systems come to successfully be selected for over evolutionary timescales and during the time scales of task execution, while being comprised of seemingly incalculable numbers of parts and seemingly limitless variables to consider. When understood with that purpose in mind, synergies provide an account of the body’s role in organism-environment systems and, moreover, a solution to the degrees of freedom problem.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 137.


“A classic example of the body as a synergy–namely, as a temporally coordinating functional unit–comes from research on speech production. In a series of experiments by Kelso and colleagues, it was demonstrated that perturbing one part of speech involving anatomy (i.e., forcefully displacing the subject’s jaw with a prosthesis) did not result in the inability to produce specific speech sounds (e.g., ‘baeb’ and ‘baez’). Instead, other parts of the relevant anatomy (e.g., lower lip and tongue) exhibited recriprocal compensation in order to continue to generate the desired speech sounds.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 138; reference: Kelso, J.S., B. Tuller, E. Vatikiotis-Bateson & C.A. Fowler. 1984. “Functionallly specific articulatory cooperation following jaw perturbations during speech: Evidence for coordinative structures.” Journal of Experimental Psychology: Human Perception and Performance. 10(6):812-832. 10.1037/0096-1523.10.6.812.


“Dimensional compression refers to the idea that the potential variables that can contribute to a particular synergy are high dimensional. But when they begin to couple into a functional unit, then they become low dimensional... Reciprocal compensation refers to the idea that in a synergy each component responds to changes in other components.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 138.


“The uncontrolled manifold (UCM) is a methodology for assessing the variability of movements with regard to tasks, or the aim of functional units. The UCM is an analysis that defines a particular configuration space that is populated by variables hypothesized to capture a particular movement. The activity that defines such tasks can be understood as the order parameter. UCM treats motor control as being fundamentally about the stabilization of performance variable values. These can be understood as the control parameters. Those values are quantified in terms of their being compensatory and uncompensatory with regard to the task. The methodology generates a state space where a manifold depicts the variables contributing to the task and quantifies the amount of constancy among those variables. The result is a ‘synergy index’, where variance along the manifold are compensatory variables when they maintain task performance (i.e., ‘good variability’) and variance perpendicular to the manifold are uncompensatory variables when they result in loss of performance (i.e., ‘bad variability’). A classic example of the application of UCM is in experiments involving two effectors tasked with producing particular amounts of magnitude. In these experiments, the two effectors are two fingers, and the task is to produce a total stabilizing force on a narrow support surface. According to the UCM hypothesis, if the effectors are functioning as a single synergy, then the increase in applied force by one effector should result in a compensatory decrease in applied force by the other effector. This hypothesis has been supported by these experiments....” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 139-140.


“NExT claims that a body is a soft-assembled system that can self-organize into synergies. As a synergy, the body is partially caused and constituted by neural population dynamics across a flexible range of variance that is constrained only by their ability to facilitate successful task completion.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 140.


“UCM methodology allows investigators to empirically assess the presence of and degree to which a system is a synergy. This allows for defining synergies without adhering to a priori definitions about where the boundaries of an intelligent system are drawn. Variables that contribute to synergies can be located in the brain, arm, or environment, as long as they facilitate successful task completion. Accordingly, bodies are properly understood as being part of adaptive, self-organizing organism-environment systems.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 140.


“As a complex system, circular causation plays a major role in facilitating self-organization within neural populations, within body synergies, and between the brain and body.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 141.


“Hypothesis 5 takes this line of reasoning [of low dimensional patterns within neural manifolds] a step further. As brain and body spatiotemporal scale activity has low-dimensional structure within high-dimensional data sets, so too does the environment. That is to say, the organism’s environment–i.e., ‘the surrounding-world of the animal’ or umwelt–can be understood as high dimensional with a principle structure that is low dimensional. Here, ‘high dimensional’ refers to the flood of potential stimulation constituting the world–e.g., light, odors, rocks, temperature, trees, etc.–and ‘low dimensional’ refers to ecological information.... ...ecological information are distributions of energy that surround an organism and are those patterns that uniquely specify properties of the world. They are higher-order properties in part because organisms perceive them in their surrounding space over time. The relational nature between environmental energies and organism that can or cannot perceive those energies is why ecological information specifies meaningful features of the world. Specification is a relationship between an organism and the actions it can perform in an environment. Those actions are the perceivable opportunities for behavior, or affordances.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 141-142, 144.


“The affordance pass-through-able is an organism-environment system event that is caused and constituted by reciprocally interacting spatiotemporal activity at the neural scale (e.g., head movement neural population manifold), body scale (i.e., synergy by way of function defined anatomical organization and movement), and environmental scale (i.e., organism and environment defined ecological information).” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 158.


“The state of the body (i.e., head location) [of a mouse trying to figure out if it can fit through a hole in a wall] and the environment (i.e., aperture edge) inform and constrain the state of the neural activity. If the head cannot move any further to the right because it hits up against the hole’s edge, then the neural population will also not continue activity in that direction, as exhibited by its location on the low dimensional manifold. So too does the state of the neural activity inform and constrain the state of the body and environment, such that where the direction of the head points will provide the perspective from which the body (e.g., eyes) will detect ecological information (e.g., light reflecting from the surface edges of the hole). In that way, direct perception is also maintained due to the fact that neural activity is informed and constrained by the direct engagement with ecological information.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 160.


“In particular, the body is fruitfully understood as a synergy that contributes to organism-environment systems. Remember, synergies are functional assemblies of parts (e.g., muscles, tendons, et.) that are temporally constrained to act as a single unit for specific tasks. The body’s ability to flexibly adapt to tasks is due in large part to its being a soft-assembled system, such that their material constitution is not rigidly constrained so they can configure and reconfigure themselves into functional coordinative structures–or synergies–in a context-sensitive manner. In this way, adaptation and flexibility are forms of ‘good variability’ when they facilitate organizations that contribute to task completion....

“In the current example, a mouse body is a synergy when it is organized to enable the function of a successful encounter with a hole in the wall. Here, a ‘successful’ synergy is not equivalent to the affordance pass-through-able. For the hole to afford pass-through-ability, the mouse would need to be able to move through it. In that way, the synergy is successful in that the functional coordinative structure that is the body contributed to passing through. However, a synergy can be a ‘successful’ functional coordinative structure when features of the environment do not facilitate an affordance. The mouse’s body can be a successful synergy that detects a hole that does not afford pass-through-ability. That is to say, it takes a properly functioning unit that can engage with the environment to successfully detect opportunities for both action and non-action. It takes a synergy to contribute to engagement with both affordances and non-affordances. In other words, all affordances require synergies.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 160-1.


“For example, the body qua synergy requires that perception and action are continuous, as the body will adapt and reorganize depending on the structure of environmental information, which is itself partly caused and constituted by the organization of the body.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 161.


“Affordances are events that spread across organism-environment systems. For the hole in the wall to afford pass-through-ability, the integration and coordination of spatiotemporal scales must occur. The principle structure of low-dimensional manifolds in neural populations exist in a reciprocal relationship that both causes and constitutes the body’s structure and function, while also being caused and constituted by the body’s activities. The principle structure of low-dimensional synergies of the body exist in a reciprocal relationship that both causes and constitutes its relationship with the environment, while also being caused and constituted by the environment’s features,. The ecological information of the environment exist in a reciprocal relationship that both causes and constitutes its relationship with the organism, while also being caused and constituted by the organism’s features.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 161.


“It must be emphasized that the causal and constitutive contributions of each of these spatiotemporal scales to organism-environment systems during affordance events are low dimensional. They are ‘low dimensional’ in a metaphysical and epistemic sense. Metaphysically speaking, the brain, body, and environment have a principle structure that causes and constitutes the phenomena of interest. During the affordance event that is a mouse passing through a hole, its relevant neural activity displays a neural mode for head movement (which coordinates with other neural modes, e.g., locomotion), its body organizes into a synergy, and the environment is constituted lawful ecological information. Each of these–i.e., neural mode, synergy, and ecological information–are low dimensional. They are metaphysically low dimensional in that there is a principle structure that integrates and coordinates with the other scales in systematic ways to facilitate task-defined success, such as the mouse successfully moving through a hole in the wall.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 161-2.


“... NExT can be practiced via the following steps:

“1. Identify the phenomenon of interest....

“2. Define the order parameter....

“3. Define mathematical model to capture the physical model....

“4. Solve mathematical model to identify system states....

“5. Identify and define control parameters....

“6. Measure-the-simulation.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. pp. 163, 164.


“The worst readers are those who act like plundering soldiers. They take out some things that they might use, cover the rest with filth and confusion, and blaspheme about the whole.” Nietzsche. F. 1879/1913. Human all-too-human: A book for free spirits, part II. NY: The MacMillan Co. p. 69. Quoted in: Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 170.


“... the neuroscience of the mid to late twentieth century could be understood as being guided by two perspectives: the Hodgkin-Huxley tradition, which emphasized biological features of neurons, such as electrophysiological properties, and the McCulloch-Pitts tradition, which lay emphasis on abstract features of neurons, such as their logical properties. The remainder of the chapter [chapter 3 of the book about the history of neuroscience] motivated the claim that neuroscience (i.e., behavioral, cognitive, computational, and sensory) embraced the McCulloch-Pitts tradition and became infused with cognitivist commitments to the information-processing features of neural systems.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 171.


“At its most straightforward, the phrase ‘ecological brain’ means that brains must be understood as always being part of ecologies.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 172.


“... recent work I conducted with Edouard Machery provides empirical evidence to support the claim that the concept of ‘representation’ is far from understood or defined in a generally accepted way. Results from our experiments with an international group of researchers that included neuroscientists suggest that they exhibit uncertainty about what sorts of brain activity involve representations or not and they prefer to characterize brain activity in causal, nonrepresentational terms. Given that the vast majority of these participants believe that cognition involves representations, it is quite concerning that they are unsure how to apply this core concept and that they exhibit preferences for descriptions of brain activity that are nonrepresentational.” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 175.


“For cognitivists, cognitive faculties like language just so happen to be the product of meat, but their true nature need not be explained as such (e.g., their logical properties are what matters).” Favela, Luis H. 2024. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. NY: Routledge. p. 176.


“For most progress in neuroscience has been limited to delineating how different neurons are connected to each other and how they communicate with electrical and chemical signals. Such work has produced a massive literature, impressive in its scope and depth, but it has not yet revealed how the brain works as a whole or how it produces behavior. In fact, contrary to popular belief, neuroscientists still do not understand how the nervous system of any organism produces any type of behavior. Even the behavior of the roundworm C. elegans remains unexplained.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 1.


“The failure to explain behavior is seldom acknowledged by neuroscientists, and apparently unknown in the lay public. Even when acknowledged, opinions differ as to why, despite our detailed knowledge of neural structure and signaling, we still cannot explain behavior. Some think that there is too much noise in the sensory input, or that behavior is too variable; while others think that mechanisms of behavior could be fundamentally non-deterministic, invoking the ubiquitous uncertainty principle from quantum mechanics. Still others think that, because the brain is so complex, to understand it we must first map the connections of every neuron and record all the signals, generating ‘big data’ that can be processed with powerful computers.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 1.


“Neuroscience, I shall argue, is now in the middle of such a crisis [a scientific revolution of paradigm per Kuhn], because its current paradigm rests on shaky foundations.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 2.


“It would be a mistake to believe that, simply by inserting variables between stimulus and response, i.e. by focusing on the internal structure as most cognitive scientists did, one can move beyond the behaviorists.... This assumption of linear or unidirectional causation is unquestioned among most students of behavior–be they reflexologists, psychophysicists, ethologists, Hullian or Skinnerian behaviorists, cognitive scientists, or systems neuroscientists. None of them ever imagined that it could be wrong, but it is.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 3.


“Neural output at the final common path from alpha motor neurons in the spinal cord to muscles is necessary for normal behavior as observed. But necessity does not equal sufficiency. Neural output is necessary but not sufficient.

“As Bernstein first pointed out, what we call behavior is not the sole result of neural output. Take the simplest example of standing: when standing the neural output sent to the muscles via the final common path is indeed producing muscle contraction and exerting forces. But there are other forces acting on the body (e.g. gravity, wind) that determine the behavior of standing as observed. Any behavior is the result of two types of influences, one from the organism’s nervous system, and the other from the environment....

“The first step toward an understanding of behavior, then, is to appreciate that neural output is insufficient to determine behavior as observed. This can be called the ‘insufficiency’ principle.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 4; reference: Bernstein, N. 1967. The coordination and regulation of movements. Oxford: Pergamon Press.


“In spite of environmental disturbances, behavior is still achieved successfully most of the time. If neural output cannot be equated with the actual behavior. The behavior is a controlled result, the visible portion of a hidden tug of war between invisible environmental disturbances and actual neural output. The neural output from the final common path must always vary according to the changes introduced by the unknown disturbances. How can the necessary variations be generated by the nervous system to cancel the effects of the disturbance exactly? If the source of disturbance is often unknown and its magnitude unpredictable, how does the brain know how much output to produce, and when? This is what I have called the calculation problem....

“There is, however, a far simpler solution to the calculation problem. Negative feedback control systems can solve it without performing the inverse calculations, without knowing what the disturbances are or where they come from, without internal representations of the physics of the environment or feed-forward computations.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 5.


“No behavior can make any sense without invoking some internal reference signal representing the ‘should be value’ of the controlled variable. The emergent property of a negative feedback organization is teleology rather than linear causation.” Yin, Henry. 2020. “The crisis in neuroscience.” In: The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV. Mansell, W. (ed). pp. 23-48. Elsevier. 10.1016/B978-0-12-818948-1.00003-4. [Uncorrected Proof] p. 23.


“In this paper, we review three questionable assumptions whose reconsideration may offer opportunities for a more robust and replicable science:

“(1) The localization assumption: the instances that constitute a category of psychological events (e.g., instances of fear) are assumed to be caused by a single, dedicated psychological process implemented in a dedicated neural ensemble.

“(2) The one-to-one assumption: the dedicated neural ensemble is assumed to map uniquely to that psychological category, such that the mapping generalizes across contexts, people, measurement strategies, and experimental designs.

“(3) The independence assumption: the dedicated neural ensemble is thought to function independently of contextual factors, such as the rest of the brain, the body, and the surrounding world, so the ensemble can be studied alone without concern for those other factors. Contextual factors might moderate activity in the neural ensemble but should not fundamentally change its mapping to the instances of a psychological category.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 246.


“Current guiding assumptions contrasted with revised assumptions for the study of brain-behavior relationships

“Current guiding assumptions

“(1) Localization assumption: instances of a psychological category can be localized to a dedicated neural ensemble. Instances of the same psychological category are assumed to be more similar to each other with respect to that neural ensemble and more different from instances of other psychological categories, which have their own ensembles.

“(2) One-to-one assumption: neural ensembles correspond one-to-one with psychological categories. This correspondence is stable across all instances of the category, regardless of context, people, measurement strategy, or experimental design.

“(3) Independence assumption: a stimulus will reliably evoke activity in a specific neural ensemble that produces an instance of the specific psychological category of interest. This neural ensemble can be studied separately from other signals that may moderate its function.

“Revised assumptions

“(1) Whole-brain signals contribute to mental events: instances of a psychological category arise from activity across the entire brain, not from a separable neural ensemble.

“(2) Many neural ensembles for one psychological category: there are degenerate (many-to-one) mappings between neural ensembles and a psychological category.

“(3) Mental events emerge as a complex ensemble of signals: an instance of a psychological category emerges from a complex ensemble of signals from the brain, body, and world. These signals can only be understood in relation to the rest of the ensemble; i.e., each may have a weak effect on its own, but a strong effect when considered collectively.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 247.


“Degeneracy of causal mechanisms is an organizing principle of virtually all biological domains. In the nervous system, many combinations of neurons give rise to the same intrinsic network with the same function and different patterns of neural activation give rise to the same behavior.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 251.


“In other words, we suggest, like others, that the brain is a complex system continually influenced by input signals from the body and the world (which we refer to as the brain complexity hypothesis).

“In the brain complexity hypothesis, a given neuron does not function in isolation and its action potentials are profoundly influenced by its neural context.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 252.


“Evidence ... suggests that the relevance of any neuron’s action potentials to a given psychological process is dependent on the other neurons it is interacting with. For example, in the anterior cingulate cortex (ACC), a similar pattern of BOLD [blood oxygen level dependent signals] activity contributed to either an attentional function or a memory function, depending on the regions to which it was functionally connected during a task. The ACC is considered a ‘rich-club’ hub because it is densely interconnected with many groups of neurons throughout the brain. The dense interconnections between the ACC and other nodes allow this region to take on different functions (e.g., emotion, multimodal integration, decision making, value, attention, and visceromotor control), depending on the ensemble to which it belongs, suggesting that isolated neural signals do not have inherent psychological meaning.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 252.


“A routinely overlooked aspect of the neural context in brain-behavior relations involves the signals associated with the sensory conditions of the body. These signals routinely go unmeasured in studies of psychological phenomena, yet evidence suggests they play a substantial role. For example, an individual’s heart rate modulates functional connectivity between several regions involved in autonomic regulation and, likewise, respiration rate correlates with signal changes across the whole-brain during resting state fMRI. The signals of import may be the sensory surfaces of the body (peripheral interoceptive signals informing on the state of the body), or the motor prediction signals that control the viscera, the immune system, energy regulation, and so on.... The often overlooked role of bodily signals may offer an alternative explanation for intrinsic fMRI activity observed in the ‘resting state’, which involves no task-based stimulation, but does involve continuous and dynamically changing brain-body interactions, meaning that ‘intrinsic activity’ may be better understood by considering the broader signal context.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. pp. 252-3.


“The standard empirical paradigm when investigating mental phenomena typically follows these steps: researchers formulate a hypothesis, design an experiment that can test the hypothesis, and then analyze data using statistical methods that are conventionally used to test similar questions. This approach does not require researchers to specify their assumptions up front, which may make those assumptions difficult to identify and evaluate, let alone change them in future investigations. A model-first approach partially remedies this situation, because researchers begin an investigation by formally specifying a model of the phenomena of interest and then formulate hypotheses based on this model and design an experiment to test it. Model specification requires an explicit acknowledgment of assumptions, allowing researchers to evaluate and refine their assumptions and the research practices conditioned on them.” Westlin, Christiana, Jordan E. Theriault, Yuta Katsumi, Alfonso Nieto-Castanon, Aaron Kucyi, Sebastian F. Ruf, Sarah M. Brown, Misha Pavel, Denisz Erdogmus, Dana H. Brooks, Karen S. Quigley, Susan Whitfield-Gabrieli & Lisa Feldman Barrett. 2023. “Improving the study of brain-behavior relationships by revisiting basic assumptions.” Trends in Cognitive Sciences. 27(3):246-257. 10.1016/j.tics.2022.12.015. p. 253.


“Microelectrode, EEG, and MEG measurements all support the hypothesis that when separate cortical areas contribute to the contents of consciousness, they exhibit enhanced synchrony in the gamma frequency band that may be phase-locked to a slower theta rhythm.... These and other findings suggest that the physical bases of conscious states consist of spatially dispersed, but reentrantly interconnected, neuronal groups in a widely distributed set of brain areas constituting a ‘Global Workspace.’” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. p. 2.


“To contribute to our understanding of consciousness, such a global brain theory must be in accord with the extraordinary variety of the contents of consciousness.... Given the implausibility of evolving an instructive mechanism to govern this complex and variable process, it is necessary to invoke a selectional theory. Neural Darwinism, or the theory of neuronal group selection, is just such a theory. It maintains that the brain gives rise to repertoires of variant neuronal groups of vast complexity and diversity. Selection from these repertoires of neuronal groups occurs to match the novelty and diversity of experience in an integrative and adaptive fashion.

“Neural Darwinism has three tenets:
“(1) Developmental selection – during the development of the brain, neurons that fire together wire together. While there are a number of genetic constraints on the formation of brain circuits, a number of epigenetic processes leads to extensive individual variance. These circuits constitute a primary repertoire for further selection.

“(2) Experiential selection – During development and after the formation of variant neuroanatomy, changes in synaptic strength result in the further selection of variant neuronal groups that is characteristic of individual experience, constituting a secondary repertoire. The distribution and magnitude of these changes are constrained by inborn value systems, a diverse set of neural circuits producing various neuromodulators selected over evolutionary time.

“(3) Reentry – Long-range, reciprocal, and massively parallel connections from one brain area to another provide the dynamic sptatiotemporal coordination in circuits of groups that is necessary for integrated and adaptive conscious behavior.” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. p. 2.


“A characteristic feature of the cerebral cortex is the presence of corticocortical connections linking various neurons in spatially dispersed regions of the cortex to one another in a reciprocal fashion. Similarly, the thalamus projects a large number of axons to all areas of the cortex, and the cortex projects an even larger number to the thalamus. Together the corticocortical, corticothalamic, and thalamocortical connections provide a necessary structural basis for dynamic reentry, the ongoing reciprocal signaling within the cortex and between the cortex and the thalamus, constituting a Dynamic Core. Reentrant coupling can result in the formation of synchronous time-locked patterns of activity essential to connecting and integrating the distinctive functions of different brain areas. Reentrant activity allows a brain area having responses originally evoked by sensory input to give similar responses in the absence of that input. By this means the brain ‘speaks to itelf,’ a necessary basis for memory and thought.” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. p. 2.


“How can we account for qualia, subjectivity, and the self? According to the selectional theory based on the behavioral trinity, the experience of qualia occurs in each individual as a set of discriminations: ‘heat’ is not ‘green,’ ‘green’ is not ‘touch,’ etc. In this view, the complex unified scene at any given moment is a composite of multiple different discriminations integrated within the Dynamic Core.” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. p. 4.


“According to Neural Darwinism, qualia reflect higher-order discriminations entailed by the workings of the Dynamic Core. For example, to the conscious individual, the experience of blue can be distinguished from the experience of warmth, which can be distinguished from the experience of an odor. No possible description of a phenomenal experience would enable an unequipped individual lacking the proper brain structures, body, or exposure to the appropriate stimuli to have that phenomenal experience. Nonetheless, the correspondence between behavior and report of an individual’s qualia as discriminations can, to a large degree, be studied from a third-person point of view.” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. pp. 4-5.


“Exactly ‘who’ experiences qualia in a given body? Or, more succinctly, what is the self? In accordance with the behavioral trinity, the self emerges from brain responses to bodily signals arising in the sensorimotor system of an individual agent. These predominantly motor signals serve to distinguish the body’s sense of agency from signals evoked by the movement of another animal’s or agent’s body. This notion is consistent with the hypothesis that, in sensing agency, motor acts are evaluated internally by comparing signals from a feedforward model of perceptual signals to those arising from the body’s motion.” Edelman, Gerald M., Joseph A. Gally & Bernard J. Baars. 2011. “Biology of consciousness.” Frontiers in Psychology. 2(4):1-7. 10.3389/fpsyg.2011.00004. p. 5.


“A a molecular level, many of the ‘neuron-specific molecules’ (voltage-gated channels, molecules that form synaptic structures) were already present in all major animal clades before the earliest fossils. Even some bacteria have genes homologous to those making these molecules, which means that these genes were present in the common prokaryotic/eukaryotic ancestor, which could have been as long as 4 billion years ago. The functions of all these genes in single-celled organisms is not known, but a reasonable guess is that voltage-gated channels functioned to regulate intracellular ions and water of the ancestral prokaryotes, to keep them from bursting in the hypotonic water that was their environment, and were only later specialized for communication. In fact, modern bacteria use voltage-gated potassium channels to communicate the presence of metabolites to their companion bacteria that have formed a biofilm, a grouping of many bacterial cells. The K+ ions released by the activated bacteria depolarize nearby cells, which activates their voltage-gated K channels causing them to release their own K+ ions, a process that propagates across the biofilm.” Kristan, William B., Jr. 2016. “Early evolution of neurons.” Current Biology. 26:R949-R954. pp. R950-1.


“Kv channels were probably the only voltage-gated channels in the earliest animals and were used to regulate cell volume by changing the ionic content of the cell in response to cell membrane stretch. Cav channels probably appeared next, as a way to control the internal metabolic state of the cell, and in later organisms, to regulate the beating of cilia and the contraction of muscles. Cells with the proper combination of Cav and Kv channels could then generate action potentials, which expanded the cellular capabilities in many ways.

“With action potentials already possible, what was the selective advantage of adding Nav channels? One possibility is that cells could then use Cav channels for other purposes, like releasing transmitters or to avoid the build-up of intracellular Ca2+ to a toxic concentration. An alternative explanation is that, because Na-dependent action potentials are shorter in duration and conduct more rapidly than Ca-dependent ones, Nav channels were selected only later, when predation made rapid movements become increasingly beneficial. Making action potentials shorter in duration may have been augmented by the evolution of Kv channels that had faster kinetics, which would quickly turn off the fast depolarization caused by Na channels and make behaviors as fast as possible.” Kristan, William B., Jr. 2016. “Early evolution of neurons.” Current Biology. 26:R949-R954. p. R951.


“A sponge takes water in through many openings, pushes it through channels lined by cells with beating flagella that force the water into a large central cavity, from which it exits through the osculum. A strong mechanical stimulation to the body causes cells lining the channels to release transmitters, including glutamate, GABA and nitric oxide, which are carried by the water to cause coordinated contractions of the muscles in the body wall and osculum. In effect, sponges use these transmitters as hormones, with flowing water taking the part of blood in our own endocrine system, using many neuron-like molecules for this purpose.” Kristan, William B., Jr. 2016. “Early evolution of neurons.” Current Biology. 26:R949-R954. p. R952.


“Once the cellular mechanisms had evolved to make chemical synapses, one can imagine that neurons began making synapses with one another, so that some of them could be specialized to accept input from other neurons rather than from outside stimuli; i.e., these neurons became interneurons. As the predator-prey competition ramped up, there would be advantages to being able to sense both food and predators in more ways, particularly at some distance. One can imagine that detecting chemical gradients could use the molecular tools available to the early multicellular animals, followed by sensation at a distance, such as vision (well-formed fossil eyes are found at 525 million years ago) and substrate vibrations. Having interneurons would allow both efficiency (for example, a single interneuron could sense different modalities of input from one location, rather than having different interneurons for each modality) and flexibility (for example, input from one location could be ignored if a stronger or more important input came in from another location).” Kristan, William B., Jr. 2016. “Early evolution of neurons.” Current Biology. 26:R949-R954. p. R953.


“While many models and analyses [of development] focus on these key elements [genotype & phenotype], another is often neglected: the physiological processes that underlie morphogenesis. This is the control layer that sits between the genomically specified cellular hardware (proteins) and the form and function that selection sees: anatomy and behavior. In effect, the behavior of cellular collectives in morphogenesis is the software of the system–the functional outcomes of the molecular machines encoded by genomic information. This is relevant not only for embryogenesis, which converts compressed genomic information into a rich emergent set of large-scale structures, but also for regeneration, metamorphosis, remodeling, and other processes which establish and modify growth and form.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 1.


“One key property of developmental morphogenesis is its emergent nature, in which the relationship between genotype and phenotype is highly indirect. It has long been clear that genomes do not directly code for anatomies; instead, DNA encodes for proteins–the nano-level hardware made available to each cell. The behavior of cells, in a ‘social’ context of multicellularity, is what gives rise to functional anatomies. Cellular behaviors include proliferation, migration, differentiation, shape change, and apoptosis, operating in parallel over millions or billions of cells that are signaling to each other via chemical, electrical, and mechanical modalities–coordinating directly, at long range, or by using their microenvironment as a stigmergic scratchpad.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 3.


“A major implication of this architecture is that it is irreversible–while it is straightforward to watch (or potentially to simulate) how biology follows local rules of chemistry and physics and thus to discover what anatomy emerges from a given genome, the inverse problem is in general unsolvable: determining which protein sequences must be encoded to produce an arbitrary, desired large-scale anatomical form. This irreversibility of the recursive, highly emergent process of morphogenesis is what limits full-scale Lamarckism: the difficulty is not how to penetrate Weismann’s barrier and edit the genome in light of somatic experience–mechanisms exist for this. Rather, it is how to know what to change in a genome to produce a desired feature based on physiological events (e.g., a longer neck).” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 3.


“Metazoan cells have numerous adaptive behaviors because they derive from ancestral unicellular organisms that needed a full range of behavioral capabilities to survive. Thus, the evolution of metazoan anatomy operates not on a passive material, but on an agential one. I argue that what evolution is really searching is not the enormous space of all possible local rules, but instead the space of behavior-shaping signals by which cells hack each other’s functionality, and that the collective intelligence of cellular swarms has major implications for the rate and course of evolution.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 3.


“The framework discussed herein extends the study of the relationship of intelligence and evolution in several basic ways. It extends the notion of intelligence to sub-organismal scales, casting morphogenesis as the result of collective intelligence at the molecular cell, tissue, and organ levels. It operates within a gradualist perspective on intelligence and goal-directedness, which are both used here in a naturalistic, cybernetic, engineering sense of varied degrees of competent problem-solving in diverse spaces by unconventional agents (i.e., not restricted to higher-level cognitive capacities in brainy animals).... Moreover, it expands the concept of intelligent behavior across a key invariant: effective navigation of diverse problem spaces, which includes problem-solving in physiological metabolic, transcriptional, and anatomical spaces.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 6.


“Analysis of morphogenesis as the behavior of a collective intelligence of cells leads to the following key proposals. First is that the space which evolution actually searches is not only the space of microstates of the genome, but also a much more tractable space of behavior-shaping signals: evolution exploits cellular intelligence as a highly exploitable affordance.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 6.


“The key feature of scaling up individuality (whether evolved or engineered) is for higher levels of control to get their components to do things they do not do when operating as individual units. This can be discovered by extracting the parts of organisms and examining their behaviors in new contexts.... A morphogenetic and behavioral example is seen in Xenobots. Frog embryo skin cells, in vivo, form a two-dimensional, passive layer on the outside of the animal that protects it from pathogens. However, when liberated from the instructive influences of the other cells, frog epithelial cells instead form a Xenobot–a functional, self-motile construct with many novel behaviors that are normally suppressed and hidden by the instructive signaling of other cells during development.... Thus, it is not obvious what the default morphogenetic behaviors and capabilities of cells are, because of the ubiquitous dominating controls of other cells in their environment....

“These and other examples indicate that biological components are themselves, to varied degrees, autonomous, but are controllable by signaling from other cells.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. pp. 6-7.


“The dynamic of using simple signals that take advantage of the recipients’ complex, reliable repertoire generalizes to the concept of hacking, which is applicable at multiple scales and in many contexts, ranging from chemical signals in cellular induction to colony-scale behavioral phenomena driven by acoustic signals. The crucial focus in this concept is on the role of an agent that takes advantage of affordances in its own way, not necessarily in the ways ‘intended’ by an engineer, or by evolutionarily-prior functions.... An amazing example of morphogenetic hacking is the formation of galls, where signals from a parasite force the leaf cells away from their normal flat, green tissue phenotype and into building spiky, three-dimensional colorful forms.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 7.


“Thus, the concept of hacking extends well beyond the typical strategies used for traditional engineering with inert materials, or computational matter, to systems with significant agency: hacking is conceptually linked to behavior shaping. In this perspective, everything in biology is a hacker, reaping rewards of efficient manipulation of its environment (and its internal components) using the appropriate tools (from direct chemical effects to subtle signals meant to be interpreted by complex agents).... Evolution can thus search the space of behavior-changing signals, exploiting the complex, agential nature of the cells which are its substrate as a hugely powerful set of affordances.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 7.


“Fortunately, there are now tools available to begin to think in different ways about morphogenetic control.... The first is cybernetics: by emphasizing the information-processing capacities of multiscale components of living systems (with all of the attendant implications of circular control, multiscale causality, etc.), it becomes possible to recognize the reliability of morphogenesis as a consequence of the goal-directedness of underlying processes. It is essential to abandon the traditional scientific teleophobia because cybernetics and control theory now provide a mature, naturalistic, quantitative science and engineering approach to mechanisms with goals.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. pp. 9-10.


“The crucial transition is recognizing that homeostatic cycles, the atoms of cybernetic systems, are not merely feedback loops (which are widely accepted as ubiquitous in biology), but are the first run on a spectrum of intelligence. Intelligence is used here in William James’ definition, not limited to advanced metacognition in primates. The field of basal cognition seeks to unravel the evolutionary origins of the brain’s remarkable trick–unifying the activity of millions of cells (neurons) toward a common purpose in behavioral space.... From this, the view of the brain as a collective intelligence has been enlarged to understand the morphogenetic transformations of the body as a collective intelligence of cellular swarms, which solve problems in other spaces. Thus, the robustness of development is not of first order (do the same thing reliably each time), but of higher degree (achieve the same target morphology, by various means, despite various perturbations).” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. pp. 10-1.


“But, these competencies [robustness of development; see previous quote] cannot be definitively ascertained from observations of the default course of morphogenesis, which obscures the ability of living tissue to handle novelty (of both, external environment and internal composition) and lulls the observer into a limited expectation that genomes code only for specific outcomes and no more. A very rich set of examples belie this misconception and instead support a view of morphogenesis as a goal-directed, homeodynamic process.

“The most obvious examples are seen in regulative development and regeneration, where cells work to implement and maintain a large-scale form (target morphology) despite surgical, genetic, and physiological sources of defects. But it goes much further than that. Tadpoles, in which the native eyes are prevented from forming and an ectopic eye is instead placed on the tail, can see and perform well in visual behavioral training, even though the ectopic eye connects to the spinal cord (or just to peripheral tissue) rather than to the brain–this radical change to the sensory-motor architecture does not require generations of adaptation to produce successful behavior..... Perhaps the most remarkable example of this is Slijper’s goat, in which the effort of trying to walk upright (due to lack of forelimbs) drove, in one generation, many of the anatomical and physiological changes usually thought to require long periods of evolutionary adaptation to bipedalism.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. pp. 11-12.


“Bioelectric networks provide modularity (triggers of complex subroutines, such as a simple voltage state that triggers the ‘build an eye’ or ‘build a leg’ subroutine)–a known component of evolvability. They also provide an important kind of coarse graining, since voltage is a high-order parameter over ion channel gene and protein microstates, and individual ion concentrations: electrogenic proteins can be swapped out as needed, and everything still works as long as the bioelectric state is correct.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 13.


“The concept of morphological homeostasis during regenerative repair could be extended naturally to the broader concept of morphological homeorhesis, in which developmental progression is a collection of regenerative repairs: each stage is in effect a ‘birth defect’ from the perspective of the subsequent stage and is ‘repaired’ by regulative development which seeks to minimize error (i.e., system-level stress) relative to the bioelectric target morphology.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 15.


“... the field of basal cognition seeks to understand how evolution gives rise to increasing competencies in navigating diverse problem spaces. Here, however, the focus is on the second half of the loop: how do these problem-solving competencies affect the evolutionary process itself? The roles of basal agency in evolution are now beginning to be discussed. Models are being formulated for understanding how each layer of the multiscale competency architecture of life deforms the option space for the layers above and below it. The above-mentioned examples of induced lens cells recruiting others, and of artificially large cells using a different molecular mechanism to complete tubulogenesis, demonstrate behavior shaping and top-down control.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 15.


“Concepts like ontogenic recapitulation of phylogenetic events stress the fact that developmental mechanisms must operate with whatever components and signals had been established before (were adaptive within the prior historical context of that species). This is true, but there is a complementary aspect, because of the ubiquitous unexpected scenarios that every embryo has to face: environmental changes, genetic mutations, physiological stressors, parasites, and numerous other challenges that cannot be planned for in advance. Thus, biological systems evolve under pressure to remain flexible enough to accomplish coherent morphogenesis despite a wide range of perturbations.

“This means that the most successful, robust embryogenic and regenerative processes must not assume prior states strongly. The above-mentioned examples show how little embryos can take for granted.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 17.


“Animals with robust morphogenetic control due to high levels of plasticity are resistant to changes of circumstance (injury) at many scales: they regenerate after loss of limbs and organs (degradation of body-scale information), they suppress cancer readily (degradation of tissue-scale information) and they resist aging (degradation of cell-level information).

“The best examples of this counter-intuitive dynamic are planaria, which not only recover their entire bodies from even small fragments, but are very cancer resistant and apparently ageless. This raises a crucial puzzle for the traditional view of genomes as specifying form and function. Why does this extremely ‘long-lived’ animal (greatly out-shining animals like humans or elephants, in which long-term cancer suppression is commonly touted) avoid cancer despite consisting of about one-third its cell number as stem cells? Moreover, because planaria often reproduce by fission, any mutation that does not kill the stem cell is propagated into the next generation and expanded, resulting in animals that are mixoploid chimeras with an extremely messy genome. How does the animal with the messiest genome have the best morphological control?

“An evolutionary intelligence ratchet

“A possible answer to this puzzle merges the above concepts of evolution producing versatile problem-solving machinery. Of course, the problem-solving competencies are themselves produced by genetically encoded hardware, suggesting the view of two kinds of genomic information: that which directly specifies phenotypes (e.g., sequence of protein enzymes, or structural genomes) and that which specifies a problem-solving competency (second-order computational capacities).” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p18.


“It has been suggested that when evolution cannot make efficient gains by optimizing the hardwired components, the remaining targets for optimization are the competency mechanisms themselves. This starts a feedback loop, because each gain in competency makes it even harder to judge the structural genome, which exacerbates the drive toward improving competencies–a ratchet for multiscale intelligence that can readily be seen in computational models of the process. Planaria, salamanders (which regenerate, but are not immortal), and mammals all represent different degrees of how far this ratchet has operated in their lineages, because other forces oppose it (e.g., complexity drain). This phenomenon is familiar for example in human evolution, in which case evolutionary pressure for the largest muscles has been lifted, because the most successful reproducers are ones with high computational capacity which use manipulation (e.g., tools, language, and medicine) to increase their reproductive success, making it hard for fitness to select for the ones that are physically the most robust.

“This model explains a number of very puzzling observations, beyond the fact that the messiest genomes (400+ million years of somatic inheritance) have the most robust anatomies–a striking disconnect between genomic and morphological stability. For example, it predicts the confirmed fact that there are no known mutant planarian strains with abnormal morphologies and explains why the research community has had such a hard time generating transgenic planaria. In this lineage, the ratchet has run all the way forward, optimizing mechanisms to create a functional body (almost) no matter what the genome looks like: all of the effort has gone into polishing a set of algorithms that produce a functional anatomy despite expected noise in the components, which then makes it very difficult to create change by targeting the genetic level.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. pp. 18-19.


“The fact that evolution not only finds solutions to specific problems, but also creates somewhat generic problem-solving machines, with multiple diverse (simultaneously existing) capabilities, has many implications for the evolutionary process itself. It facilitates credit assignment for the evolutionary search process, enables exploration and novelty, and hugely accelerates the process of increasing complexity. This feedback between evolutionary scaling of intelligence and the acceleration of the discovery of novelty by evolution forms a powerful ratchet, which is compatible with the emerging picture of a continuum of basal cognition across the tree of life.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 23.


“Physicists use low-agency, mechanical tools to observe the natural world, and inevitably arrive at low-agency mechanistic models. Taking full advantage of virtual governors, proto-cognitive modules, and other aspects of the software of life requires tools that recognize and learn to hack these capacities. Agency cannot be directly observed with a microscope, but brains, evolutionary processes, and emerging machine learning tools are primed to detect and exploit it via agential models of control because they themselves are higher-agency systems.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 24.


“Recent consilience of a range of disciplines are giving rise to the field of diverse intelligence, which recognizes a spectrum of problem-solving and creative competencies in unconventional, basal media that goes beyond the old dichotomy of ‘dumb mechanical machine vs. high-level true intelligence’.... What is becoming increasingly clear is that intelligence is not some latecomer that arrives with the appearance of big brains–it is baked in at the very beginning, present at multiple scales of the biological substrate of evolution, and continuously shapes its course in a fluid dance that potentiates all participants.” Levin, Michael. 2023. “Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology.” Cellular and Molecular Life Sciences. 80:142. 10.1007/s00018-023-04790-z. p. 24.


“This extremely rich set of feedback loops [of cellular resting potential, GJs [gap junctions], ion channels, pumps, neurotransmitter molecules] establishes computational capacity; for example, ion channels and GJs, as voltage-gated current conductances, are in effect transistors and possess a fundamental property of historicity (memory in which past events impact current signaling state). These events do eventually impact other kinds of pathways (such as gene expression), but it is critical that the information processing in such networks is essentially physiological–the rapid propagation of signals via action potentials and slow waves across the network does not itself require transcriptional change. As a corollary, the information content of this network cannot be read out at the transcriptional or even proteomic level: channels open and close post-translationally, and the same channels can give rise to different voltage states depending on cells’ history, while diverse channels can give rise to the exact same voltage map.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1871.


“There is no one-to-one mapping between the molecular state and the bioelectrical state, making it essential to study such systems in the living condition (unlike genetic and protein-level information, which can be studied in fractionated or fixed material, bioelectrical information disappears at cellular death). One implication of this feature is a critical separation of hardware and software.... One cannot know the informational content of a brain merely from knowing its neural layout and genome: the exact same brain can contain numerous different memories, goals, etc.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1871.


“All cells have ion channels, and most cells couple via regulated gap junctions to their neighbors, enabling the bioelectric physiology that guides growth and form during morphogenesis. Consistent with the evolutionary pivot model [use of the body’s morphological growth by communicating cells to form neural communication for the activity of these morphological components in behavior], these electrical networks also process information to enable navigation: prior to navigating 3D space by controlling muscle action (when brains appeared), this system was used to process information and make decisions, while bodies navigated anatomical morphospace during embryogenesis, regeneration, and cancer suppression. This isomorphism between somatic and neural bioelectricity is what enables all of the tools of neuroscience to be used outside of the brain.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1872.


“The bioelectric system is so versatile that it was readily exapted for behavior when nerve and muscle evolved, with two major changes: a significant speed-up (milliseconds, instead of hours, as the primary time scale) and a focus on temporal signaling (spiking patterns) for behavior instead of development’s reliance on spatial bioelectric patterns across tissues.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1873.


“The robust regulative properties of bodies strongly emphasize the system’s ability to solve novel problems. For example, early mammalian embryos cut in half do not form two half-bodies. Instead, each side recognizes the damage, makes up for it exactly, and creates one of a pair of monozygotic twins. Perhaps even more remarkable is the case of newt kidney tubules. By default, they consist of 8-10 cells in cross section. However, if the cells of the early embryo are artificially made to be larger, fewer cells will be used, resulting in the same (normal) tubule diameter and overall body size. Remarkably, this can be pushed to a fascinating extreme: if the cells are made to be enormous, a single cell will bend around itself, producing the normal size tubule diameter. This example illustrates not only the ability to reach the same anatomical state despite diverse and novel starting conditions with no need for periods of lengthy adaptation, but also the startling ability to call up diverse molecular mechanisms (cell:cell communication in normal conditions, but cytoskeletal bending in the case of huge cells) as needed in the service of a large scale anatomical goal.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1877.


“The on-the-fly competencies of the morphogenetic control system offers evolution the same thing that nervous systems eventually offered: the ability to not over-train on evolutionary priors and instead generate problem-solving machines.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. pp. 1877-1879.


“Bioelectricity is fundamentally a mechanism to scale computation. While bioelectric states do control cell-level properties, such as plasticity, proliferation, differentiation, etc., the real power in this system is in determining large-scale behaviors at the tissue and organ level. It has been shown to control size, organ identity, and whole body axes. A critical (and brain-like) aspect of bioelectrical networks is the hierarchical organization of functionality and the association of complex morphogenetic activity with simple stimuli. Much like the central nervous system (CNS) allows complicated multi-step behaviors to be triggered by a low-information content stimulus, a brief and transient bioelectrical signal can induce whole eyes and appendages in which all the internal details are handled autonomously.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. pp. 1882-3.


“... tumorigenesis has been shown to be controllable by modulation of bioelectric state–normalizing cancer by reconnecting cells to the electrical network that harnesses them toward adaptive tissue homeostasis, a promising alternative to current toxic chemotherapy approaches.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1883.


“The remarkable fundamental mechanism that enables a true emergent collective intelligence–a mind (at whatever scale of sophistication)–is ancient, and is also responsible for the plasticity and robustness of morphogenesis. Evolution re-used some of the same computational strategies, for binding competent signaling subunits into networks with memory and problem-solving capacity, to navigate a diverse set of spaces (physiological, anatomical, behavioral, and linguistic). Bioelectricity offers a tractable and powerful entry-point into understanding this process, because it serves as the cognitive medium of collective intelligence–whether of neurons in the brain, or of cells in a body trying to achieve anatomical outcomes.” Levin, Michael. 2023. “Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind.” Animal Cognition. 26:1865-1891. 10.1007/s10071-023-01780-3. p. 1883.


“In neural networks, cellular excitation can propagate between cells by different mechanisms. During synaptic transmission, one cell influences the activity of others through chemical or electrical synapses. In ephaptic coupling, extracellular currents generated by one neuron directly alter the excitability of adjacent neurons. A third mechanism is volume transmission mediated by diffusible chemical signals linking signal-secreting sender cells to receptor-expressing receiver cells. Both chemical and synaptic transmission can wire complex neuronal networks with specific connections while specificity is more limited in ephaptic coupling.

“Here I propose a detailed hypothesis, the chemical brain hypothesis for nervous system origins. The theory suggests that the first cellular networks involved in sensing, reacting and coordination of tissue-level and whole-body activity were organized by paracrine signalling.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. pp. 1-2.


“Neuropeptides act through cell surface receptors, most commonly G-protein coupled receptors (GPCRs).” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 2.


“The bilaterian common ancestor had at least 30 neuropeptide-receptors systems and these show general conservation across major bilaterian clades with patterns of losses and further clade-specific divergences.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 2.


“The high diversity and cell-type-specific expression of neuropeptides in the non-bilaterian lineages of placozoans, cnidarians and ctenophores also suggests the presence of specific cell-to-cell signalling and complex peptide-wired cellular networks in these organisms.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 2.


“Neuropeptides seem to be among the most specific and most highly-expressed neuronal markers across animals. This suggests that each neuron type has a specific peptidergic fingerprint. Upon activation, this fingerprint reveals the identity of the cell to its neighbours by paracrine signalling. The chemical brain hypothesis states that this and not the language of synapses if the first language proto-neurons used.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 3.


“The chemical brain hypothesis posits that elementary nervous systems first evolved as chemically connected networks of excitable cells.... In chemical nervous systems, there were no synapses yet and cellular patterns (e.g. waves) of excitation propagated by the release of secreted signalling molecules that influenced the activity of target cells expressing specific receptors. Cellular excitation here refers to nonlinear changes in the cell’s ionic or second messenger (e.g. cAMP) content playing out on the millisecond or second timescale. Such excitation can be elicited by both ionotropic and metabotropic receptors and can lead to cellular responses (e.g. contraction). The signalling molecules may have been small molecules (e.g. glutamate, GABA, NO, ATP) and small secreted peptides. Owing to their unlimited potential to diversify, peptides became the most significant paracrine signalling molecules. Peptides signalled environmental or internal states and enabled the coordination of effector activity and physiology in multicellular animal bodies. Paracrine signalling made chemical nervous systems diffusion limited, suggesting that they could only have worked efficiently in small organisms. To overcome the limitations of diffusion, peptidergic cells evolved cellular projections, the precursors to axons, to increase the available surface for secretion. Synapses may have first evolved to link cells expressing the same peptides into neuronal nets allowing coordinated release of peptides through synchronization.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. pp. 3-4.


“Signalling neuropeptide-like molecules feature prominently in the chemical brain hypothesis. Their diversity and phylogenetic ancestry makes them the most likely molecules to have wired chemical networks in early animals. Neuropeptides are highly diverse and are present in all major clades of animals, with the exception of sponges.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 4.


“By analogy with ecology, one can evaluate the success of a class of molecules as one can evaluate the success of a phylogenetic clade: by species richness and per cent cover. According to these measures, neuropeptides are the most successful signalling molecules. They outnumber classical neurotransmitters by at least an order of magnitude in most nervous systems. In terms of cover, neuropeptides collectively also rival classical neurotransmitters as they occur in most if not all neurons, often co-occurring with small transmitters. Even in the mammalian neocortex–the epitome of a synaptically connected structure–almost all neurons express one or more neuropeptides and neuropeptide receptors. Several neuropeptides are also widely expressed in the central nervous system of cephalopods.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 4.


“Why were peptides favoured in evolution over small molecules (e.g. NO, GABA) or globular proteins to wire chemical cellular networks? To address this question, we can compare these different classes of molecules in terms of their cost to the cell, their potential for evolutionary diversification, their diffusibility, stability and other measures.

“In terms of costs to the cell, short peptides are cheaper than long globular proteins.... In terms of diffusivity, small peptides and small molecules are generally more diffusive than globular proteins.... Peptides clearly outperform proteins in their diffusibility, providing an advantage of faster spreading in paracrine signalling.

“Next, we can compare the diversity of potential types evolution has access to within a class of molecules.... This further limits the evolvability of small-molecule signalling pathways. In contrast, peptides have unlimited diversity, with a 5 amino-acid-long form having 205 possible variants, not considering modifications (although solubility and stability will somewhat limit the number of variants). Peptides can also easily diversify through the process of gene duplication and divergence or by intra-precursor divergence. The evolution of receptors can follow, through coevolutionary diversification (duplication of both ligand and receptor, followed by the divergence of specificity), a general process in the evolution of peptide-receptor systems.

“Overall, if one considers synthesis costs, copy number, diffusibility, evolvability and potential diversity, small peptides are the clear winners and evolution did not overlook them.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. pp. 4-5.


“Peptidergic signalling is also slower than synaptic signalling and plays out in the second rather than millisecond timescale. The main, early limitation, however, was probably diffusivity.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 5.


“Total group animals appeared shortly before 571 Ma, as suggested by the fossil record, and inherited the property of cellular excitability from their protist ancestors. Some aspects of advanced nervous systems appeared before eumetazoans, including volumetric signalling and possibly cellular projections involved in signalling. Total group eumetazoans appeared somewhat before 565 Ma with nervous systems combining synaptic transmission, projections and volume transmission appearing in the stem lineage or independently in ctenophores and cnidarians + bilaterians. The first bilaterian trace fossils date to around the same time. With total group bilaterians, neurohaemal organs and centralized brains started to evolve around 558 Ma. This period experienced the great neuropeptide explosion and was followed by the origin of predation and the Cambrian explosion.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 7.


“An infraneuronal system is defined as a necessary but not sufficient character of a structure that we would without doubt consider a nervous system. These infraneuronal systems include (i) cellular excitability, (ii) synaptic cell-to-cell signalling, (iii) cellular projections, and (iv) volumetric cell-to-cell signalling. Out of these four systems, cellular excitability through voltage-gated ion channels, pumps and receptors is the oldest and evolved in single celled organisms. The various combinations of the three other characters define three possible pathways to a full-fledged nervous system....

“The chemical brain hypothesis proposes the early origin of neurosecretion, followed by the later evolution for projections and synapses.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 7.


“The chemical brain hypothesis suggests an alternative path for the origin of synaptic connections. It may be that the first synapses evolved to connect several sensory-neurosecretory cells of the same type into neuronal nets. Synapses with activatory transmitters linking cells of the same type could have enabled synchronous activation, with coordinated pulses or travelling waves of activity. This could have ensured synchronized peptide release across the entire field of cells, contributing–together with the advantages provided by branched projections–to a more robust effector response.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 8.


“Comparative genomics indicates that peptidergic signalling systems have undergone an explosive radiation in stem bilaterians. There are approximately 30 proneuropeptide families and their receptors conserved across major bilaterian clades and most of these originated in the bilaterian stem.... If we look at the distirbution of neuropeptides in bilaterian brains, we always find the highest diversity and concentration in anterior neurosecretory-neurohaemal organs where brain peptides are directly released into the haemo-lymph....

“The final postulate of the chemical brain hypothesis is that the evolution of circulation and neurohaemal organs released the constraints imposed on peptidergic signalling by diffusion. Hemocoelar circulation coupled to the release of peptides at a neurohaemal site ensured the rapid spread of peptides across the body.” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 9.


“Could it be that circulatory systems actually evolved for the transport of neuropeptides and not for the transport and exchange of gases and nutrients? Animals smaller than August Krogh’s critical dimension of approximately 1mm can rely on diffusion and skin breathing alone for respiration. Bilaterians in this size range can already have a haemocoel and active circulation, as found for example in the small interstitial annelid Dimorphilus gyrociliatus. If gas exchange is not diffusion limited in an organism of this size, why does it have circulation? Could the reason be to ensure that signalling peptides reach target cells across the body to coordinate whole-body actions and physiology?” Jekely, Gaspar. 2021. “The chemical brain hypothesis for the origin of nervous systems.” Philosophical Transactions of the Royal Society: B. 376:20190761. 10.1098/rstb.2019.0761. p. 9.


“Nervous systems are traditionally thought of as enabling sensing and behavioral coordination functions at the level of the whole animal. From this perspective, nervous systems make complex morphologies useful.... Here, we review evidence from evolutionary, developmental, and regenerative biology suggesting that nervous systems also function to enable the precise, long-distance coordination of cell proliferation and differentiation that is required to create and maintain a body comprising multiple distinct cell types organized into specialized structures, including organs and limbs. If this hypothesis is correct, nervous systems make complex morphologies possible. The competitive advantages they confer are the competitive advantages of morphological complexity itself.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 17.


“Recent modeling results motivated by the hypothesis that all organisms must minimize Bayesian surprise suggest, however, that multicellular bodies may have evolved independently of motile capability to protect dividing cells from a hostile environment. The primary functions of cell-cell communication in such bodies would have been the suppression by dividing cells of daughter-cell proliferation and the induction of daughter-cell terminal differentiation, functions that elongated cells could perform at greater than typical nearest-neighbor distances....

“If the original, ancestral function of the nervous system is the precise, long-distance (relative to typical nearest neighbor distances) coordination of cell division and differentiation, one would expect the morphology of the nervous system to reflect this function.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 17.


“In general control theory, the Good Regulator theorem states that any effective controller of a system must incorporate a model of that system. One could expect, therefore, the morphology of the nervous system to implement a model of the morphology of the body.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 17.


“The complexity of the morphological model can be expected to increase rapidly as the number of long-distance constraints on the relative sizes and shapes of structures built on different body axes increases. Encephalization, in this case, can be viewed as an adaptive response to the challenge of successful morphogenesis: it centralizes the morphological model and hence centralizes the enforcement of constraints that are dependent on information from distal parts of the body and therefore cannot be enforced using purely locally sourced information.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. pp. 17-8.


“Reconceptualizing the function of nervous systems in terms of morphological coordination renders signaling in nervous systems functionally continuous with these more ancient mechanisms. Nervous systems become, in particular, a novel means of extending both the range and targeting precision of these earlier systems, since they allow information generated in one part of the body to be used to control, with high spatial and temporal resolution, cellular processes in another, distal part of the body.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 18.


“Armed with this concept [that a species has a ‘target morphology’], we can state the morphological coordination problem as the problem of achieving those aspects of the target morphology that cannot be achieved by a combination of cell-autonomous processes, local responses to resource and physical constraints, and common, local rules executed everywhere.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 19.


“Most, if not all, cells are able to change their electrical properties by using the same ion channels and downstream neurotransmitters and calcium as second messengers that neurons use; these are ancient functions that predate specialization of neurons for speed and selective connectivity. Motivated by the network-theoretic considerations above, here we define neurons functionally in terms of specificity and speed: a neuron is a cell that transmits electrical or chemical signals from one or more specific source cells to one or more specific target cells in a time much less than that required for source-to-target diffusion.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. pp. 20-1.


“Overall, this evidence suggests that most signaling factors, be they hormones, morphogens, or neurotransmitters, are widespread across phyla and predate obligate multicellular life, indicating that neurons are not intrinsically required in simple animals for the functions they currently fulfill. Specialized cell types are predated by complex signaling functions, behaviors, and the required tool-kits. Why then do eumetazoa have neurons? As suggested above, geometry, not biochemistry, may be the answer. We hypothesize that, with the advent of multicellular bodies, even if these have a primarily behavioral or protective function, the nervous system developed to transmit some of the previously developed signaling factors over longer than typical nearest-neighbor distances to allow for large-scale coordination of cell proliferation, sensory processing, behavior, morphological development, and differentiation in complex animals.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. pp. 21-2.


“We suggest here that information transfer between different parts of the developing body is essential to the production of the invariant adult morphologies typical of Eumetazoa and that nervous systems are the evolutionary innovation that enables such information transfer at single-cell resolution. We further suggest that the requirements for long-distance, high-resolution information transfer increase with the number of specifically symmetric (e.g., left and right index fingers in humans) or asymmetric (e.g., right thumb versus right big toe) structures. Animal phylogeny can be viewed, in this case, as an elaboration of bodies enabled by an elaboration of nervous systems.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 23.


“Illustrating the diversity of cancer types, other studies have demonstrated an active role for innervation in tumor induction, maintenance, and metastasis, suggesting that tumorigenesis is at least in some cases nerve-dependent in a way analogous to regeneration. A variety of tumor cells are stimulated by neurotransmitters, although dopamine can have a tumor-suppressive effect....

“These results suggest that, if neural activity contributes to the regulation of cell proliferation, as developmental data suggest, this regulation can be overridden by tumor cells.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 26.


“We have suggested, in particular, that the Precambrian development of nervous systems enabled the development of complex, multi-axis animal bodies and hence the Cambrian explosion. Such bodies in turn enable the complex, neurally controlled behaviors typical of animals, including predation, escape, social communication, and active mating behaviors.” Fields, Chris, Johanna Bischof & Michael Levin. 2020. “Morphological Coordination: A Common Ancestral Function Unifying Neural and Non-Neural Signaling.” Physiology 35:16-30. 10.1152/physiol.00027.2019. p. 26.


“Formally, an interaction is a mutual coupling between two dynamical systems. A system is coupled to another when its parameters and constraints depend on the state of the other system. The coupling is mutual if the same situation obtains in both directions. The environment of any given system is defined in dynamical terms as the set of all external variables to which the system is coupled and the sets of all external parameters it influences. Crucially, while the states of coupled systems change during interaction, the sets of variables, parameters, and formal relations do not change.” Di Paolo, Ezequiel A. 2020. “Picturing Organisms and Their Environments: Interaction, Transaction, and Constitution Loops.” Frontiers in Psychology. 11(1912). 10.3389/fpsyg.2020.01912. p. 2.


“We can then define transaction loops as processes of structural coupling whereby an agent’s organization is maintained but structures in the agent and the environment undergo a history of mutually enabled changes.” Di Paolo, Ezequiel A. 2020. “Picturing Organisms and Their Environments: Interaction, Transaction, and Constitution Loops.” Frontiers in Psychology. 11(1912). 10.3389/fpsyg.2020.01912. p. 4.


“We may sometimes be concerned not just with the historical transformation of organism and environment but with their very production, the coemergence of an individual together with its associated milieu. If this is an ongoing process, as enactivists sustain, the continued existence of the organism as an entity must be the result of relations of constitution, i.e., relations by which organisms and environments co-emerge....

“The idea of a self-producing entity that is itself constituted by the way it relates to its medium, though perfectly conceivable in scientific terms, is difficult to picture.” Di Paolo, Ezequiel A. 2020. “Picturing Organisms and Their Environments: Interaction, Transaction, and Constitution Loops.” Frontiers in Psychology. 11(1912). 10.3389/fpsyg.2020.01912. p. 6.


“We may tentatively suggest that one difference between ecological psychology and enaction is that the former focuses more intensively on interaction and transaction loops, and the latter on transaction and constitution loops.” Di Paolo, Ezequiel A. 2020. “Picturing Organisms and Their Environments: Interaction, Transaction, and Constitution Loops.” Frontiers in Psychology. 11(1912). 10.3389/fpsyg.2020.01912. p. 7.


“At the same time, however, despite these conceptual improvements the current debate about different types of NC [niche construction] still faces the same problems that theories of reciprocity could not solve in the early twentieth century: participants in this debate usually (i) do not spell out on what grounds meaningful boundaries between organisms and environments can be maintained and exploited for research purposes, and/or (ii) provide no guidance for how to integrate experiential and physical forms of reciprocal causation [between organism and environment].” Baedke, Jan, Alejandro Fabregas-Tejeda & Guido I. Prieto. 2021. “Unknotting reciprocal causation between organism and environment.” Biology & Philosophy. 36:48. 10.1007/s10539-021-09815-0. p. 9.


“Therefore, a first step towards unknotting organism-environment reciprocal interactions would require distinguishing between the two components. This means that some causal processes occurring in the organism are relatively autonomous from the environment, and vice versa. Thus, in addition to causal pathways connecting organism and environment, we have to incorporate others that start and end within the limits of the organism and within the limits of the environment.” Baedke, Jan, Alejandro Fabregas-Tejeda & Guido I. Prieto. 2021. “Unknotting reciprocal causation between organism and environment.” Biology & Philosophy. 36:48. 10.1007/s10539-021-09815-0. p. 13.


“In our model, the ‘experienced environment,’ variable Ex, represents a mediating interface between organism and physical environment. It constitutes the sum of environmental cues (temperature, pressure, location, etc.) that can causally affect this interface and thus the organism. Ex is meant to convey four basic ideas. First, what is a cue depends on the organism’s sensory system and the way the organism modulates its behavior to choose certain environmental factors. Second, experienced cues are transduced into chemical and cellular processes, and finally lead to metabolic, morphological or behavioral changes. Third, a difference in Ex between two organisms living in the same environment E means that E is experienced differently by each organism. Individual experiences are then directly linked to the ecological performance of these organisms in E, and hence affect their distribution and potentially their evolutionary trajectories. Finally, and most importantly, a change in Ex means a change in the relation of the organism to its physical environment, without alterations of the intrinsic properties of the external environment.” Baedke, Jan, Alejandro Fabregas-Tejeda & Guido I. Prieto. 2021. “Unknotting reciprocal causation between organism and environment.” Biology & Philosophy. 36:48. 10.1007/s10539-021-09815-0. pp. 16-7.


“Our first example is the acceleration of flower production in Solanum melongena (eggplant) as a consequence of active leaf damage by Bombus terrestris bumblebees. When faced with a shortage of pollen, bumblebee workers actively damage the leaves of flowerless plants, which accelerates flower production. In this way, bumblebees increase the local availability of their nutritional resources. A shortage of pollen in the environment of the bumblebees is experienced by them as nutrient scarcity. This modifies the behavior of bumblebees, which start damaging the leaves of eggplants. The damaged plants experience their environment as threatening and thus alter their constitution by allocating resources to the production of flowers.... The availability of flowers, in turn, alters the behavior of bumblebees, which cease damaging the plants and start collecting pollen.” Baedke, Jan, Alejandro Fabregas-Tejeda & Guido I. Prieto. 2021. “Unknotting reciprocal causation between organism and environment.” Biology & Philosophy. 36:48. 10.1007/s10539-021-09815-0. p. 21.


“Recently, we developed the cellular basis of consciousness (CBC) theory of the origin of sentience, identifying several bio-molecular features inherent to all cells. The most important feature for cellular cognition is the limiting membrane of cells, the plasma membrane, which defines the inside (subjectivity) from the outside (environment).” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 1.


“The cellular limiting membrane is maintained actively by cells and cannot form de novo. Instead, cellular membranes require cell division for their existence.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 2.


“Besides deploying extracellular vesicles, ancient cells presumably communicated through tunnelling nanotube (TNT) cell-cell channels that are present in all organisms (in plants they are historically termed plasmodesmata), allowing direct transfer of a variety of molecules and electrical cell-cell couplings. Importantly in this regard, both extracellular vesicles and TNTs act as cellular mediators of immune self-identity. We consider these extracellular vesicles to represent analogous structures to ancient vesicles, which evolved initially into the proto-cells and then into the most ancient archaea and bacteria.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 2.


“The mystery of why the symbiotic evolution of eukaryotic cells took so long (around 2 billion years) may be associated with a need to generate new and uniquely merged self-identity from previously different self-identities of host and guest cells.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 3.


“Whereas cell walls of plant cells precluded cellular mobility in plant bodies, the situation in animals is the opposite.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 3.


“All motile cells of the animal/human immune systems use their actin-myosin cytoskeleton for migration through dense tissues and organs to find and attack the invading cells of pathogens and parasites, as well as to safeguard body integrity after wounds or damage to their cells, tissues and organs.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 4.


“During sexual reproduction, both animals and plants revert back to the evolutionarily ancient unicellular protist-like life style.” Baluska, Frantisek, William B. Miller & Arthur S. Reber. 2022. “Cellular and evolutionary perspectives on organismal cognition: from unicellular to multicellular organisms.” Biological Journal of the Linnean Society. XX:1-12. p. 5.


“Differently put, I am talking about the difficult question: what is a body? This question, not always put in these explicit terms, is the platform on which enactive theory is raised. It is, in my opinion, what differentiates the enactive approach from all other so-called embodied approaches: the thematization of bodies as a prerequisite for understanding anything about minds.” Di Paolo, Ezequiel. 2018. “The Enactive Conception of Life.” In: Newen, Albert, Leon De Bruin & Shaun Gallagher (eds). The Oxford Handbook of 4E Cognition. pp. 71-94. p. 72.


“Let us consider again the two conditions of the definition of autopoiesis (self-production and self-distinction), paying special attention to what they imply with respect to the organism-environment relation....

“There is a primordial tension to this definition of life insofar as the organism-environment relations that best satisfy each of its two conditions tend in exact opposite directions. The tension is well captured by the original split of the autopoiesis definition in two separate conditions. The organism must tend to be self-enclosed to assert its distinctiveness as an individual, but it must also tend to be open to sustain its self-production as a far-from-equilibrium system.” Di Paolo, Ezequiel. 2018. “The Enactive Conception of Life.” In: Newen, Albert, Leon De Bruin & Shaun Gallagher (eds). The Oxford Handbook of 4E Cognition. pp. 71-94. p. 83.


“A real-world autopoietic system would also need to be a dynamically adaptive one, which by necessity would be open to selected environmental flows and closed to others (e.g., those that act against the condition of self-distinction)....

“The overcoming of the primordial tension of autopoiesis takes us closer to the enactive conception of life.” Di Paolo, Ezequiel. 2018. “The Enactive Conception of Life.” In: Newen, Albert, Leon De Bruin & Shaun Gallagher (eds). The Oxford Handbook of 4E Cognition. pp. 71-94. p. 84.


“The picture of mutual co-definition between organisms and environment is even more compelling when we consider life as originating in communities from the very beginning, an issue that we have not discussed here and would deserve a more thorough separate treatment. If environments can be the source of structuring powers, which the organism can to some degree adaptively select to be open or closed to, this is a fortiori the case if we take account of the collective nature of life. Here not only do we find organisms interacting with structuring/ structured flows of active matter and energy available in the inorganic world but with objectified biological and historical products, sedimented practices and acts that play the role of signals, symbiotic relations, and even whole other organisms.” Di Paolo, Ezequiel. 2018. “The Enactive Conception of Life.” In: Newen, Albert, Leon De Bruin & Shaun Gallagher (eds). The Oxford Handbook of 4E Cognition. pp. 71-94. pp. 89-90.


“In brief, ecological psychology characterizes perceiving on the part of the individual as a process of perception-action involving the pickup of information in the environmental surround that is available to the perceiver and that specifies properties of the environment. Enaction theory claims that the perceived environment is realized, comes into being, is ‘enacted’ for an individual by means of an interdependent dynamic network of sensorimotor processes within the boundaries of the organism.... Ecological psychology takes as a core concept ‘information’; whereas central to enaction theory is the concept of ‘sensorimotor processes.’” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 2.


“Following Turvey, sensations are conventionally assumed to have the following characteristics: they are anatomically specific products of sensory receptor stimulation, and as such they are biological correlates of physical energy variables originating in the environment. As biological correlates of receptor stimulation, they are private, occurring ‘in’ the organism. Importantly, owing to their origins in individual receptor functioning, sensations are assumed to be discrete as well as transient.

“In contrast, perceptual experience tends to have the qualities of patterns and ordered or semi-ordered structure rather than discrete bits of sensation. Further, features of perceptual experience typically are ‘felt’ to be located in a public domain beyond the body boundaries – and as such, they are taken to be qualities that, in principle, others can experience as well, rather than being exclusively private.

“The recurring challenge for perceptual theorists has been how to explain this apparent ‘gap’ between properties of sensations, on the one hand, and perceptual experience, on the other. Ecological psychology and enaction theory offer alternative accounts. Enaction theory offers an account of perceiving whereby system processes incorporate sensations into a sensorimotor loop, by means of which perceptual experience of the environment is realized (‘enacted’). Ecological psychology, in contrast, rejects the assumption that sensations play a role in perceiving: instead they are considered to be incidental to perceptual experience. Instead of a sensation-based account, ecological psychology offers an ‘information-based account of perceiving.’ That is, ecological psychology, unlike enaction theory, dispenses with sensations in its account of perceiving. What is directly perceived is the environment. For this reason, the proximal-distal distinction found in most modern accounts of perception collapses.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 3.


“But is the world beyond proximal sources of stimulation even accessible to the perceiver in such an account [of enaction theory]?... But perceptual experience is much more than that. We experience a world that surrounds and extends ‘away’ from us. That is, we have ‘distal’ experiences. The evolution of vision (as well as audition and olfaction) quite likely is due to the functional value of detecting features of the environment at a distance from the perceiver. The language of ‘sensations’ would seem to trap enaction theory within the dynamic system that is the organism....

“To get beyond system boundaries involves, as we saw above, what enaction theorists call sense-making – the enactment of the perceived world. That may be assumed to take the perceiver beyond proximal ‘contact’ with the world; but ultimately the ‘distal’ sources of sensations would seem to be conjured up by some means other than ‘direct’ contact because sensations are inadequate to do the necessary work.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 4.


“Briefly, the environment, or better the habitat, exists separately from an animal’s actions and experience because the histories of each are different. This way of formulating the nature of perceptual experience can be found in William James’ philosophy of radical empiricism. Immediate experience stems from the intersection of processes in the environment and processes of the perceiver. Referring to the immediate experience of a room in which his reader might be located in, William James writes: ‘the experience is a member of diverse processes that can be followed away from it along entirely different lines. One of them is the reader’s personal biography, the other is the history of the house of which the room is a part. [That latter history includes] a lot of previous physical operations, carpentering, papering, furnishing, warming, etc”.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. pp. 7-8. Brackets in the original.


“To account for system individuation, DiPaolo et al. invoke the notion of operational closure, which refers ‘to a network of processes whose activity produces and sustains the very elements that constitute the network’....

“That said, it is obvious to enaction theorists that the system cannot be wholly independent of the surrounding environment.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 11.


“When we shift our level of analysis from the cell to the organism as a whole, and adopt a higher-order psychological focus, we also find some instances of a proximal region of exchange between the organism and the environment, as in the case of tactile perception. But also, and particularly striking, are those commonplace experiences when the region of exchange between the organism and the surround is experienced as being located at places distant from the physical body boundary. In those cases, the body is experienced as being extended distally into the environment.

“To offer two obvious examples: when individuals use a tool, such as a stick to probe a surface or a screwdriver to tighten a screw, they invariably report that the environment is experienced as beginning at the end of the tool – at the surface and at the screw notch, respectively – and that the body is experienced as if it extends to that point. Exchanges with the environment as mediated by tools are typically reported as being felt at some distance from the biological boundary of the body.

“With their roots partially in phenomenological writings, both ecological psychology and enaction theory recognize this phenomenon....

“The study of ecology takes the organism in relation to a system of environmental interdependencies. Enaction theory while recognizing the tight interdependencies within the organismic system seems to underplay the interdependencies of an organism-environment system.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 11.


“In spite of enaction theory allowing for so-called ‘enabling relations’ between the system and factors external to it, ... [it] is hard to imagine how the enaction approach can account for the experience of the extended body other than merely stating that it is enacted, and leaving the matter at that.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 12.


“Holt, a student of William James and one of Gibson’s graduate school mentors proposed that every action of an organism has a quality of ‘adience’ by which he meant the quality of ‘reaching toward’ a source of stimulation – that is, it has a quality of external reference....

“Organismic processes accordingly seem to remain encapsulated within system boundaries. In contrast, Holt offers a relational perspective: ‘The knower is a concrete material body in a concrete material environment and the cognitive relation exists between the two.’” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 12; subquote: Holt, E.B. 1931. Animal Drive and the Learning Process. An Essay Toward Radical Empiricism, Vol I. NY: Henry Holt. p. 51.


“Enaction theory’s focus on ‘system individuation’ gives rise to an emphasis on the organism’s boundary that distinguishes its network of interrelations from those things that lie outside of it.... Ecological psychology adopts as its unit of analysis the organism-environment relation in keeping with the orientation of the ecological sciences. From that perspective, the boundary or region of exchange between the organism and the environment – that is, where the organism ‘ends’ and the environment ‘begins’ – is fluid on functional and psychological grounds.” Heft, Harry. 2020. “Ecological Psychology and Enaction Theory: Divergent Groundings.” Frontiers in Psychology. 11(991):1-13. 10.3389/fpsyg.2020.00991. p. 12.


“In fact, evolutionary processes and simple learning processes are formally equivalent. In particular, learning can be implemented by incrementally adjusting a probability distribution over behaviours (e.g. Bayesian updating) or, if a behaviour is represented by a vector of features or components, by adjusting the probability of using each individual component in proportion to its average reward in past behaviours.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] p. 4.


“For example, in a modular problem, where features in different modules are approximately independent but features in the same module are not, then effective generalisation would be provided by new combinations of modules. Genetically, free recombination would disrupt modules and asexual reproduction would fail to exploit the independence of one module from another. An appropriate compromise is provided by an intermediate level of recombination, such as when nucleotides within genes do not recombine, but genes do.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] p. 5.


“For evolution, learning of this type [connectionist learning] requires separating phenotypes from genotypes and evolving the parameters of a mapping between them.... A minimal example is the evolution of a single ‘relational’ allele, causing subsequent mutations to produce correlated variation in two phenotypic traits (e.g. via pleiotropy). Pavlicev et al. showed that selection on relational alleles increases phenotypic correlation if the traits are selected together and decreases it if they are selected antagonistically (Hebbian learning). This simple step from evolving traits to evolving correlations between traits is crucial; it moves the object of natural selection from fit phenotypes (which ultimately removes phenotypic variability altogether), to the control of phenotypic variability.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] pp. 6-7; reference: Pavlicev, M, J.M. Cheverud & G.P. Wagner. 2011. “Evolution of adaptive phenotypic variation patterns by direct selection for evolvability.” Proceedings of the Royal Society B. 278(1713):1903-1912.


“Each of these areas [evo-devo, evo-eco, and evolutionary transitions in individuality or ‘evo-ego’] is challenging for evolutionary theory because they involve feedbacks where the products of evolution modify the mechanisms of the evolutionary process that created them. Although it is clear that the processes of variation, selection and reproduction underpinning evolutionary adaptation are not constants in natural populations, theoretical treatments of ‘modifier alleles’ that enable selection to act on these processes are currently very limited.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] p. 8.


“... because learned models can generalise, an evolved memory can ... also facilitate faster adaptation to new targets. In short, evolvability is to evolution as generalisation is to learning.

“Whilst generalisation is not always easy, it does not require clairvoyance – it simply requires the ability to find structural regularities that are deep enough to be invariant over time.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] p. 9.


“A different type of learning is relevant here. Unsupervised learning mechanisms do not depend on an external reward signal. By reinforcing correlations that are frequent, regardless of whether they are good, unsupervised correlation learning can produce system-level behaviours without system-level rewards.” Watson, Richard A. & Eors Szathmary. 2015. “How can evolution learn?” Trends in Ecology & Evolution. 31(2):147-157. 10.1016/j.tree.2015.11.009. [Prepublication copy] p. 10.


“A synergy is functional grouping of structural elements (molecules, genes, neurons, muscles, etc) which, together with their supporting metabolic networks, are temporarily constrained to act as a single coherent unit. Just as new states of matter arise when a group of atoms behaves as a single particle (the Bose-Einstein condensate), so new states of biological function emerge when ensembles of different elements cooperate together to form a synergy....

“Atoms and their nuclear components are the elementary constituents of matter. Synergies are the elementary functional units of living things.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. pp. 83-4.


“The hallmark of a synergy is that during the course of ordinary function a perturbation to any part of the synergy is immediately compensated for by remotely linked elements in such a way as to preserve the functional integrity or the goals of the organism. Thus, natural variations (‘errors’) in the synergy’s components are compensated by adjustments (‘covariations’) in other members of the synergy to maintain a given function stable or satisfy a particular task requirement. A further property of synergies is that the relations between interacting components are preserved stably in time despite quantitative variation in measures of components parts.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 85.


“Synergies are reflected in shared patterns of invariance among elemental variables while performance variables vary less than if elemental variables fluctuated independently from trial to trial. This is reminiscent of the eminent developmental biologist Paul Weiss’s criterion for a coordinated system:

Vs << ∑(va + vb + vc + ........ vn)

where Vs, the variance of the system’s collective behavior is significantly less than the sum of the variances of its constituents (a, b, c, ...n). For Weiss, the basic characteristic of a system was its essential invariance above and beyond the much more variant flux of its component elements.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 85.


“As a complex system composed of billions of cells which in turn is capable of displaying a complex repertoire of behaviors the brain is likely to be highly synergized.... To identify synergies in the brain it would be necessary to perturb one member of the synergy (e.g., a piece of cortical tissue known to be engaged for a given task or function) and observe remote compensation by other putatively linked brain areas.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 87.


“Fundamentally, synergies are the unique expression of two mechanisms heretofore conceived of as independent: self-organization and natural selection.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 88.


“It seems reasonable to propose that natural selection latched on to generic mechanisms of self-organization as a means to create functional groupings among multiple elements in specific environments. Synergies cut across organisms and environments forming an informationally coupled dynamic system. Once formed, synergies may then be modified for coordination and control, both of which are crucial to adaptation and survival. A synergy is a naturally selected chunk of self-organized behavior.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 88.


“Several main lines of enquiry are under development. One, the Center Manifold Theorem (CMT) is the essence of the well known ‘slaving principle of synergetics’ proposed by Hermann Haken. The essence of the well-known ‘slaving principle is that near instabilities where complex systems form new patterns, the evolving structure or pattern can be described by one or a few unstable modes, the so-called order parameters. Intuitively, a separation of timescales occurs: all the stable modes have fast timescales and are ‘enslaved’ to the slowly varying order parameter. Through this mathematical mechanism an enormous reduction of degrees of freedom is possible. Another, lesser-known approach is the so-called Uncontrolled Manifold Hpothesis (UCM) developed in the field of motor control by Gregor Schoener and John Scholz. The idea is that a functional task is associated with selecting a performance variable that is stabilized with respect to perturbations. Individual elements of a putative synergy are allowed to change their states as long as they remain within the manifold but not if they leave it. Hence, the individual elements are said to be less controlled within the manifold than outside it. The operational upshot is that the variance in the selected variable is less than the summed variance of the individual components.... A third line of theoretical development, related to and perhaps embracing the previous two is coordination dynamics, a conceptual framework for understanding how the parts and processes of living things work together, i.e., synergize. Coordination dynamics views coordination on all levels in terms of meaningfully coupled self-organizing systems. A major plus of coordination dynamics is that it explicitly takes into account both the intrinsic properties of the coordinating elements and the nonlinear coupling between them. For example, coordination in the brain has been hypothesized to arise as a result of changes in the dynamic balance between the coupling among neural ensembles (mediated, typically by reciprocal pathways) and the expression of each individual neural ensemble’s intrinsic properties (usually heterogeneous in nature).” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. pp. 88-9; reference: Scholz, John & Gregor Schoener. 1999. “The uncontrolled manifold concept: identifying control variables for a functional task.” Experimental Brain Research. 126:289-306.


“Synergies, note, are the proposed relevant units underlying behavior, not the entire behavior itself. They constitute nature’s way of handling information in systems of enormous complexity: synergies ‘crack’ the complex into the simple. Yet, as a kind of grammar, they make complex behavior possible.” Kelso, J.A.S. 2009. “Synergies: Atoms of Brain and Behavior.” In: Progress in Motor Control. Advances in Experimental Medicine & Biology, V. 629. Sternad, D. (ed). pp. 83-91. Springer. p. 89.


“We define an ‘evolving system’ as a collective phenomenon of many interacting components that displays a temporal increase in diversity, distribution, and patterned behavior. The concept of increased complexity is sometimes employed in this context.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 3.


“Consider three examples of evolving systems.

“1. Stellar evolution and nucleosynthesis: Stars begin as gravitationally bound masses of primarily hydrogen and helium in which internal pressures and temperatures are sufficiently high to initiate and sustain nuclear fusion reactions.... In the final, violent stages of stars’ lives, events such as classic novas, supernovas, and neutron star collisions generate the full periodic table of more than 100 elements and their ~2,000 isotopes. Thus, stellar evolution leads to new configurations of countless interacting nuclear particles. Inexorably, the system evolves from a small number of elements and isotopes to the diversity of atomic building blocks we see in the universe today.

“2. Mineral evolution: ‘Mineral evolution’ describes the changing diversity and distribution of minerals that arise during the formation and evolution of terrestrial planets and moons.... These minerals contribute to the dust and gas that form planets–materials that undergo further sequences of condensation, melting, crystallization, differentiation, alteration by temperature and pressure, and fluid-rock interactions. Each new physical, chemical, and (on Earth) biological process has the potential to diversify a planet’s mineral inventory. Thus, on Earth more than 5,900 mineral ‘species’ have been codified with perhaps 3,500 additional species awaiting discovery and description....

“3. Biological evolution: Life is the quintessential evolving system.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 3.


“... we conjecture that these examples [stellar evolution, mineral evolution, biological evolution] (and many others) are conceptually equivalent in three important respects:

“1. each system is formed from numerous interacting units (e.g., nuclear particles, chemical elements, organic molecules, or cells) that result in combinatorially large numbers of possible configurations.

“2. In each of these systems, ongoing processes generate large numbers of different configurations.

“3. Some configurations, by virtue of their stability or other ‘competitive’ advantage, are more likely to persist owing to selection for function.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 3.


“The fact that the rate of evolution varies when the mechanisms for sampling new configurations and/or a system’s selection pressures change highlights the meaningful connection between evolution and context.

“Those three characteristics–component diversity, configurational exploration, and selection–which we conjecture represent conceptual equivalences for all evolving natural systems, may be sufficient to articulate a qualitative law-like statement that is not implicit in the classical laws of physics. In all instances, evolution is a process by which configurations with a greater degree of function are preferentially selected, while nonfunctional configurations are winnowed out. We conclude:

“Systems of many interacting agents display an increase in diversity, distribution, and/or patterned behavior when numerous configurations of the system are subject to selective pressure.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 4.


“Minerals forged at the pressure and temperature conditions of Earth’s mantle can persist on the surface due to kinetic stability. Similarly, organic matter does not spontaneously combust in an oxygen atmosphere due to the high activation energy of combustion. We owe our existence to all of these metastable features of our universe.

“Thus, the most basic selective force stems from the fundamental properties of our universe that allow for static persistence. (Such persistence is, of course, not absolutely indefinite but should extend over periods much greater than other time scales of local changes.) Many structures in nature have been selected for by their stability against decay to equilibrium. We can cast this as a principle of static persistence,’ which we call ‘first-order selection’”

“Configurations of matter tend to persist unless kinetically favorable avenues exist for their incorporation into more stable configurations.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 4.


“Dynamical entities are by necessity open systems. Therefore, unlike statically persistent entities, they are not defined by the persistence of a precise material composition: A star’s elemental abundances change over its lifetime; a hurricane incorporates many different parcels of air over its lifetime; organisms constantly exchange matter with their environment.

“What, then, is persisting? In our view, it is processes, giving rise to what we call ‘second-order selection’:

“Insofar as processes have causal efficacy over the internal state of a system or its external environment, they can be referred to as functions. If a function promotes the system’s persistence, it will be selected for.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. pp. 4-5.


“Let us call dissipation, autocatalysis, homeostasis, and information processing the ‘core functions’. Each of these functions serves to perpetuate itself by enabling further dissipation: Stars achieve homeostasis by balancing gravitational collapse with the kinetic energy generated by fusion, allowing fusion to persist; fire achieves autocatalysis by heating surrounding materials to combustion temperatures, prolonging burning; life achieves information processing through various learning mechanisms, including Darwinian evolution and neurological cognition, which in turn sustains the lineage of information transfer by promoting survival, propagation, and continued metabolic activity.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 5.


“In nested complex systems, ancillary functions may arise. For example, enzymes are selected for their ability to catalyze a specific reaction, which may be one chemical transformation in an elaborate autocatalytic network, a homeostatic feedback system, or an information-processing apparatus. In other words, an enzyme’s function is not to perform any of the core functions alone, but to play a specific role in the context of a core function expressed at a higher level of organization. From the perspective of the enzyme, there is a top-down selection pressure for enzymes to have high catalytic efficiencies due to a selection pressure at a higher level for a lineage of organisms to persist. In other words, the enzyme’s function is informed by its context within a larger system.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 5.


“The ability to continually create (or discover?) new functions is a hallmark of life. Although some of these functions may seem neutral or even detrimental with regard to the stability of the whole system, overall the generation of novelty has the potential to further intertwine the core functions within a nest of feedback loops that supplement their stability and/or amplify their effectiveness. As a simplistic example, the invention of flight allowed animals new vectors by which to continue performing their core functions, making multiple lineages of organisms more successful at surviving and reproducing.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 5.


“Another key feature of ancillary functions in biology is exaptation–a change in function over time. Returning to the example of flight, it has been suggested that insect wings initially served thermoregulatory purposes, and feathers may have performed thermoregulatory, display, and biomechanical support functions before aiding in flight.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 5.


“Adding new functions that promote the persistence of the core functions essentially raises a dynamic system’s ‘kinetic barrier’ against decay toward equilibrium. Moreover, a system that can explore new portions of phase space may be able to access new sources of free energy that will help maintain the system out of equilibrium or move it even further from equilibrium. In general, in a universe that supports a vast possibility space of combinatorial richness, the discovery of new functional configurations is selected for when there are considerable numbers of functional configurations that have not yet been subjected to selection. Hence, we identify a ‘third-order selection’ for novelty:

“There exist selection pressures favoring systems that can open-endedly invent new functions–i.e., selection pressures for novelty generation.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. pp. 5, 6.


“We anticipate a biological paradigm shift analogous to the leap between classical mechanics and quantum mechanics: just as we replaced localized individual particles and discrete electron orbitals with wavefunctions and electron clouds, we may one day replace biological individuals with a ‘fuzzier,’ networked picture of life.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 6.


“Configurations that are themselves statically persistent and promote dynamically persistent systems will be selected for.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 6.


“Functional information quantifies the state of a system that can adopt numerous different configurations in terms of the information necessary to achieve a specified ‘degree of function,’ where ‘function’ may be as general as stability relative to other states or as specific as the efficiency of a particular enzymatic reaction.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 6.


“A significant limitation of the functional information formalism is the difficulty in calculating I(Ex) for most systems of interest. Functional information is a context-dependent statistical property of a system of many different agent configurations: I(Ex) only has meaning with respect to each specific function....

“The law of increasing functional information. The functional information formalism points to an important universal characteristic of evolving systems:

“The functional information of a system will increase (i.e., the system will evolve) if many different configurations of the system are subjected to selection for one or more functions.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 7.


“Why is this so? Let us explicitly address the temporal nature of functional information. Two things can cause the functional information of a system to increase over time: (a) The possibility space expands; or (b) the degree of function (i.e., the selection pressure) increases. Thus, a law of increasing functional information must not only rely upon 1) the existence of selection, but also 2) changes to the possibility space, and/or 3) changes in the selection pressure(s).” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 7.


“The GOE [great oxidation event] amplified all three drivers of complex evolving systems: 1) Abundant atmospheric O2 provided an extra source of component diversity; 2) O2 provided a new source of free energy to drive combinatorial exploration; and 3) as a highly reactive oxidant, O2 also provided a new set of selective criteria for persistence. Hence, the GOE is paradigmatic of how the Earth’s genesity–its ability to drive the evolution of complex systems–and its functional information have increased over planetary history.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 8.


“The rate of evolution of some systems can be influenced artificially: The functional information formalism suggests that the rate of evolution in a system might be increased in at least three ways: 1) by increasing the number and/or diversity of interacting agents, 2) by increasing the number of different configurations of the system; and/or 3) by enhancing the selective pressure on the system (for example, in chemical systems by more frequent cycles of heating/cooling or wetting/drying).” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 9.


“Evolving systems are overlapping and interdependent: The examples of nucleosynthesis, minerals, and biology are but three examples of the deep connections among evolving systems. Minerals could not have formed without prior nucleosynthesis, while life (by most accounts) could not have emerged without minerals. Similarly, numerous evolving technological and symbolic systems had to await the evolution of human society.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 9.


“A more deeply rooted factor in the absence of a law of evolution may be the reluctance of scientists to consider ‘function’ and ‘context’ in their formulations. A metric of information that is based on functionality suggests that considerations of the context of a system alters the outcome of a calculation, and that this context results in a preference for configurations with greater degrees of function.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 10.


“We conjecture that selection based on static persistence, dynamic persistence, and novelty generation is a universal process that results in systems with increased functional information.” Wong, Michael L., Carol E. Cleland, Daniel Arend Jr., Stuart Bartlett, H. James Cleaves II, Heather Demarest, Anirudh Prabhu, Jonathan I. Lunine & Robert M. Hazen. 2023. “On the roles of function and selection in evolving systems.” PNAS. 120(43):e2310223120. p. 10.


“The fundamental idea behind constructing an assembly theory model is that you define an ‘assembly universe,’ which consists of a finite set of distinct basic building blocks and another finite set of rules that allow you to assemble them into more complex composite objects....

“Now you introduce a dimension of time to the model, which is implemented by recursivity. In other words, at each step of the assembly process, you can use all objects that are already assembled for further assembly. Thus, at each step, you get a bigger choice of objects to build with....

“Recursivity makes the dynamics of the model historically contingent.” Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do.” Journal of Molecular Evolution. 92:87-92. 10.1007/s00239-024-10163-2. p. 87.


“... you would generally expect many different complex composites to be present at very low abundance at later steps. Yet, if you find certain complex composites enriched, especially early on, that’s a sign that things are not just based on the random interplay of the basic rules in your system....

“Put simply: finding composites with high complexity at high abundance means the basic rules of your ‘world’ have probably been skewed in some way that is not built into the basic rules. That’s what the authors mean by ‘selection’.” Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do.” Journal of Molecular Evolution. 92:87-92. 10.1007/s00239-024-10163-2. p. 89.


“In other words, assembly theory is a tool to detect the emergence of new levels of organization and their causal influence on lower-level phenomena in the world you are observing. If the outcome you are detecting is biased, the underlying rules must have been constrained or channeled in some way to generate that bias. Philosophers call this ‘downward causation,’ and keep on arguing about it.” Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do.” Journal of Molecular Evolution. 92:87-92. 10.1007/s00239-024-10163-2. p. 90.


“Suffice it to say that the problem [downward causation] goes away if you consider that processes and relations (i.e., the rules that are affecting your objects) are fundamental, and not only the objects with their intrinsic properties themselves.

“All you need to know about downward causation in this context is that it does not change the underlying rules. Instead, it constrains and channels the underlying processes in unexpected ways. And, if you think about it, that’s exactly what evolution does with the laws of physics: natural selection never alters the rules of physics and chemistry underlying the processes that compose your body, but constrains and channels the direction of these processes in ways that you fundamentally cannot predict from the underlying physics or chemistry alone.” Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do.” Journal of Molecular Evolution. 92:87-92. 10.1007/s00239-024-10163-2. p. 90.


“To say it again: assembly theory cannot tell us whether some bias is due to natural selection or not. It only tells us whether the bias is there, and how much of it is there, given the basic assumptions underlying the rule-based world we are modeling with assembly theory....

“What assembly theory really does is to detect and quantify bias caused by higher-level constraints in some well-defined rule-based worlds. That’s it! Even if Darwinian selection may contribute to such bias, assembly theory cannot tell you if it does, how much it does, or if other factors play a role as well.” Jaeger, Johannes. 2024. “Assembly Theory: What It Does and What It Does Not Do.” Journal of Molecular Evolution. 92:87-92. 10.1007/s00239-024-10163-2. p. 91.


“Evo-devo–implications for modifying variability, and the evolution of long-term evolvability: Can development be organised to facilitate future adaptation?...

“Evo-eco–implications for modifying the selective context, and the evolution of ecosystem organisation: Can an ecosystem be organised ‘for’ anything if it is not an evolutionary unit?...

“Evo-ego–implications for modifying heritability, and the evolution of new evolutionary units: Can evolution at one level of organisation favour the creation of heritable evolutionary units that are adaptive at a higher-level of organisation” That is, can the evolution of reproductive organisations find new heritable units that are suitable for responding to selection at the higher level of organisation before that level of organisation exists?...

“In each case, the conventional answer seems to be–it cannot. It is not possible for evolution by natural selection to produce adaptations for an environment it has not yet encountered, to produce organisation at the system level without selection at the system level, or to create new units that are adaptive for a level of selection that does not yet exist.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. pp. 556, 557.


“Whereas a simple processes [sic] of optimisation (or incremental improvement) is usually applied to a solution or output directly, a learning process [a learning system that does not have goal-directed intentions] optimises a model of good solutions or outputs or an indirect representation of solutions. Evolutionarily, this is like the difference between adapting the parameters of a phenotype directly vs adapting the parameters of a developmental process that produces fit phenotypes (this is a distinction which is lost when we assume a one-to-one mapping between genotype and phenotype).” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 557.


“In advanced learning methods a model can be complicated and mechanisms for approximating the parameters of the model from observations can be quite sophisticated. But quite often the model can be simple; for example, a correlation model is a representation of how features in good solutions ‘go together’ or correlate. In practise, this often means making connections of some sort between different elements of a solution (causing their usage in solutions to become correlated). Correlation learning can be implemented via a very simple learning principle that adjusts the connections of such an organisation incrementally.... Rather than merely finding good outputs, a learning process finds the structure underlying good outputs.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 558.


“Developmental, ecological and reproductive organisations are structures that determine which things ‘go together’ and which things are independent. Specifically, the organisation of developmental interactions governs whether it is possible for multiple coordinated changes to occur in a way that preserves their functional dependencies without causing multiple unwanted side-effects on other aspects of the phenotype. Ecological interactions specify how a change in the density of one species modifies the selective pressures acting on other species and thus govern which species are mutually exclusive and which can coexist, for example. Reproductive organisations govern whether fitness differences among the components within evolutionary units are suppressed and whether fitness differences between different evolutionary units can be inherited. Understanding the evolution of developmental, ecological and reproductive organisations thus requires that we understand how evolution alters which things vary together, which things are selected together and which things are inherited together, respectively.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 558.


“When the structure of a network affects the dynamics that occur on it, and the dynamics that occur on the network affect changes to network structure, this is known as an adaptive network, e.g. where agents on a network can both choose behavioural strategies that are suitable for the current organisation, and can also choose to re-wire connections on the network to suit the current behaviours. We argue that developmental, ecological and reproductive organisations exhibit this two-way property.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 559.


“More specifically, we investigate the hypothesis that the evolution of developmental, ecological and reproductive organisations are all subject to the same underlying organisational principle, a simple principle of positive feedback between the topology of an interaction network and the behaviours that the network structure governs. That is, a connection between two components or nodes in this network causes them to exhibit correlated behaviour, and when nodes have correlated behaviours this causes natural selection to create or strengthen the connection between them. In short, entities that co-occur together ‘wire’ together (and entities that wire together co-occur together). For example, genes that are selected together are wired together via the evolution of gene-regulatory interactions that cause them to co-vary (be co-expressed) in future, species that co-occur in high-density are wired together by the evolution of ecological relationships that cause them to be co-selected in future, and evolutionary units that reproduce together are wired together by changes to reproductive relationships that cause them to be co-inherited in future.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 559.


“Evo-eco: The more often that two species populations grow to high-density together (at the same time/in the same environment) the more selective advantage there is for individual traits that strengthen ecological interactions between them. These interactions change in a way that causes their population growth to be more correlated in future, e.g. via reductions to competitive interactions between them.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 559.


“Connectionism is an approach to cognitive modelling that attempts to explain the cleverness of cognitive processes not by ascribing sophistication to the individual components parts (such as individual neurons) but to the organisation of the connections between them.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 560.


“A positive connection produces positive correlation in the activation of the nodes it connects; conversely a negative connection means that when one is activated the activation of the other is suppressed. Adjusting connections in this manner is therefore a type of correlation learning. This type of learning is just a way of implementing the very general idea of associative learning which has influenced cognitive modeling for centuries, i.e. learning which objects or ideas go together, or learning which stimuli go together with which outcomes, or behaviours with rewards.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 560.


“Under Hebbian learning, the direction of change in the connection is determined by the current output of the system (e.g. whether the two neurons are currently firing) not by a task-specific performance metric. This type of unsupervised learning mechanism is equivalent to reinforcement learning that favours amplification of the current outputs regardless of what they are (i.e. their signs). Because this amplification is enacted through changes to connections rather than independent variables, it has the consequence of reinforcing combinations of values in the current output. This causes those combinations of outputs to become more stable and resilient to perturbation. That is, if one or a small number of the system variables are changed, the weighted connections from other variables that have not changed will force it to change back (or will reduce the external input necessary to change it back). For a given distribution of initial conditions, this means that that particular combination of values is more likely to re-occur (in dynamical systems terms, the initial conditions that lead to a particular attractor state is the ‘basin of attraction’ for that pattern, and the effect of this type of learning is to increase the size of this attractor basin, i.e. to increase the number of initial conditions that lead to that attractor). Thus, whereas reinforcement learning strengthens correlations that are good (making changes that improve rewards and make good combinations of outputs more likely to occur in future), unsupervised learning merely strengthens correlations that are frequent (making changes that amplify or stabilise the current output and make those combinations of outputs more likely to occur again in future).” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 561.


“We introduce the term ‘evolutionary connectionism’ to recognise that, by processes that are functionally equivalent to connectionist models of memory and learning, natural selection acting on the relationships within and between evolutionary entitites can result in organisations that produce complex system-level behaviours in evolutionary systems and improve the adaptive capabilities of natural selection over time. The basis of evolutionary connectionism is that the simple principle of positive feedback on the organisation of a system, well-understood in the context of neural network models, is also common to the evolution of developmental, ecological and reproductive organisations.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. pp. 561-2.


“Specifically, whereas developmental organisations bias phenotypic variability (by recreating specific phenotypic patterns through the organisation of internal selection or context-sensitive differential growth between components), reproductive organisations can bias genetic variability (by enabling the combination of genetic differences in a collective to be inherited to descendent collectives as a unit and suppressing internal differential selection between them).” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 566.


“The three organisations are complementary in the level of evolutionary unit they address: evolution of a network as a single evolutionary unit (evo-devo), evolution of multiple evolutionary units within a network (evo-eco), and evolution that changes the evolutionary units (subsets of nodes become single nodes) (evo-ego).” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 567.


“These differences [MTEs by fraternal or egalitarian transitions] suggest that these types of transition do not share common mechanisms or motives. However, both types of transition, when considered more fully, involve both a change in the level of the evolutionary unit (from particles to collectives) and the origination of heterogeneous functional roles, but in different orders. In egalitarian transitions, evolutionary entities differentiate functional roles first (e.g. via speciation) and then form a new evolutionary unit, whereas in fraternal transitions, entities change the scale of the evolutionary unit first and then differentiate in their functional roles.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. pp. 569-70.


“Consider an example where particles attain fitness benefits by having phenotypes that are coordinated with the phenotypes of others (complementary roles), rather than benefits that arise from their intrinsic individual characteristics. For example, suppose that a proto-multicellular organism must be both motile (in order to gather resources to survive) and fecund, and that individual cells cannot be in the motile state and in the reproductive state simultaneously. Both roles might be provided initially in a single-celled organism via phenotypic plasticity and a lifecycle that moves reversibly between one state and the other. But if two cells work together to allow specialisation in these roles, there are efficiencies to be gained in, for example, the time and energy required to switch between phenotypic states. Clearly, the fitness of an immotile reproductive cell or a non-reproductive motile cell alone may be zero. But the fitness of cells that belong to a collective (of two) with complementary roles is non-zero....

“Thus when particles have diverse, potentially synergistic, functional roles, good coordination between them can create fitness benefits at the collective level that cannot be accounted for by phenotypes that confer fitness differences at the individual level. It is thus the coordination ability itself that is both fit for the particle and fit for the collective, whereas neither of the two cell-phenotypes are individually fit. Accordingly, the ability to coordinate particle phenotypes with one another is not just a useful ‘add-on’ in evolutionary transitions, but actually essential in creating fitness differences that belong to the collective and not to the lower level of biological organisation. Accordingly, the evolution of individual particle phenotypes is inadequate to explain collective-level adaptations, and it is in exactly this case where a connectionist approach, i.e. addressing the evolution of relationships that coordinate particle phenotypes, comes into its own....

“In hindsight, we were missing a trick; this work overlooked the value of unsupervised correlation learning in reducing the dimensionality of the search space before new units are created. More recent work rectifies this by exploiting principles of unsupervised learning introduced into the new approaches to deep learning. This uses individual-based simulations where, as before, individuals have traits that define symbiotic partnerships controlling who they co-disperse with during reproduction, thus creating new heritable units. But this is now combined with ecological dynamics (particle level selection) such that selection for new evolutionary units occurs mostly at local ecological equilibria. Under these conditions, new evolutionary units that join two species together must be at least as good as the combinations of species that already co-occur under individual selection at ecological equilibria–otherwise individuals that are not partnered will be fitter. By occasionally perturbing the ecological dynamics, we cause the system to visit many different ecological equilibria, and under these conditions, only partnerships that are robust over the distribution of ecological equilibria visited will survive selection. Accordingly, the partnerships that are favoured by selection are those that evolve to canalise the combinations of species that already co-occur most frequently under particle-level selection. This implements the unsupervised correlation learning principle, i.e. evolutionary units that reproduce together (at the same ecological equilibria) become ‘wired’ together into new evolutionary units.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. pp. 571, 574.


“Social evolution theory explains the evolution of cooperation by showing that strategy assortment makes cooperators fitter than defectors even when the reverse is true in a well-mixed population.... ... instead of concluding that cooperation prevails because genotypes are positively assorted, we ask why population structures that provide such genetic assortment evolved–and in particular, whether these structures evolved precisely because they enabled greater cooperation.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. pp. 572, 573.


“Our work illustrates several different but complementary processes by which the Darwinian Machine changes as a result of its own products. The evolution of developmental networks modifies the distribution of phenotypic variants that selection can act on, and the evolution of ecological networks modifies the selection acting on those variants.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 575.


“We have introduced the term evolutionary connectionism to recognise that, in the same way [as connectionism among neurons], evolutionary innovation need not originate from the adaptation of the evolutionary parts per se but from the evolution of the relationships between them. We have argued that this is much more than a superficial analogy between learning and evolution. Specific, but simple, organisational principles are common to correlation learning systems and the evolution of organisations.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 576.


“... the transfer of learning theory to evo-devo suggests that the evolution of evolvability will be sensitive to the match between the deep structural regularities of the environment and the intrinsic inductive biases of developmental processes (i.e. the kind of regularities that are easy to ‘learn’ in that ‘model space’), and to the costs and benefits of ‘overfitting’ the environment. The transfer of learning theory to evo-eco suggests that the evolution of community organisation will be sensitive to the presence of ecological constraints that cause species to coevolve dependencies with one another rather than simply evolve toward independence. And the transfer of learning theory to evo-ego suggests that the evolution of new evolutionary units will exhibit limitations analogous to those of deep learning.” Watson, Richard A., Rob Mills, C.L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios & Daniel Power. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evol. Biol. 43:553-581. 10.1007/s11692-015-9358-z. p. 577.


“Dynasties and rulers have come and gone, and in the Chinese way of thinking they will come and go for millennia to come. As Henry Kissinger has noted, ‘China’s sense of time beats to a different rhythm from America’s. When an American is asked to date a historical event, he refers to a specific day on the calendar; when a Chinese describes an event, he places it within a dynasty. And of the fourteen imperial dynasties, ten have each lasted longer than the entire history of the United States.’” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 31.


“... the Warring States period did not involve great military outlays. Nonviolent competition for several decades constituted the main form of struggle. A famous strategy was to deplete an adversary’s financial resources by tricking it into spending too much on its military. Two thousand years later, when the Soviet Union collapsed, the Chinese interpretation was that the Americans had intentionally bankrupted Moscow by tricking it into spending excessively on defense.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 41.


“At the heart of Chinese strategy is shi, which is a difficult concept to explain to a Western audience. It cannot be directly translated into English, but Chinese linguists describe it as ‘the alignment of forces’ or ‘propensity of things to happen,’which only a skilled strategist can exploit to ensure victory over a superior force.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 42.


“On a routine visit in the 1990's to the CIA translation center in Reston, Virginia, I asked a translator why so few examples of Chinese leaders’ anti-American tirades appeared in its reports....

“‘That’s easy,’ she replied. ‘I have instructions not to translate nationalistic stuff.’

“I was puzzled by this. ‘Why?’ I asked her.

“‘The China division at headquarters told me it would just inflame both the conservatives and left-wing human rights advocates here in Washington and hurt relations with China.’” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. pp. 96-7.


“Her [a Chinese defector] main point seemed inconceivable: China’s leaders devoted tremendous time and energy to controlling the message inside China in a way that would directly influence foreign perceptions of China. The U.S. government uses diplomacy and strategic communications to put its best foot forward. But imagine trying to control every U.S. media outlet–every local newspaper, every TV station, every blogger–all in a way designed to influence foreign perceptions of America. It would be immoral and–at least in the American context–illegal and impossible. The White House staff and pollsters who advise the president cannot just order the New York Times and the Associated Press what to print.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 119.


“Rather than attempting to replicate the U.S.-style system of power projection, as the Soviets did, China has elected not to do so because it would be a violation of the rules of the Warring States-era lessons for China to provoke the hegemon, or ba, prematurely. Chinese leaders have studied how the United States had become alarmed at the Soviet Union’s military buildup, and how this buildup supposedly provoked the Americans to end wartime cooperation with Stalin and initiate the Cold War and a massive U.S. trade and investment embargo on the Soviet Union. Beijing has vowed not to follow Moscow’s example in this regard. To do so would spell the end of the Marathon.

“Rather than enhancing its power projection capabilities to compete with the United States, China as made little or no investment in various means of power projection, such as long-range bombers, massive ground forces, and nuclear-armed ICBMs. Indeed, China has actually made significant reductions across its power-projection capabilities. Chinese military spending on advanced weapons has increased dramatically over the past decade.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 140.


“Another key difference I discovered when reviewing People’s Liberation Army materials and documents was that China is prepared to use what it calls a ‘warming strike’ that would increase shi and tilt the flow of events in China’s favor. Da ji zeng shi, a term that appears in Chinese military texts and is discussed among military insiders, means ‘strike with force to increase shi.’ While China has historically not used force for territorial conquest, it has instead done so for political motives of a different sort: to achieve psychological shock, reverse a crisis situation, or establish a fait accompli. As in the surprise intervention against U.S. and UN forces in Korea in 1950 and in surprise offensives against its neighbors India (in 1962), the Soviet Union (in 1969), and Vietnam (in 1979), Chinese military leaders believe that the preemptive surprise attack can mean the difference in determining the outcome of a military confrontation and can set the terms for a broader political debate (such as a territorial dispute)....

“Though it is rarely uttered publicly, there is a consensus among most U.S. policymakers and defense experts who deal with China that the deep-seated suspicion among Chinese leaders could lead to a war that neither side wants. Susan Shirk, who served as deputy assistant secretary of state for East Asian and Pacific affairs from 1997 through 2000, has warned that ‘we face the very real possibility of unavoidable conflict with rising China,’ given that ‘the more developed and prosperous the country becomes, the more insecure and threatened they feel.’” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. pp. 146-7.


“To execute a warning strike, the People’s Liberation Army needs the shashoujian. Chinese officials are highly reluctant to talk with Americans about their military’s exploration of Assassin’s Maces. When I asked a senior Chinese military strategist about it, he told me that the term absolutely could not be discussed. However, after seeing references to Assassin’s Mace weaponry in three military books and more than twenty articles by modern military strategists in China, I was able to piece together a portrait of the arsenal the Chinese are discussing–and building.

“The Assassin’s Mace weapons are far less expensive than the weapons they destroy. They are developed in as much secrecy as possible. They are to be used at a decisive moment in a way, before the enemy has had time to prepare. Their effect on an adversary is confusion, shock, awe, and a feeling of being overwhelmed. As the Department of Defense wrote in its 2002 report to Congress on China’s military capabilities, China’s strategy emphasizes ‘operations that will paralyze the high-tech enemy’s ability to conduct its campaign, including operations to disrupt and delay the enemy campaign at its inception and operations that are highly focused on identifying the types and locations of enemy high-tech weapons that pose the greatest threat.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 147.


“[Quote from People’s Liberation Army newspaper:] “In their [US’s] own words, a highly computerized open society like the United States is extremely vulnerable to electronic attacks from all sides. This is because the U.S. economy, from banks to telephone systems and from power plants to iron and steel works, relies entirely on computer networks.... When a country grows increasingly powerful economically and technologically ... it will become increasingly dependent on modern information systems .... The United States is more vulnerable to attacks than any other country in the world.” Pillsbury, Michael. 2015. The Hundred-Year Marathon: China’s Secret Strategy to Replace America as the Global Superpower. NY: Henry Holt and Co. p. 151.


“Complex systems are often represented by complex networks, defined as networks that are neither deterministically ordered (simple networks) nor completely random (random graphs). Instead, complex networks manifest small patterns of specific interactions, called network motifs, that occur at frequencies higher than those in randomized networks. In the context of information-processing systems, the fundamental utility of the network motifs formulation is that individual network motifs perform specific information-processing functions, thus acting as functional building blocks or computational primitives of complex biological networks. Functions in complex systems emerge through dynamic interactions among several types of well-defined network motifs, each characterized by signature function and dynamics, together yielding a function-based modular representation of complex networks.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. p. 506.


“A single neuron is thus an intricate and complex network constructed from several heterogeneous compartments, each endowed with disparate network motifs that drive cellular function. It is therefore not surprising that a single neuron is endowed with complex functional capabilities that can be modeled as a network. The array of network motifs and the specific set of interactions among them depend on the specific neuronal subtype. Even within individual neuronal subtypes, there is widespread neuron-to-neuron heterogeneity in motifs, their structural and molecular composition, and interactions among them.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. p. 513.


“Network motifs in cellular neurophysiology are not limited to the manifestation of the characteristic functional properties of individual neurons but are prevalent across all aspects of neuronal plasticity. The fundamental requirements for individual neurons to change arise from the need to accomplish adaptation (learning) targets and to maintain homeostatic balance. In addition, there are perturbations (e.g., stochastic, pathological) to neuronal function which could trigger plasticity in cellular variables.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. p. 513.


“The balance between plasticity and stability is fundamental to all learning systems. A tilt towards the homeostatic side of the balance hampers adaptation goals, whereas a tilt in favor of plasticity could trigger pathological changes to neuronal physiology.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. p. 513.


“Plasticity processes in neurons are widespread and involve adaptation of synaptic, morphological, and intrinsic properties. Importantly, however, the prevalence of plasticity does not imply that plasticity occurs in arbitrary fashion. We argue that there are well-defined network motifs that impose clear constraints on the ensemble of components that undergo plasticity and the direction of change in each component. Neural plasticity associated with theta burst pairing (TBP) in hippocampal neurons is an elegant example of the intricate constraints placed on components that change together. TBP increases calcium, which connects to several molecular nodes (enzymes) through edges, and these together induce long-term plasticity (up- or downregulation) of specific ion channels, which themselves mediate positive or negative feedback motifs with membrane voltage. There is specific structure to the set of nodes and edges that are present, together yielding a well-defined and constricted plasticity space spanned by TBP activity. Such intricate plasticity motifs that implement structured plasticity manifolds are associated with different plasticity paradigms across several neuronal subtypes.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. p. 515.


“A key feature of complex systems is the manifestation of degeneracy – the ability of disparate combinations of subsystems to yield similar functional outcomes. The network motifs perspective unveils the manifestation of a cascade of different forms of degeneracy associated with the physiology and plasticity of single neurons. Specifically, a given network motif could be implemented by different sets of molecular nodes, referred here as component degeneracy. A compartmentalized cellular function can be implemented by same sets of network motifs with disparate edge strengths, referred to as edge degeneracy, or by disparate combinations of different network motifs, defined as motif degeneracy. Finally, overall neuronal physiology and plasticity emerge through disparate combinations of different network motifs, each implemented by disparate sets of components. Degeneracy in the manifestation of characteristic physiological properties, in physiological properties across the dendritic arbor, in the emergence of plasticity profiles, and in encoding characteristics are well-established across several cell types.” Mittal, Divyansh & Rishikesh Narayanan. 2024. “Network motifs in cellular neurophysiology.” Trends in Neurosciences. 47(7):506-521. 10.1016/j.tins.2024.04.008. pp. 516-7.


“The first interpretation [of a functional understanding of the development of early nervous systems] sees nervous systems primarily as connecting devices that link sensors to effectors. In this case, the simplest nervous system that is conceptually possible consists of a single neuron that connects a sensor to an effector. This interpretation that will be referred to as the input-output view is closely linked to the standard interpretation for modern nervous systems. A contrasting interpretation, the coordination view, sees early nervous systems primarily as coordination devices enabling motility by increasingly large multicellular organisms. Conceptually, the simplest nervous system possible here would be a diffuse nerve net consisting of a significant number of neurons spread out over a large portion of the animal’s body, such as can nowadays be seen in Hydra.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 312.


“The present paper will develop a particular coordination interpretation in some detail. The view developed here consists of the skin brain thesis or SBT.... Thus the specific aim of the present paper will be to sketch how the SBT deals with sensing as well as motility.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. pp. 312, 313.


“More specifically, the SBT and the ASMO [animal sensorimotor organization] notion are closely related to embodied approaches to cognition as well as sensorimotor approaches to cognition and consciousness.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 313.


“Nevertheless, contractile tissue is definitely the most typical, powerful and dominant source of animal motility. Without it the large motile animals of the Cambrian and onward could not exist as motility by cilia comes with strong size and efficiency constraints that are only overcome by contractile tissue. Muscle forms the animal’s ‘prime mover’ and is the key feature of the standard sensorimotor organization of modern animals, constituting around 40% of human body weight. The rise of muscle-based motility is unquestionably a key transition in animal evolution. The SBT proposes that contraction-based motility goes back to the very origins of nervous systems and that both are intrinsically related.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. pp. 318-9.


“At a neuronal level, the SBT differentiates between two essential properties of modern neurons: (a) Neurons have synapses that enable electrical signaling to other cells through the release of neurotransmitters. (b) Neurons have axodendritic processs that enable them to send and receive these signals to and from specific targets cells across long distances. While these two properties are combined in modern neurons, they may have evolved independently and at different times. As discussed above, the mechanisms for synaptic signaling, such as various parts of the postsynaptic scaffold, are already present in unicellular organisms and can be said to predate the axodendritic processes that require a multicellular context. The question addressed is how protoneurons–defined as cells having (a) but not (b)–capable of synaptic signaling to neighboring cells could have been functional in a way that scaffolded the evolution of full neurons with axodendritic processes, that enabled signaling to non-neighboring cells.

“The SBT conjectures that the evolution of the first nervous systems took place in two different phases. The first phase involved the evolution of excitable myoepithelia. These are epithelia that have both contractile properties and conduct electrical activity across their surface. Such epithelia are both coordinator and effector at the same time. In contrast to modern forms of excitable myoepithelia, the SBT suggests that these early forms were constituted by protoneurons capable of chemical transmission through exocytosis, similar in outline to signaling by chemical synapses.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 319.


“In contrast [to neural signals that ‘initiate and control motility’], skin brains–taken here to include both myoepithelia and diffuse nerve nets–are conceptualized as physically intertwined with a contractile surface [or ‘skin’] instead of constituting a separate controlling system. A second and deeper contrast concerns the foundational role of the contractile surface with respect to a skin brain organization. The latter is not an independently existing controlling system but an organization built in direct connection to a specific contractile tissue across which it helps to modulate electrical and contractile activity. Appreciating this point involves a mind switch away from a general agent-style interpretation of nervous system operation and towards a focus on the requirements imposed by the need to generate patterns of contraction and extension across a specific physical surface.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 321.


“The total contractile surface that an animal has available for motility will be referred to as an animal’s Pantin surface. For each animal species-and ultimately for each individual animal–this Pantin surface will have a specific size and shape. Muscle-based motility can then be conceptualized as deriving from the systematic patterning of contraction-extension across such a Pantin surface, which will be referred to as Pantin patterning.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 321; reference in the naming: Pantin, C.F.A. 1952. “Croonian Lecture: the elementary nervous system.” Proc R. Soc Lond Ser B Biol Sci. 140(899):147-168.


“Summarizing, Pantin patterning is ill-described as output for various reasons. First, there is no dissociation between a controlling and a controlled system, there is simply a single contractile tissue with neural features incorporated or attached to it. Second, the Pantin surface is a physically circumscribed tissue with a particular size and shape that changes during individual development, growth, aging and damage. Patterning will be particular for that tissue and reflect the animal’s body rather than a generic output function that refers to a generalized functional behavior description, such as feeding or mating. Finally, Pantin patterning always involves a whole behaving organism acting as a unit.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. pp. 322-3.


“A single-cell sensor can signal the presence or intensity of an environmental feature–such as light–but sensor arrays enable organisms to become sensitive to patterns across extended surface arrays.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 323.


“Given their central importance, the question here becomes: How did such sensor arrays and their neural infrastructure first evolve?... ... sensor arrays derive from the evolutionary accumulation of spatially organized individual sensors into increasingly complex arrays and circuits geared to detect complex environmental features. Interestingly, such sensor arrays rely to a large extent on connections and interactions between cells within the array; that is to connections transverse to the input-output direction. As the importance of such transverse connections is well-established in neuroscience, one of the assets of the skin brain proposal is its focus on the evolution of a transverse nerve net organization spread out across the animal body.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. pp. 323-4.


“The idea of a deep and intrinsic connection between sensing and moving is now widely established and specifically developed within embodied approaches to cognition. Embodied cognition stresses, in various ways, that the coupling between acting and sensing is the key to intelligence and distances itself from the notion of an internally situated ‘intelligent system’.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 324.


“...the SBT provides a radical idea: The body itself becomes a sensing device that is at heart independent of input or external sensors. A skin brain organization provides the means to allow an animal to differentiate between external surface structures in a way that does not build on sensory input as a precondition.

“In a nut-shell, the SBT states that skin brains need to be sensitive to the internal Pantin patterns they generate; therefore the resulting embodiment will also become sensitive to environmental structure that impinges on this patterning and changes it. This sensitivity would be solely based on self-initiated movements and internal feedback derived from mechanical obstructions of the animal’s body and does not require sensors triggered by events outside the body.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 325.


“Given that a skin brain organization enables an organism to become sensitive to the dynamics of its own body, such sensitivity could–even must–extend to external surfaces that touch and impinge on this body.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 326.


“The sensor-less sensing body is an idealization, aimed to focus on the central idea that a skin brain organization turns the animal body itself into a new kind of multicellular organization that is capable of accessing and handling extended environmental surface structures such as shapes, textures and movements. Skin brains provide an organization that can become differentially sensitive to environmental features at a bodily scale because it must be already differentially sensitive to the spatial dynamics across its own Pantin surface. This internal bodily sensitivity provides the scaffold upon which a similar sensitivity to external spatial organization becomes possible. This direct linkage between internal and external structure provides the foundation of the specific animal sensorimotor organization, the ASMO.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. pp. 326-7.


“To summarize, sensing as accomplished by animals with skin brains is insufficiently described as ‘input’. Instead, the animal body itself becomes a sensing device through its use of contractile tissues and the environmental feedback this generates, both within and external to the body. Together, this constitutes a specific animal sensorimotor organization, or ASMO, that differs from the input-output view on both nervous system and the sensorimotor architecture of animals.” Keijzer, Fred. 2015. “Moving and sensing without input and output: early nervous systems and the origins of the animal sensorimotor organization.” Biol Philos. 30:311-331. 10.1007/s10539-015-9483-1. p. 327.


“We also argue for the importance of reafferent sensing to the evolution of the body-self, a form of organization that enables an animal to sense and act as a single unit.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 1.


“... the concept of reafference: the effects of action on what is sensed.... We show how reafference manifests itself in a number of senses–gravisensing, flow sensing, sensing associated with stretch–in non-bilaterian animals and simpler bilaterians.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 1.


“Von Holst and Mittelsaedt argued for ‘a complete reversal of the usual way of looking at the system’ [the ‘prevailing view of neural activity based on reflex arcs, with their simple flow from sensory stimulus to response’], one that starts with action and inquires into the consequences of those actions on the senses–those consequences are reafference. Part of this reversal was a model in which animals continually establish and maintain states of ‘equilibrium by filtering their raw sensory input with ‘efference copies’ that register their own actions; animals then refer the ‘residual’ of what is sensed to higher control centres as input that is indicative of externally caused events, or exafference.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 2; reference: Von Holst, E. & H. Mittelstaedt. 1950. “Das Reafferenzprinzip.” Naturwissenschaften. 37:464-476. 10.1007/bf00622503.


“We understand reafference itself as any effect on an organism’s sensory mechanisms that is due to the animal’s own actions.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 2.


“... reafference provides an opportunity, a resource, that can be exploited by animals.... A bacterial example would be the way in which Escherichia coli and other bacteria use motility to assess the presence of a chemical gradient....” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 2.


“... given that self-initiated activities tend to have predictable consequences, reafference constitutes feedback concerning such predictions. In this way, reafference provides a means by which organisms can evaluate these predictions and modify the activity involved. This need not involve a nervous system. For example, in sponges, sensory cilia keep track of the flow produced within the body and can signal when this flow ceases.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. pp. 2-3.


“During deformational reafference, changes in the shape of the body lead to sensing, such as during proprioception. During translocational reafference, self-initiated motions induce an interaction with the environment with consequences for sensing (e.g. various flows).” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 3.


“An organism has, or embodies, a body-self if it has a particular form of organization. That form of organization includes motility (of the whole or parts) and sensing, where action and sensing are tied together through reafference. The body-self then encompasses the devices and their activities that enable reafferenct coupling between the animal’s own actions and sensing. The body-self can thus include sensors and effectors, their activity or actions, and also the form of the body influencing reafferent coupling. In this view, brains, if they are present, are not the sole locus or even the centre of this self, but a part of the body that is characterized by this self. The body-self enables the organism to sense and act as a single unit, and thus a self that separates itself from the rest of the world.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 3.


“Gravity sensing relies on specialized cells or organs called statocysts in many animals. Statocysts have a cavity containing small concretions or statoliths. When the animal changes its orientation relative to the gravity field, the statoliths move in the cavity and stimulate mechanosensory cells lining the cavity. The signal for the statocyst is generated by the tilt of the body and can lead to a response (e.g. the animal ‘righting’ itself). Such tilt may come about by self-generated movements or external forces (e.g. water turbulence). When actively induced tilt has sensory consequences, this qualifies as reafference. Reafferent gravisensing then contrasts both with exafferent gravisensing (in response, for example, to turbulence or waves), and with passive gravi-orientation, where the body acts as a buoy, as a consequence of the distribution of mass in its physical layout.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 4.


“Just as active motion induces changes in relation to the Earth’s gravitational field, in aquatic organisms, it also induces flow. Flow sensors, widespread in aquatic animals, generally consist of one or more mechanosensory cells which have a sensory cilium deflectable by flow. The cilium can be surrounded by microvilli, forming a mechanosensory apparatus where deflections are transduced into cellular signals by mechanosensory ion channels.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 5.


“Sensing changes in water flow can be relevant for both swimming and sessile organisms. Sessile or planktonic filter-feeding animals including sponges, ascidians, anthozoans and many other animals can generate feeding currents by cilia or muscular appendages. If the animal can sense this self-generated flow, it is readily enabled to detect deformations in the flow field caused by clogging or approaching objects such as predators distorting the flow field.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 5.


“By contrast [to bacteria and organisms with low Reynolds numbers), larger animals like fish operate at higher Reynolds numbers where inertia is more important. A fish after a swim bout will glide in the water, without motor activity. During gliding, reafferent signals can still activate the lateral line. The corollary discharge can persist during the glide phase, suppressing reafferent signals even without a motor action. This is a good example to illustrate that in a corollary discharge system, it is not sufficient to have a simple ‘subtraction of the motor command itself, but the system needs to predict the consequences of the motor action, given the nature of the body and environmental setting.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. pp. 6-7.


“For organisms with more complex visual eyes, self-induced optical flow provides an important mechanism to orient themselves with respect to the environment. The changes in visual texture, signalled by the light falling on an array of photoreceptors, provide the animal with information about objects, pathways to traverse and imminent collisions. In a way that is comparable to the forward point of stasis in Platynereis larvae, the direction of movement is simply signalled by the point in the visual array from which all other points diverge.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 7.


“Although the term ‘reafference’ has been most often used for effects of action on exterosensors, the distinction between self-caused and other-caused sensory events (reafference and exafference) is also available in the case of interoception.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 7.


“The animal body shape is a dynamic feature even when it is outwardly unchanging. A useful concept here is tensegrity or tensional integrity. Tensegrity is a general design principle that is followed to build structures from rods under compression with attached cables imposing the compression. The integrity of the structure arises from a combination of rigid and elastic components combined under tension. This form of organization also applies to the animal body. Here, a skeleton constitutes the rigid parts that oppose compression, while muscle and tendons (mostly) constitute the flexible component that, by means of tensile forces, binds the skeleton together.

“For early animal evolution, three differences with the original tensegrity concept are relevant. First, the tensile components can change length by muscle contraction and relaxation, making the tensegrity structure capable of dynamic and reversible changes. Second, early cases did not have hard skeletons, so the opposing force for a muscle system derives instead from more diffuse hydrostatic skeletons that, like water-filled balloons, provide a flexible but incompressible mass. Third, the dynamically changing mechanical forces involved in these animal tensegrity structures themselves constitute signals that travel across large parts of the body–like using a connecting rope to ring a faraway bell-and influence biochemical processes at the cellular level.”

“The sensitivity of cellular processes to the dynamically changing pattern of mechanical forces across the tensegrity structure makes reafference an intrinsic ingredient of this organization. Self-generated forces imposed on the structure will influence proprioceptive sensors both at a cellular and at a multicellular scale. The importance of force-dependent molecular switches that react to developmental tissue deformations has been well established.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 7.


“Along with new species and new traits, evolution occasionally produces new kinds of living units–new kinds of selves. The nature of such a new form can include the layout and materials of the body, capacities for acting and sensing, and systems of coordination and control, such as nervous systems and others. The animal body-self is one such form of organization, resulting from evolutionary change in all these areas. The existence of a body-self is a matter of degree. In its paradigm cases, a body-self is unified by neural control, reafferent sensing and a suitable morphology, all of which facilitate action at a multicellular level.” Jekely, Gaspar, Peter Godfrey-Smith & Fred Keijzer. 2021. “Reafference and the origin of the self in early nervous system evolution.” Philosophical Transactions of the Royal Society: B. 376: 20190764. 10.1098/rstb.2019.0764. p. 9.


“The Information Hypothesis: For every perceivable property of the environment, however subtle, there must be a higher order variable of information, however, complex, that specifies it.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 2.


“Gibson Information: Higher order, spatio-temporal variables of stimulation that are specific to behaviorally relevant properties of the environment within the nomic constraints of an ecological niche.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 3.


“One energy array that has been hijacked as a medium of information is the electric field. Weakly electric fish, which evolved independently in Africa and South America, emit electric organ discharges (EOD) not to stun their prey but to sense their surroundings via active electrolocation. The well-studied African mormyrid Gnathonemus petersii has a cluster of electrocytes (modified muscle cells) in its tail that generates brief EOD pulses with an amplitude <1 V, creating an electric field in the water around the fish’s body. Thousands of electroreceptor organs along the dorsal and ventral surfaces register the spatial pattern of voltages across the skin, with a higher density near the head. Distortions of the electric field produced by objects alter the voltage pattern, enabling mormyrids to sense objects up to 12 cm away, localize prey, and orient to their surroundings.

“An object within a mormyrid’s field casts an electric ‘shadow’ on its skin, modulating the voltage amplitude with a Mexican-hat profile along the fish’s body. Objects that are more conductive than water (larvae, plants, and metal) concentrate the current flow, increasing the amplitude of the profile, whereas resistive objects (rock, clay, dead wood, and plastic) reduce its density, forming an inverted Mexican hat. The location of the shadow’s peak amplitude (max or min) on the skin specifies the bearing direction of the object.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 4.


“In sum, a host of object properties are uniquely specified by higher order ratios of four variables: the peak amplitude, maximum slope, diameter of the electric shadow, and the distortion of the EOD waveform [detectable as a pattern across the skin of the mormyrid]. These variables are informative by virtue of the laws of electrodynamics in an aquatic niche, including the resistance and capacitance of meaningful classes of objects.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 5.


“Now consider the narwal’s ecological niche. The narwal is an arctic whale that hunts halibut in complete darkness using click echolocation, deep beneath the winter pack ice. It can dive to depths of 1,500 m for up to 25 minutes, reaching pressures greater than 150 atmospheres. But at their wintering grounds, ice covers 90% or more of the water surface. As a mammal that must surface regularly to breathe, there is thus strong selective pressure to avoid getting trapped under rapidly forming and shifting sea ice.

“When surface water freezes, the salinity of the water below the ice increases. Thus, a narwal swimming up to surface ice encounters a salinity gradient in space (along the tusk) and time (as it moves up the gradient). A higher concentration of sodium and chloride ions in the seawater generates an outward osmotic flow in the dentin tubules, stimulating the odontoblasts; conversely, a lower concentration generates an inward osmotic flow....

“Within the narwal’s arctic niche, salinity gradients thus specify a very relevant property: the penetrability of the surface. The laws of chemistry, together with the niche’s regularities, grant salinity the status of information for the affordance of penetrability.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. pp. 6-7.


“Affordances are everywhere: graspable objects, walkable surfaces, climbable slopes, throwable projectiles, catchable prey, edible food, habitable shelters, cutting or pounding tools, and so on. Indeed, Gibson proposed that an ecological niche is a set of affordances, which co-evolve with the action capabilities of the species.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 10.


“Following principles of geometric and dynamic similitude, affordances can be expressed as dimensionless ratios of environmental and animal variables. In dimensional analysis, such ratios are called π-numbers. At critical values of a π-number, the system’s behavior changes qualitatively, and because π-numbers are dimensionless, their critical values are scale-invariant. A good example is the Reynolds number, whose critical values capture the transition from laminar to turbulent fluid flow in systems of different scales.

“Applying this way of thinking to the humble gap, passability may be characterized by a dimensionless π-number,
π = G/W
where G is gap width, W is frontal body width, and a critical value πc expresses the boundary between passable and impassable gaps. Such body-scaled (geometric) or action-scaled (dynamic) ratios capture affordances that are invariant across individuals of different sizes. In principle, higher order affordances could be characterized by increasingly complex π-numbers.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. pp. 10-11.


“This brings us to what is perhaps Gibson’s most notorious claim–that affordances not only exist, but can be perceived:

“The Affordance Hypothesis: An affordance is perceivable if there is higher order information, however complex, that specifies the relation between environmental properties and animal properties that constitutes it.

“Note what the hypothesis does not say. It does not claim that all affordances can be perceived; that is an empirical question. Neither does it claim that all affordances are specified by information. Nor does it assert that affordances are perceived spontaneously, for the affordances of terrain, food items, and projectiles may be discovered by exploration, and the observer may become attuned to information through perceptual learning. Rather, the claim is that affordances are potentially perceivable if they are grounded in information specific to the relevant environmental-animal relations–if only we are clever enough and dogged enough to find it.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. pp. 12-13.


“Affordances are not merely combinations of neutral physical properties, however, for when considered in relation to an animal, the complex has ‘unity,’ ‘value,’ and ‘meaning’ for behavior. He [Gibson] offered an example: A surface that is horizontal, flat, extended, rigid, and low, relative to the animal’s body size, weight, and leg length, might be specified by a higher order combination of optical variables. this ‘compound invariant’ would thus specify the affordance of a walkable surface.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 13.


“It might be objected that, while the environment may be perceived, affordances are surely inferred based on prior knowledge of one’s body plan and motor abilities. In contrast, Gibson claimed that affordances are perceived per se, based on information about the relevant complex of environmental-animal relations. At the heart of this claim lies the notion of body-scaled or action-scaled information, the idea that visual information can specify the relation between environmental properties and the animal’s action system.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 13.


“As Gibson foresaw 40 years ago, if we begin with cases of successful perceiving and acting, we often find informational variables that specify environmental properties and guide effective actions within the nomic constraints of an animal’s niche. Information is where you find it. The case studies I have reviewed here serve as existence proofs that information exists in wildly different energy arrays and is uniquely specific to behaviorally relevant properties for creatures great and small. Starting with the presumption that perception is an ill-posed problem leads us to abandon the search, sending vision science down the rabbit hole of prior knowledge. Gibson’s hypothesis that vision is ecologically well-posed holds out hope for a vision science grounded in natural law.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 18.


“Rather than internally representing external constraints, I would suggest we leave them in the environment where they belong. This would enable us to understand the visual system as adapting to the information they make available, in the course of evolution, development, and learning. The visual system need not internally represent facts about gravity or surface texture to enable successful perceiving, its neural networks just have to be tuned to the resulting patterns of stimulation. In van de Grind’s useful analogy, a fish need not know the laws of hydrodynamics in order to swim, its body and perceptual-motor loops merely need to be tuned to the properties of water. If one persists in calling such tunings ‘knowledge’ and their activation ‘inference’, one persists in being metaphorical–and so does one’s theory.” Warren, William H. 2021. “Information is Where You Find It: Perception as an Ecologically Well-Posed Problem.” i-Perception. 12(2):1-24. 10.1177/20416695211000366. p. 19; reference: van de Grind, W. 1988. “The possible structure and role of neuronal smart mechanisms in vision.” Cognitive Systems. 2:163-180.


“Leibniz’ lament ‘that perception and that which depends on it are inexplicable on mechanical grounds, that is to say, by means of figures and motions’ has never been satisfactorily addressed.” Sporns, Olaf. 2022. “The complex brain: connectivity, dynamics, information.” Trends in Cognitive Sciences. 26(12): 1066-1067. p. 1066.


“Connectivity has already become a core concept in neuroscience. Nevertheless, despite widespread use, the term is rarely carefully defined. The fundamental distinction between structural connectivity (material connections) and functional connectivity (statistical dependencies) is often neglected. Yet, this distinction is important to grasp, as the dialogue between structure and function animates much of the brain’s complexity.” Sporns, Olaf. 2022. “The complex brain: connectivity, dynamics, information.” Trends in Cognitive Sciences. 26(12): 1066-1067. p. 1066.


“We build on recent theoretical and computational developments to systematically search for autocatalytic cycles in reaction networks and then assess their thermodynamic consistency, i.e. the impact of thermodynamic constraints on their realization.... We then question whether such autocatalytic cycles, defined on the sole basis of the reaction network topology, can also be realized once thermodynamic constraints are introduced. To do so, we take into account the reaction kinetics that themselves depend on the Gibbs free energies and concentrations of the reactants, and the activation barriers of the reactions. We show that regardless of these physical quantities, any potential autocatalytic cycle may be instantiated in some region of the concentration space as long as one assumes this space is unbounded. In contrast, thermodynamic constraints do restrain compatibility relationships between autocatalytic cycles and will thereby impact the dynamics of complex chemical networks.” Kosc, Thomas, Denis Kuperberg, Etienne Rajon & Sylvain Charlat. 2024. “Thermodynamic consistency of autocatalytic cycles.” bioRxiv. 10.1101/2024.10.11.617739. p. 1.


“Working toward the long-term goal of an explicit grounding of Darwinian dynamics into physical processes, we addressed in this study the implications of thermodynamic constraints on the existence and detection of autocatalytic cycles given a reaction network....

“We found that constrain[t]s imposed by free energies and activation barriers can always be compensated by adjusting concentrations, thereby allowing any minimal autocatalytic cycle to also be thermodynamically consistent. In other words, the list of autocatalytic cycles in a reaction network remains unaffected by these physical constraints, as long as concentrations are not limited by upper or lower bounds. However,... it should be noted that heterogeneity in free energies and activation barriers do restrict the volume of the concentration space where a cycle runs.

“These conclusions on isolated cycles do not readily apply on combinations of cycles. Indeed, thermodynamic realism does restrict the list of mutually compatible cycles, even in an unlimited concentration space, such that topologically compatible cycles can turn out incompatible. Incompatibilities between two autocatalytic cycles can therefore stem from two distinct sources, namely the topology of the reaction network and irreconcilable demands on concentrations. Kosc, Thomas, Denis Kuperberg, Etienne Rajon & Sylvain Charlat. 2024. “Thermodynamic consistency of autocatalytic cycles.” bioRxiv. 10.1101/2024.10.11.617739. p. 6.


“In this context [pre-template evolution], autocatalytic sets of chemical reactions (ACSs), comprising various chemical species, with cooperative catalytic interactions have been proposed as an intermediate stage of chemical evolution.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 2.


“... an ACS is comprised of a food (substrate) set, a reaction set, and a molecule set; (i) each reaction is catalyzed by at least one molecule, and (ii) every molecule is produced by a series of reactions from food molecules. A more general definition of autocatalysis in a network uses reaction network stoichiometry; a cycle of reactions (which are not necessarily catalytic) is defined as autocatalytic if the chemicals within the cycle increase stoichiometrically over time.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 3.


“The likelihood of the existence of an ACS in a randomly generated network has been studied under various assumptions. These studies find that the likelihood of existence is typically an exponentially decreasing function of the size of an ACS. In contrast, for a reaction network of fixed size with random links, as the number of links (edges) increases, the probability of the existence of an ACS shows a phase transition–from 0 to 1–as the number of edges crosses a threshold. This is the same as the ‘percolation’ transition, wherein a random graph goes from being composed of largely disconnected clusters of nodes to one giant connected cluster.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 3.


“Broadly, the self-reproduction of an ACS can be viewed as the generation of a copy–from the substrates–of the autocatalytic chemical system due to the self-catalyzed reaction dynamics. This generally depends on the ‘boundary conditions’ on the system–sustained self-reproduction requires a continuous flux of chemicals and associated dissipation, i.e., out-of-equilibrium. Indeed, a defining characteristic of life is energy consumption, which arises naturally from the necessity for continuous self-reproduction. Such out-of-equilibrium situations can be realized due to the boundary conditions on the system, which may be of the following kinds: (i) continuous stirred-tank reactor (CSTR) or chemostat, (ii) multiple cycles of serial transfer, and (iii) generic compartmental dynamics such as cell growth and division....

“Generally, in any of the above conditions, a chemical reaction system eventually reaches stationary stable states–attractors, in the language of dynamical systems. In the present context, self-reproduction of autocatalytic networks is characterized as one such stationary state.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. pp. 5, 6.


“The evolvability of the autocatalytic network has been debated between critics and advocates. It was pointed out by Vasas and colleagues that autocatalytic networks, specifically based on the GARD model, were not evolvable in the sense that the network compositions were not heritable. However, later work together with Kauffman, which considered another model, did indeed show that non-evolvability is not necessarily true for autocatalytic networks in general–particularly, the conditions for the evolvability of a specific autocatalytic network were identified. Generally, such a disagreement on the evolvability of autocatalytic networks might arise either due to the lack of consideration of several aspects such as the ones we have summarized above (e.g., system size and order of reactions) or due to an insufficient exploration of parameter space.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 11.


“Taken together, systems based on small organic molecules are relatively easy to engineer and can possibly be used to construct complex networks. Sugar reaction systems are autocatalytic and have the potential to enrich essential sugars from a prebiotic milieu. Though there exist a few methods to enrich ribose in the formose reaction, there seems to be no demonstration of evolution due to the lack of heredity.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 15.


“Self-reproducing peptide systems possess several features required for evolution to occur. Though they do not have inherent catalytic properties and diversity (e.g., sequence diversity of nucleic acid catalysts), templated-ligation using coiled-coil structures in the peptide-system results in exponential autocatalytic growth. Structurally too, all the self-reproducing peptides discussed here are based on the same coiled-coil heptad repeat structure with little flexibility on the amino acid requirements. In spite of this, complex cross-catalytic networks with as many as 25 nodes can be formed. In this system, a recombination-like mechanism, employing reversibility which allows new combinations of reproducer to emerge from the peptide pool, can be a source of variation. Peptide-based reproducing systems are also robust against molecular perturbations (chemical changes) as native sequences dominate a reproducer pool even in the presence of mutant sequences. Furthermore, peptide-based systems are the only ones where bistability has been demonstrated experimentally; however, since bistability is between two concentrations of the same reaction node, the notion of distinct chemical compositional identity has not yet been established. Another important feature of the self-reproducing peptide system is the ability to control the autocatalytic and spontaneous reactions using environmental control such as the pH and salt concentration. Such properties allow environmental control of the switch between autocatalytic and non-autocatalytic pathways.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 18.


“In spite of the possibility for cross-catalysis, network construction has not yet been demonstrated in some systems (e.g., sugars and macrocycles). Self-reproducing autocatalytic networks have been constructed using DNA, RNA, and peptide chemistries; however, networks comprising of a large (>2) number of nodes has been demonstrated only for RNA- and peptide-based systems. Steady-state compositional identity of the autocatalytic networks has so far only been established for Azoarcus [a bacterium] RNA-based systems. However, the compositional identity can be affected by the presence of spontaneous reactions, i.e., background reactions (or due to catalysis by non-autocatalytic pathways) and degradation.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 21.


“Experimental demonstration of the compositional heredity of an autocatalytic network remains an open challenge.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 22.


“While the existence of bistable (two states) compositional identities has been demonstrated using peptide chemistries, such multi-stability has not yet been demonstrated in other experimental systems.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 22.


“The realization of Darwinian evolution in minimal chemical system and the quest for minimalistic life-like systems will require intensive investigations of actual network topologies, energetics, and kinetics to bridge theoretical models with experimental possibilities. In the context of the origins-of-life scenario, autocatalytic systems as the first unit of life should (i) spontaneously emerge from an abiotic chemical mixture (which could include catalytic and non-catalytic building blocks) and (ii) be evolvable. Although there are theoretical studies that discuss the conditions for such an emergence, there are not experimental demonstrations so far. For both theoretical and experimental studies, elucidating the conditions for spontaneous emergence and evolvability of autocatalytic systems is imperative.” Ameta, Sandeep, Yoshiya J. Matsubara, Nayan Chakraborty & Sandeep Krishna. 2021. “Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems.” Life. 11:308. 10.3390/life11040308. p. 23.


“We now report an example where a set of newly formed replicators exhibits exactly the opposite [from mechanisms of co-operation and co-existence where replicators assist each other’s formation]: parasitic behavior. Emergence of the parasitic replicator relies on cross-catalysis by a structurally closely related pre-existing replicator, which is subsequently consumed by the very replicators that it brought into existence. While several reports describe the emergence of parasites in systems where enzymes mediate replication of nucleic acids, this is the first report of the emergence of a parasite in a system of autonomous self-replicators.” Altay, Meniz, Yigit Altay & Sijbren Otto. 2018. “Parasitic Behavior of Self-replicating Molecules.” Angewandte Chemie: International Edition. 57:10564-10568. 10.1002/ange.201804706. p. 10565.


“... multicellular organisms confront an additional challenge [to regulatory organization for single-cell organisms by chemical signaling] –controlling, across longer distances, the activities of individual cells and of groups of cells that constitute their tissues and organs, so that they carry out the activities required by the whole organism. To do this, multicellular organisms adapt two strategies already manifest in single-cell organisms–the diffusion of chemical signals in the extracellular milieu and conduction of ion changes along the membrane of cells.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. pp. 1-2.


“Among them [factors rendering the C. elegans worm a valuable model for understanding a nervous system], three are particularly relevant. (1) The worm lacks a central brain. When organisms have a brain, and especially a highly evolved brain, there is a tendency of researchers to concentrate on the brain and its ‘highest’ centers. However, even in organisms with a brain, much of the nervous system is decentralized, and in the worm it is fully decentralized.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 2.


“Neuromodulators operate differently than gluamate and GABA that serve to extend electrical activity from a presynaptic neuron to a postsynaptic one as a result of being released at a synapse and acting specifically on ionotropic receptors of the post-synaptic neurons (and otherwise degraded). Neuromodulators (typically monoamines and neuropeptides) are secreted from diverse locations on neurons, diffuse through the extracellular matrix, and act on any neuron with appropriate metabotropic receptors. In most cases these receptors are G-protein coupled receptors (GPGRs) that elicit second messengers within the cell that change its metabolism, often initiating new gene expression. These effects are much longer lasting than those that suffice to elicit electrical activity in a post-synaptic neuron; we will characterize them below as setting the agenda for other neural processing.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 2.


“Given their role in coordinating responses to the state of the organism over longer timescales than electrical signaling, the characterization of monoamines and neuropeptides as modulators does not do justice to their role in the operation of the nervous system. By registering overall conditions in the organism and its environment they establish enduring configurations of the circuits that respond to more transient inputs. One might better characterize them as using the state of the organism to set the agenda for processing sensory information.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 3.


“Sleep, defined behaviorally in terms of quiescence, reduced responsiveness to mild stimulation but maintained responsiveness to strong stimulation, and increased sleep pressure when sleep is prevented or interrupted, is widespread among animals with neurons.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 4.


“Worms are commonly classified as engaging in three activities: roaming, dwelling, or quiescence. Since in laboratories worms are commonly raised on E. coli, which for them is a suboptimal nutrient, they spend little time quiescent. In the wild, however, worms become satiated, and when they do, they cease both roaming and dwelling and enter a quiescent state that resembles steep. Here we focus on just roaming and dwelling, states worms can maintain for tens of minutes before quickly transiting to the other. While roaming, worms exhibit prolonged, fast forward movements punctuated with infrequent reversals. In contrast, while dwelling, during which they feed, defecate, and lay eggs, worms move forward slowly and frequently reverse their direction.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 6.


“Perhaps the most dramatic response is when starvation occurs early in development, when it can have particularly serious consequences. Worms have developed a strategy, known as dauer diapause, for anticipating starvation that would impair their development and protecting themselves. One indicator of potential starvation is abnormally high population density. Worms in the first larval stage (L1) can recognize this through their ASI sensory neurons, which detect increased ascarosides secreted by other worms (as well as extremely high temperatures or actual starvation). By inhibiting the release of the peptides that trigger normal molting and progression to L2, worms enter the predauer stage L2d. If the increase in ascarosides turns out to be a false alarm (recognized by the availability of NAD+ and other products generated as food is metabolized), worms proceed to molt and enter stage L3. But if they do not consume food, they enter the dauer state, undergoing large-scale changes of morphology, physiology, and behavior. Morphologically, they manifest a thicker cuticle, a remodeled pharynx, and a narrowed body. Physiologically, they increase their stores of lipids and their metabolism changes. Behaviorally, they are mostly motionless, but they do react to vibration or touch and can even stand on their tails, wave their bodies, and attach themselves to insects or other animals. During this state they are highly tolerant to starvation as well as other stresses. They can remain in this state for up to four months (much longer than their usual one-month lifespan). When an adequate food source is detected, worms exit the dauer state and live as normal adults....

“The decision to enter dauer is very consequential. Going through the dauer state results in changed behavior in the adult, including food seeking behavior–worms that have gone through dauer are more likely to dwell than to roam. It also has effects on reproduction that persist into the following generation. Reversing dauer is an extended process. Worms require about 20 h to re-enter normal development.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. pp. 7-8.


“In this section we have described some of the activities in which worms engage to procure the energy they require to maintain themselves far from equilibrium. The control of these various activities is complex, involving numerous neural circuits. These circuits involve not only classical synapses but also monoamines and neuropeptides that are released and diffuse through the worm to loci at which they bind metabotropic receptors. These receptors operate on a slower timescale than ionotropic receptors, thereby serving to coordinate behavior over prolonged periods of time. Moreover, monoamine and neuropeptide circuits often work in coordinated opposition to one another in an arrangement that enables switching between different enduring states. A further feature of these circuits is that they enable the worm to coordinate responses to multiple sources of information that require different responses if the worm is to maintain itself.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. pp. 8-9.


“The polymodal ASH sensory neurons constitute one of the inputs to this circuit [AVA and RMG interneurons that figure centrally in initiating reversal movements]. ASH neurons extend a ciliated dendrite into the external environment that is capable of sensing conditions such as high-osmolarity, high and low pH, the presence of blue or UV light, heavy metals, toxic volatile odorants, and detergents, as well as when objects touch its nose.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 9.


“Ghosh et al. investigated how worms decide between locomotor options by positioning worms within a hyperosmotic ring that could desiccate them. Only 30% of well-fed worms crossed the barrier. However, if an attractive scent, indicating food availability, originated from outside the ring, 80% crossed the barrier. If the worms had been food deprived for 1 or 5 h, the percent crossing the barrier was even greater.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 10; reference: Ghosh, D.D., T. Sanders, S. Hong, L.Y. McCurdy, D.L. Chase, N. Cohen & M.N. Nitabach. 2016. “Neural architecture of hunger-dependent multisensory decision making in C. elegans.” Neuron. 92(5):1049-1062,. 10.1016/j.neuron.2016.10.030.


“Some bacteria are pathogenic to worms. For example, Pseudomonas aeruginosa disrupts the intestinal lumen, Bacillus thurigiensis generates a crystal pore-forming toxin that disrupts ingestion, and Microbacterium nematophilum adheres to the rectal and post-anal cuticle. When harmful bacteria are detected in the pharynx, worms can halt normal grinding and expel the current contents. When pathogens reach the gut and infect the worm, it often initiates an innate immune response. Worms also learn to avoid those bacteria which have made them ill. We will focus on a response to illness that is common among animals but only recently discovered in worms–sleep. As we noted above, sleep in worms was initially only identified in transitions between larval states, not in adult worms. Researchers have more recently demonstrated sleep behavior after exposures to pathogens that result in sickness and during prolonged starvation. Davis and Raizen argue that sleep during illness redirects energy from motor tasks to address the illness or injury. Typically this sleep state is maintained for a period after the stress is removed, suggesting that sleep may contribute to the recovery from stress. Moreover, the sleep period is extended proportionate to the degree or duration of the stressor.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 11; reference: Davis, K.C. & D.M. Raizen. 2017. “A mechanism for sickness sleep: lessons from invertebrates.” J. Physiol. 595(16):5415-5424. 10.1113/JP273009.


“Theorists that constitute the biological autonomy tradition... have characterized biological organisms as organized systems that are far from equilibrium and that maintain themselves through their own actions. These theorists have appealed to some variation of the notion of organizational closure to explain how organisms perform the activities they need to perform to maintain themselves. This notion characterizes the organization common to all living organisms as a circular network of components and production processes in which, for each component C1, the conditions necessary for its production and maintenance are determined by another component C2, whose existence and maintenance depends, directly or indirectly (through other components), in turn, on C1. Different theorists have advanced different accounts of what, specifically, is closed: Maturana and Varela (1980) invoke closure of processes, Rosen (1991) closure of efficient causation, and Moreno and Mossio (2015) closure of constraints. All of them, however, characterized these abstractly and have not elaborated on how organisms actually maintain themselves. Bich and Bechtel have argued that to explain how organisms maintain themselves as autonomous systems, it is not sufficient to focus only on closure of productive components. Closure needs to be complemented with an account of how the activities of components are controlled from within the system. As a contribution to providing a substantive account of how multicellular organisms control the basic mechanisms through which they maintain themselves, we have examined some of the processes employed in the worm, both those involved in procuring food and avoiding adverse conditions. In doing so, we have illustrated how ideas from the autonomy tradition about self-maintenance and control can be grounded in actual biology. The worm provides a useful model for understanding the basic processes of neural control since, lacking a vascular system, it employs its nervous system to coordinate the diverse cells that constitute it.

“Even with relatively few neurons and without a centralized brain, the worm is able to control a host of behaviors so that they are performed when they are appropriate given the state of the worm and the conditions it confronts.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. pp. 11-13; reference: Bich, Leonardo & W. Bechtel. 2022. “Organization needs organization: understanding integrated control in living organisms.” Stud. History Philosophy Sci. 93:96-106. 10.1016/j.shpsa.2022.03.005.


“In particular, in addition to transmitters such as glutamate and GABA acting on ionotropic receptors, worms make extensive use of monoamines and neuropeptides acting on metabotropic receptors. These importantly enable the worm to maintain agendas, set by their assessment of their internal state, over relatively long periods. They enable the worm to process information in different ways depending on conditions within the worm and its environment so as to carry out concerted activity.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 13.


“Recent work on unicellular organisms has identified four main features of control: (1) measuring appropriate variables and acting upon those measures in a manner that (2) is dynamically decoupled from basic metabolism, while (3) allowing for intermediate components to integrate different measurements and effects, and yet (4) enabling sufficient segregation to achieve specific responses. In multicellular systems control exhibits similar features but involves intercellular as well as intracellular activities. The neural systems we have described in the worm are such multicellular control mechanisms. Neurons are sensitive to features of the internal and external environment of the organism and operate accordingly. They do so in a way that does not directly depend on their internal metabolic state but on what they sense.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 13.


“There is no central control in the worm, but a distributed network of neurons organized into local circuits both regulating specific activities and integrating their activities with each other. Moreover, studying neural control in the worm brings to light a further general feature of biological control. Control mechanisms not only transiently modulate other mechanisms at specific points in time, but also set agendas that affect those mechanisms over sustained periods of time. This means establishing, maintaining and shifting between different global behavioral and physiological regimes such as feeding, dwelling, sleeping and dauer, which endure for a prolonged time depending on the state and needs of the organism.” Bechtel, William & Leonardo Bich. 2023. “Using neurons to maintain autonomy: Learning from C. elegans.” BioSystems. 232:105017. 10.1016/j.biiosystems.2023.105017. p. 13.


“The ‘oxidative damage initiation hypothesis for meiosis’ proposed by Hoerandl and Hadacek in 2013, states that the molecular actors of meiosis evolved from archaean DNA repair machinery as a means to counter the oxidative stress induced by proto-mitochondrial endosymbionts. As such, sexual recombination would have been a cell survival strategy. Alternatively, sexual recombination could also have been a mechanism to correct accidental DNA replications. In the case of haploid cell-cell fusion, meiosis could have been favored if diploidy conferred a selective advantage in the population. Either way, the process of meiosis brought multiple advantages to eukaryotic life, including the promotion of genetic diversity, protection of the genome against the accumulation of deleterious mutations (i.e. known as Muller’s ratchet), and the ability to rapidly adapt to environmental changes. These benefits have supported the maintenance of meiosis throughout eukaryotic evolution.” Rizos, Iris, Miguel J. Frada, Lucie Bittner & Fabrice Not. 2024. “Life cycle strategies in free-living unicellular eukaryotes: Diversity, evolution, and current molecular tools to unravel the private life of microorganisms.” Journal of Eukaryotic Microbiology. 71:el3052. 10.1111.jeu.13052. p. 2; reference: not listed.


“Historically, protist reproduction was thought to be limited to binary fission (i.e. asexual reproduction) like bacterial reproduction. However, complex life cycle transitions and sexual cycles appear to be in fact the norm among protists.... By summarizing the various life stages, life phases, and life cycle transitions described among protists, we propose a synthetic protist life cycle defined by three sub-cycles, through which a vegetative life stage transits: (1) an asexual cycle characterized by mitotic reproduction of the vegetative stage; (2) a sexual cycle defined by the processes of meiosis and syngamy (i.e. cell fusion and nuclear fusion), and (3) a colonial cycle in which mitotic, aggregation, and differentiation events give rise to morphologically variable multinucleate life stages.” Rizos, Iris, Miguel J. Frada, Lucie Bittner & Fabrice Not. 2024. “Life cycle strategies in free-living unicellular eukaryotes: Diversity, evolution, and current molecular tools to unravel the private life of microorganisms.” Journal of Eukaryotic Microbiology. 71:el3052. 10.1111.jeu.13052. p. 2.


“In contrast to multicellular organisms, sex in protists can be nonreproductive, meaning that it does not result in the formation of a new individual. Sex is generally facultative for protists, as new individuals can also be produced mitotically.... However, in certain cases, sex may be required for adaptation to unfavorable environmental conditions.” Rizos, Iris, Miguel J. Frada, Lucie Bittner & Fabrice Not. 2024. “Life cycle strategies in free-living unicellular eukaryotes: Diversity, evolution, and current molecular tools to unravel the private life of microorganisms.” Journal of Eukaryotic Microbiology. 71:el3052. 10.1111.jeu.13052. p. 2.


“The endogenous circadian system functions to organize behavior and physiology to adapt to and anticipate environmental changes in light, temperature, food, and mate availability. In addition, the circadian system temporally organizes molecular, cellular, and physiological processes relative to one another. Synergistic processes are timed to coincide, whereas mutually incompatible ones are temporally separated. The presence of circadian clocks in organisms from bacteria to mammals is evidence of their critical role in organism fitness....

“... circadian systems are organized into three main parts: the core clock, which keeps time; input pathways, which synchronize the clock to the environment; and output pathways, which transmit information to temporally organize behavior and physiology.” Allada, Ravi & Brian Y. Chung. 2010. “Circadian Organization of Behavior and Physiology in Drosophila.” Annu Rev Physiol. 72:605-624. 10.1146/annurev-physiol-021909-135815. [numbering from author manuscript] p. 1.


“Several feeding- and metabolism-related parts of the fly harbor circadian clocks. These parts include the fat bodies (the fly homolog of fat, liver, and the immune system), involved in fuel storage and energy balance; the antennae and maxillary palp, involved in food/odor detection; proboscis (the fly mouth part), involved in taste and feeding; and the gastrointestinal tract, involved in digestion and nutrient absorption. The fat body clock is an important contributor to metabolism and feeding.” Allada, Ravi & Brian Y. Chung. 2010. “Circadian Organization of Behavior and Physiology in Drosophila.” Annu Rev Physiol. 72:605-624. 10.1146/annurev-physiol-021909-135815. [numbering from author manuscript] p. 9.


“The list of rhythms that flies display is ever-expanding. Some rhythms that we did not have space to discuss include those in cuticle deposition, in susceptibility to oxidative stress, and in synaptic bouton size at the neuromuscular junction. Although the cellular basis of these rhythms is also unclear, they are likely driven by peripheral clocks as well. In addition, clocks are present in a range of organs beyond those mentioned above, such as the Malpighian tubules, which serves a similar function as do the kidneys. The larger point is that, although the focus has been on locomotor activity rhythms, Drosophila exhibits a rich repertoire of circadian rhythms reflecting the diversity of its physiological systems.” Allada, Ravi & Brian Y. Chung. 2010. “Circadian Organization of Behavior and Physiology in Drosophila.” Annu Rev Physiol. 72:605-624. 10.1146/annurev-physiol-021909-135815. [numbering from author manuscript] p. 11.


“In addition to light, the timing of food intake acts as a dominant Zeitgeber to peripheral clocks (but not the SCN [suprchiasmatic nucleus]). Peripheral circadian rhythms can be synchronized by scheduled feeding. It has been demonstrated that daytime restricted feeding in mice inverts their feeding behavior as well as the phase of circadian clocks in metabolically active tissues such as liver and pancreas, but does not affect the phase of the SCN pacemaker clock, the latter remaining entrained to the external light-dark cycle.” Pilorz, Violetta, Charlotte Helfrich-Foerster & Henrik Oster. 2018. “The role of the circadian clock system in physiology.” European Journal of Physiology. 10.1007/s00424-017-2103-y. P. 2.


“We have obtained a wealth of information about how the circadian clockwork can be entrained to and affected by external photic and non-photic cues. At the same time, we have just started to decipher the modes of interaction by which cellular clocks talk with each other and environmental factors and how they collaborate in shaping the physiological circadian landscape.” Pilorz, Violetta, Charlotte Helfrich-Foerster & Henrik Oster. 2018. “The role of the circadian clock system in physiology.” European Journal of Physiology. 10.1007/s00424-017-2103-y. P. 8.


“In this study, we examine the relational aspects of reproduction through the character concept, which allow us to explore a broader spectrum of evolutionary reproductive relations.

“The notion of character addresses the units organisms are composed of, which are integrated at different levels of organization. These units include component parts of organisms (such as feathers or limbs, but also molecules and cells), as well as developmental processes and social behaviors. The character concept is a core concept in biology, for it serves a multitude of roles, ranging from identifying cladistic groups and populations for evolutionary studies to serving as a starting point for studying developmental mechanisms. Despite its relevance in systematizing and explaining diversity, the concept of character is underdeveloped and demands further theoretical study. Here, we are interested in conceptualizing reproductive characters, including gametes, gonads, courtship behaviors, incubation methods, or embryo nourishment arrangements.” Cortes-Garcia, David, Arantza Etxeberria & Laura Nuno de la Rosa. 2024. “The evolution of reproductive characters: an organismal-relational approach.” Biology & Philosophy. 39:26. 10.1007/s10539-024-0996-1. p. 2.


“In our proposal, reproductive characters are body parts, activities or behaviors that are integrated into the organism and serve specific reproductive functions by interacting with other characters of the same organism or of other organisms. Two aspects of this definition require further clarification. First, our perspective of functions differs from that of the adaptationist framework. Our standpoint does not accord design functions a central epistemic role in character explanation in the form of ‘character X evolved because it was selected for function Y’. Instead, we introduce a systemic notion of organismal functions emerging from developmental processes and material relations. Hence, reproductive characters are regarded as systemically organized entities, intricately linked in such a way that they contribute to successful reproduction. Second, the relations that we identify as characterizing reproductive characters are of two kinds. Intraorganismal relationality concerns relations among different component parts or processes contributing to the maintenance and functioning of individual organisms across various levels of organization, from gametes to reproductive organs and extraembryonic structures. Interorganismal relationality relates to interactions between individual organisms, including relations between sexual partners for fertilization, and between parents and offspring for successful embryo development.” Cortes-Garcia, David, Arantza Etxeberria & Laura Nuno de la Rosa. 2024. “The evolution of reproductive characters: an organismal-relational approach.” Biology & Philosophy. 39:26. 10.1007/s10539-024-0996-1. p. 7.


“Therefore, the concept of interorganismal trait is genuinely interactive, accounting for the material changes and rearrangements involved in reproductive processes as a result of relational dynamics. For instance, placentas cannot be realized without the interplay of maternal and fetal tissue dynamics. Therefore, the study of interorganismal traits cannot be reduced to their functional aspects nor their morphology, as it concerns the evolution of relations and not of individuals. Furthermore, this shift explains why the evolution of interorganismal traits cannot be reduced to co-evolved pairings, as proposed by the conflict theory. Conventional co-evolution models involve interactions between individuals (such as parent and embryo), which are the ones that are considered to evolve. However, by focusing on the relations themselves, reproductive processes appear as grounded on a series of interactive relations, to which co-evolution models are blind. In this context, reproductive relations giving rise to interorganismal traits resemble symbiotic relations more than antagonistic co-evolutionary dynamics. Thus, interorganismal traits refer to relations embodied in an emerging supra-organismal level of organization that causally affects individuals at the organismal level (i.e., parents and/or embryos).” Cortes-Garcia, David, Arantza Etxeberria & Laura Nuno de la Rosa. 2024. “The evolution of reproductive characters: an organismal-relational approach.” Biology & Philosophy. 39:26. 10.1007/s10539-024-0996-1. p. 9.


“Our classification [using two parameters – incubation and post-fertilization nourishment – yields four classifications for parent-offspring relationality] distinguishes itself from standard approaches in reproductive biology in terms of how classes are defined: within our framework, the distinction between oviparity and viviparity is not a matter of the state of the embryos at the time of partition (i.e., contained in egg coatings vs. free-living individuals), but a consequence of the extension of pre-partition incubation. Accordingly, the traditional criterion used for distinguishing oviparity and viviparity, namely the presence or absence of egg-coatings at release, is understood within our approach as secondary to the evolution of extended periods of internal incubation theorized in terms of parent-offspring relationality. Other common derived traits besides thinning or loss of egg-coatings, such as enhanced water supply and gas exchange, or immune rearrangements, can be identified in clades with increased embryo retention.” Cortes-Garcia, David, Arantza Etxeberria & Laura Nuno de la Rosa. 2024. “The evolution of reproductive characters: an organismal-relational approach.” Biology & Philosophy. 39:26. 10.1007/s10539-024-0996-1. p. 12.


“... the underlying developmental mechanisms of reproductive relations have evolved in a way that confers varying degrees of stability to these relations. This variability in the stability of relational characters helps explain the co-called problem of reversibility, which addresses the apparent constraints associated with reverting from one mode of reproduction to another. The most paradigmatic case is the transition from oviparity to viviparity, which rarely occurs in the opposite direction. From an organismal-relational perspective, this can be explained by the evolution of specializations for stabilized internal incubation and nutritional provision, which involves intricate changes in the anatomy and physiology of both parent and offspring. Those changes condition the relationality between them, ensuring robust developmental control. In contrast, other traits, such as mating behaviors, do not entail such intricate relational changes and, as a consequence, are more labile over evolutionary time. Mating behaviors exhibit greater plasticity, responding to environmental cues, population density, or resource availability.” Cortes-Garcia, David, Arantza Etxeberria & Laura Nuno de la Rosa. 2024. “The evolution of reproductive characters: an organismal-relational approach.” Biology & Philosophy. 39:26. 10.1007/s10539-024-0996-1. p. 15.


“In the late 1960s and early 1970s, homology was extended and applied to molecular genes, identified by their degree of sequence similarity. Homology has also been extended to gene regulatory networks and organismic behaviours. An important category of trait that has received much less attention is that of developmental processes, including gene expression dynamics, morphogenesis and cell differentiation, as well as processes that characterize life cycles, such as metamorphosis or metagenesis. ... we argue that processes can and should be homologized, and this paper explores how this can be done.” DiFrisco, James & Johannes Jaeger. 2021. “Homology of process: developmental dynamics in comparative biology.” Interface Focus. 11:20210007. p. 2.


“There is little point in establishing homology criteria for processes if processes are fully traceable in terms of gene homology. Over the past two decades, however, an increasing amount of evidence has accumulated indicating that evolution at different levels of organization is highly dissociable. Homologous morphological traits are often generated by processes involving non-homologous genes (developmental system drift), while homologous genes are often co-opted in the generation of non-homologous traits (deep homology). We now know that the relationship between evolution at the genotypic and the phenotypic levels is surprisingly fluid, degenerate, multi-level and complex.” DiFrisco, James & Johannes Jaeger. 2021. “Homology of process: developmental dynamics in comparative biology.” Interface Focus. 11:20210007. p. 2.


“Our first example is somitogenesis, the process by which body segments are formed in vertebrates. Following the current literature on the subject, we define this process as involving the posterior growth of a tissue called the paraxial or presomatic mesoderm, and a regulatory network with three functionally distinguishable ‘dynamical modules’: (i) a cell-autonomous oscillator (the ‘segmentation clock’), (ii) cell-cell signalling between neighbouring cells that maintains and/or synchronizes cell-autonomous oscillations across the tissue, and (iii) a graded long-range modulation of the clock period (often called ‘the wavefront’) causing it to slow down and eventually stop at some distance away from the posterior end of the tissue. The coordinated interaction of these dynamical modules results in periodic waves of gene expression travelling ‘up’ the presomitic mesoderm towards the anterior, which results in transient blocks of mesodermal tissue forming sequentially along the antero-posteior axis of the embryo. These blocks are called somites. Different subregions of the somites ultimately give rise to vertebrae, rib cage, skeletal muscle, as well as cartilage and dermis. Somites, as well as the activities of all three dynamical modules, are highly conserved across vertebrates, from fishes to birds to mammals.

“However, the underlying molecular and genetic mechanisms differ in many details.” DiFrisco, James & Johannes Jaeger. 2021. “Homology of process: developmental dynamics in comparative biology.” Interface Focus. 11:20210007. p. 3.


“In vertebrate somitogenesis, the ‘causality horizon’ (the lowest level at which the causes of the same phenotypic feature are conserved) lies at the relatively high level of the dynamics of the process rather than the lower level of the underlying detailed molecular-genetic interactions. Thus, we should ask not only what aspects of the mechanism are conserved, but also what aspects of the activity of the system are conserved (and which ones are not). Stabilizing or positive selection can act on different aspects of the oscillatory dynamics. Also relevant to understanding the conservation of the process are entrenched interdependencies between somites, somitogenesis, and other characters and processes, such as axis elongation and tissue maturation.” DiFrisco, James & Johannes Jaeger. 2021. “Homology of process: developmental dynamics in comparative biology.” Interface Focus. 11:20210007. p. 3.


“The trouble with similarity-based criteria of homology, including shared dynamical properties, is that similarity can arise from convergent evolution rather than common descent. It is for this reason that Remane refined the generic criterion of similarity to similarity in ‘special quality’–structural details of a character not explained by its adaptive role. The more complex those structural details are, the less likely they are to be products of convergent evolution.” DiFrisco, James & Johannes Jaeger. 2021. “Homology of process: developmental dynamics in comparative biology.” Interface Focus. 11:20210007. p. 10; reference: Remane, A. 1952. Die Grundlagen des natuerlichen Systems, der vergleichenden Anatomie and der Phylogenetik. Leipzig: Geest and Portig.


“Nevertheless [despite ignoring evidence of a molecular basis to memory being found in prokaryotes], similar ST [signal transduction] mechanisms appear to be at work, albeit with different leading biochemical actors and different degrees of regulatory complexity. For example, ST in bacterial two-component systems (2CSs) relies principally on histidine kinases for protein phospho-transfer and DNA methylation, while genomic analysis shows that serine/threonine kinases, their analog in eukaryotes, comprise roughly 1 in 4 of the more than 500 protein kinases involved in ST in humans. However, analysis of bacterial genomes increasingly reveals serine/threonine kinases in prokaryotes, particularly those that live in changing environments and display complex social behavior, such as myxobacteria, Bacillus subtilis, and Paenibacillus vortex.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 2.


“Shettleworth defines cognition as ‘the mechanisms by which animals acquire, process, store, and act on information from the environment. These include perception, learning, memory, and decision making.’ Cognition thus is comprised of the total suite of mechanisms that underwrite information acquisition, storage processing and use....

“With this in mind, I extend the definition as follows:

“‘Biological cognition is the complex of sensory and other information-processing mechanisms an organism has for becoming familiar with, valuing, and [interacting with] its environment in order to meet existential goals, the most basic of which are survival, [growth or thriving], and reproduction.’” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. pp. 3, 4; reference: Shettleworth, S.J. 1998. Cognition, Evolution and Behavior. Oxford UP.


“In each cell of E. coli, for example, which is not the smartest proteobacterium on the block (but no dummy, either), there are upward of 10,000 chemoreceptors per cell, each with multiple binding sites, whose output interacts with several flagella, on each of which are about 40 binding sites for CheY-P, the protein that modifies the direction of rotation.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 4.


“Bacteria are capable of sensing and responding to an astonishing variety of environmental signals: amino acids, sugars, oxygen, pH, osmolarity, temperature, light, secondary metabolites, molecular waste products (e.g., ammonia), environmental DNA, and other microbes (both conspecifics and other species)–even physical objects such as tiny, chemically inert beads.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 5.


“Two-component systems [2CSs] segregate the sensor-input and effector/output functions between a sensory kinase (SK), which upon stimulation autophosphorylates, typically at a highly conserved histidine residue, and a response regulator (RR) to which the SK binds and transfers phosphoryl groups, sometimes at multiple sites....

“Because 2CSs have two inherent advantages-multiple possibilities for control and graded signal amplification–they appear to form ‘phospho-relay systems’ with other regulatory components that employ transient phosphorylation as a means of transducing signals within the cell.... Other systems comprise several components. For example, E. coli has a seven-component signaling system for detecting orthophosphate and regulating the genes of the phosphate regulon through the PhoR/PhoB 2CS, a paradigm in bacteria of a membrane-bound receptor controlling cytoplasmic gene expression.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 5.


“The third most abundant ST elements in bacteria belong to the family of sigma (σ) factors [a special sub-class of 2CSs called extracytoplasmic function (ECF) sigma factor proteins], which bind to RNA polymerase to promote genetic transcription, the basic ‘housekeeping’ form of which is present in all RNA polymerase holoenzyme complexes.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 5.


“Autoinduction is the process by which an organism synthesizes a class of molecules, called autoinducers (AIs), which stimulate a change in genetic expression in the organism itself when the molecules reach a threshold concentration....

“The reason autoinduction is important from a cognitive standpoint is twofold. First, it increases the level of complexity of signal integration within the cell, and suggests there is a hierarchy of signaling values based on the concentrations of these molecules. Second, it involves the use of proxies for conditions that cannot be sensed directly, for example, population density or the physical properties of the surroundings. AIs thus may provide a paradigm case of biological ‘information’ that is conventional–deployed with a degree of arbitrariness, as in language–or, in the case of biological systems, evolved via natural selection to have the meaning it does.

“In actual fact, all biological ST systems have this quality of assigned or evolved meanings, even those that directly sense features of the environment, once the signal is transduced into the cell. Protein interactions become what Millikan calls ‘pushmi-pullyu representations,’ which simultaneously transmit what is the case and what to do about it.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. pp. 6, 7; reference: Millikan, R.G. 2004. “On reading signs: some differences between us and the others.” In: Evolution of Communication Systems: A Comparative Approach. Oller, D.K. & U. Griebel (eds). MIT Press. pp. 15-29.


“There is a strong positive correlation between bacterial genome size and the number of different ST proteins a microbe can synthesize, as well as between the complexity of a bacterium’s lifestyle and the number of ST pathways available to support its behavior and physiology. Similarly, ECFs tend to be ‘under-represented and often absent’ in smaller bacterial genomes and ‘over-represented’ in bacteria with more complex lifestyles. Moreover, there appears to be a correlation between the complexity of the signaling pathways a bacterium possesses and the complexity of its behavior, although more research is needed to see if this observation holds generally.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 8.


“According to the microbial signal transduction (MiST2) database, from which all of the following figures are derived, the largest and smallest genomes in the bacterial kingdom are found among the gamma-proteobacteria. The genome of Buchnera aphidocola, a symbiont of aphids, is a mere 400,000 base pairs (0.4 Mbp), which express only three 1CSs and no ECFs. The largest genome, weighing in at an imposing 39.10 Mbp, belongs to Vibrio parahaemolyticus, a pathogenic species that inhabits brackish saltwater and has the capacity to withstand digestion both by seafood and humans. V. parahaemolyticus, about which comparatively little is known, expresses 3,321 ST systems, 79% of which are 1CSs.

“In contrast, consider the delta-proteobacteium M. xanthus, a social predator that inhabits soil, arguably one of the most complex ecosystems on the planet. Potentially the primate of the eubacteria, M. xanthus is renowned for its myriad collective behaviors, including structured, multidimensional swarming motility, pack-like predation, and the use of chemical cues to lure faster-moving prey, as well as a complex developmental sequence leading to fruiting body formation and sporulation. At 9.14 Mbp, the M. xanthus genome is one of the top 20 in size and expresses 687 ST systems, of which more than half are 2CSs. To date no other species, even those with complex ways of life, appears to have such a large proportion of 2CSs systems....

“In short, the sensorimotor activity of M. xanthus is astonishing complex, and understanding this behavior may throw light on the sensorimotor behavior of social animals, such as birds, fish, and insects.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. pp. 8, 10.


“Long-term memory is often indistinguishable from non-associative learning, a type of learning in which ‘presentation of a particular stimulus alters the strength or probability of a response according to the strength and temporal spacing of the stimulus’. Non-associative learning includes sensitization, the amplification of a response following presentation of a stimulus, and habituation, the attenuation or extinction of a response to a stimulus upon repeated presentations. Habituation and sensitization have both been demonstrated in bacterial CT [chemotaxis]–a discovery that ‘gave some neurophysiologists apoplexy, because they believed that a nervous system’ was required....

“Signal amplification is required for learning by neural networks, so a group of Dutch researchers decided to test the idea that autoamplification of genes in certain 2CSs, which further stimulates ST component production, might result in learning effects. Memory storage in animals with nervous systems was long thought to involve a mechanism involving an autophosphorylating protein kinase and paired phosphatase that operate together as a bistable switch.

“The pho regulon of E. coli, which operates to detect the metabolically critical nutrient Pi [orthophosphate] through the canonical PhoR/PhoB 2CS, was selected as the signaling pathway for experimentation....

“Cells from an exponentially growing population of phoA mutants were incubated first in a Pi limited medium (for 45 min at 42̊), then in a high Pi medium (for 1 h at 30̊). Later, the cells were transferred to Pi limited medium and incubated at 30̊. The speed at which alkaline phosphatase production was induced, enabling the cell to scavenge traces of Pi or phosphorylated compounds from the environment, was then measured.

“As hypothesized, the cells previously incubated in the Pi limited medium responded to the new limiting conditions faster than the control cells incubated exclusively in the high Pi medium. The faster response time correlated with the accumulation of ST components, a good probability that the response was the result of operon autoamplificaiton rather than ‘a consequence of unspecific physiological effects.’ Moreover, the learning behavior was ‘mechanistically and effectively different from the adaptation effects observed in CT,’ and appeared to resemble immune system learning.

“Another series of experiments involving B. subtilis are similarly indicative of non-associative learning. Wolf et al selected B. subtilis for its sensitivity to environmental conditions and the well-known mechanisms governing sporulation. These mechanisms, triggered as part of a stress response, exhibit switch-like bistability, the basis of memory effects in computers, some physical compounds (i.e., magnetized iron), and neuronal activity in animals.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 11; reference: Wolf, D.M., L. Fontaine-Bodin, I. Bischofs, G. Price, J. Keasling & A.P. Arkin. 2008. “Memory in microbes: quantifying history-dependent behavior in a bacterium.” PLoS ONE. 3:e1700. 10.1371/journal.pone.0001700.


“Prediction. The ability to anticipate and preemptively respond to regular changes in the environment confers a considerable fitness advantage, and has been observed in bacteria quite apart from circadian periodicity....

“Escherichia coli inhabits several ecological niches during its life cycle, from water, sediment and soil to the mammalian gastrointestinal tract. To determine whether E. coli signaling networks are capable of predictive behavior ‘in a fashion similar to metazoan nervous systems,’ Tagkopoulos et al tested strains under conditions mimicking the transition from the outside world to the gastrointestinal tract of a mammalian host. As they enter the oral cavity cells immediately experience rising temperatures up to 37̊. As they transition to the gut available oxygen drops precipitously to anaerobic conditions. If the homeostatic (sense-respond) framework is correct, E. coli should not repress respiration until a drop in oxygen is detected. On the other hand, if enteric bacteria are capable of dynamic predictive behavior, rising temperatures should induce respiratory repression.

“This is precisely what the studies showed. Exposure to temperature upshift, from 25̊ to 37̊, not only induced the heat shock response regulon, but also strongly repressed genes encoding components of the molecular machinery for aerobic respiration, rapidly reprogramming to anaerobic mode. Similarly, a downshift in temperature (mimicking the organism’s excretion from the host) initiated the return to aerobic respiration.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 12; reference: Tagkopoulos, I., Y.-C. Liu & S. Tavazoie. 2008. “Predictive behavior within microbial genetic networks.” Science. 320:1313-1317. 10.1126/science.1154456.


“Based on these and other findings, Freddolino and Tavazoie assert that the homeostatic paradigm can no longer sustain an appropriate understanding of cellular behavior, whereas a ‘predictive-dynamic framework’ is more explanatory. They conclude that regulatory networks in microbes and neural networks in metazoans have essentially the same function, and that microbiologists are now moving into behavioral territory previously occupied by animals with nervous systems. Whether these also share mechanisms with similar design principles remains to be seen.

“A cautionary note must be sounded, however. Researchers in two of the studies described here [including Tagkopoulos et al 2008] claim their discoveries are demonstrations of associative learning. However, conditioning in these cases is clearly epigenetic. Whether this will count as ‘genuine conditioning’ remains an open question. Also, Tagkopoulos et al claim that one of the implications of their research is that bacteria possess internal models or representations of the environment. The existence of representations and models, except in a metaphorical sense, is still open to debate even in humans, so microbiologists need to be careful.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 13; references: Freddolino, P.L. & S. Tavazoie. 2012. “Beyond homeostasis: a predictive dynamic framework for understanding cellular behavior.” Annu. Rev. Cell Dev. Biol. 28:363-384. 10.1146/annurev-cellbio-092910-154129.; Tagkopoulos, I., Y.-C. Liu & S. Tavazoie. 2008. “Predictive behavior within microbial genetic networks.” Science. 320:1313-1317. 10.1126/science.1154456.


“So how does an individual bacterium integrate the information from a dizzying array of signaling pathways–sensorimotor, physiological, chemosensory, and communicative–into a coherent adaptive response? The short answer is we don’t know. This is perhaps the greatest challenge facing work in this field. We have already seen how cross talk can link substantially different signaling pathways (heat shock and respiration) to enable cells to predict regular changes in their habitat, but the mechanism is unknown.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 13.


“At present prokaryotes offer very few examples of specialized information-processing ‘organs’ for investigation. The chemosensing receptor clusters at the leading pole of flagellated bacteria such as E. coli have already been suggested as analogous in important respects to neural clusters in metazoans with nervous systems. With an estimated 10,000 receptors in each cluster, with five different sensory targets and multiple binding sites, the computational complexity of the nanobrain is not especially tractable, however. A second candidate involved in sensorimotor coordination are the FACs [focal adhesion clusters] on the sides of M. xanthus together with the protein complex for transducing signals from the cell surface to the oscillator-regulator located in the cytosol. A third candidate organ for specialized information-processing in bacteria in the ‘stressosome,’ a large (multimillion dalton) protein complex associated with the general stress response that integrates multiple signals into a single outcome, and is best characterized in B. subtilis, although it is found [in?] many microbial phyla.” Lyon, Pamela. 2015. “The cognitive cell: bacterial behavior reconsidered.” Frontiers in Microbiology. 6:264. 10.3389/fmicb.2015.00264. p. 14.


“... we suggest that there have been at least five different major transitions, reflected in the presence of five different types of computational architecture across animal brains....

“The first transition is from the lack of a nervous system to simple decentralized computational architectures....

“In the simple bilaterian phyla such as the Nematoda, Tardigrada and Platyhelminthes we recognize a second transition to a centralized computational architecture. Here there is a distinction between a central and peripheral nervous system....

“Insect brains have a control flow that is as much feedback as it is feed-forward; hence we recognize a third transition to a recurrent computational architecture. Recurrent computational architecture allows the output of a process to be fed back to influence and control the operation of earlier processes. In the insect brain information flow iterates through the modules of the brain in the process of action selection. This transforms the space of possible cognitive capacities. In the insect brain representations can reverberate, thereby remaining active and influential in the network over time. Reverberation enables new types of working memory, which in turn supports the learning of relationships between stimuli separated in time. A wider range of relationships can be recognized and learned, including learning of simple ‘abstract’ relationships. Coupled fast scan and slower fixation systems can operate on the same information supporting forms of selective attention. Additionally, the output of a sensorimotor transformation can be fed back into the system allowing use of an efference copy to cancel out the consequences of the movement of the sensory systems, as well as elementary forms of forward modeling of the consequences of choices and actions.

“In the vertebrates, and cephalopod gastropods, we recognize a fourth transition to laminated computational architecture. In laminated systems, the control flow operates in parallel but interacting recurrent subsystems....

“Lamination also makes possible distinct use-independent decoupled representations, which in turn facilitates the use of the same representation for distinct purposes (multiplexing). Note that the advantages of multiplexing are not straightforward. Multiplexing is a very efficient use of neural representations, but it comes at the risk of crosstalk and interference if more than one process is making simultaneous demands on the same representation. Multiplexing then imposes a limit on multitasking....

“Lamination also increases evolvability. The presence of multiple independent processing pathways allows for a degree of redundancy and degeneracy that allows for new cortical functions to evolve. In a laminated system, multiple degenerate pathways are involved in the same process. It is possible for pathways to diverge and adopt different functions, through either neuroplasticity or random evolutionary processes, without compromising the original functions of the system....

“The final transition we recognize is a movement to an architecture capable of reflection. In computer science, ‘reflection’ refers to the ability of a program to access and modify its own source code. This allows a computational architecture to modify its architecture and control flow according to task need.” Barron, Andrew B., Marta Halina & Colin Klein. 2023. “Transitions in cognitive evolution.” Proceedings of the Royal Society: B. 290:20230671. 10.1098/rspb.2023.0671. pp. 3, 4-5.


“We believe that the study of cognition is on the cusp of a seismic shift similar to the Copernican and Wegnerian revolutions....

“Understanding behaviour in all its forms will require a dramatic shift in perspective. The result, however, should be a potentially productive cross-fertilization of the life sciences and the cognitive sciences that could help to solve major problems in both domains, which currently barely reference one another.” Lyon, Pamela, Fred Keijzer, Detlev Arendt & Michael Levin. 2021. “Reframing cognition: getting down to biological basics.” Philosophical Transactions of the Royal Society: B. 376:20190750. 10.1098/rstb.2029.0750. p. 2.


“What do we mean by ‘cognition’? The short answer is we don’t know because we can’t agree, and unambiguous, biologically grounded proposals are effectively non-existent. Cognition–and where it is found in the natural world–has been an inexhaustably meaty bone of contention since (in ‘western’ culture) Aristotle, for whom animals marked a singular transition, and (in ‘eastern’ culture) the Rig Veda and associated texts, many of which admitted cognition in plants.” Lyon, Pamela, Fred Keijzer, Detlev Arendt & Michael Levin. 2021. “Reframing cognition: getting down to biological basics.” Philosophical Transactions of the Royal Society: B. 376:20190750. 10.1098/rstb.2029.0750. p. 4.


“From our perspective, however, the definition [by Sara Shettleworth that was centered on animals’ using and processing information from the environment] is not ideal. Importantly, it fails to specify the adaptive value these mechanisms have in the functional economy of the organism: what cognition thus defined does for the organism. Additionally, ‘information’ does all the conceptual heavy lifting yet remains uncharacterized. This is not unusual, far from it, but the emphasis on information specifically from the environment is misleading. Information from the external milieu only ever makes sense in relation to the state of the cognizing system: under-nourished, starving, sated, reproductive, dormant, acting alone, acting with others, experienced, naive, threatened, secure and so on. Moreover, the cognizing system is never a passive recipient of input from the environment, but is ever and always endogenously active.” Lyon, Pamela, Fred Keijzer, Detlev Arendt & Michael Levin. 2021. “Reframing cognition: getting down to biological basics.” Philosophical Transactions of the Royal Society: B. 376:20190750. 10.1098/rstb.2029.0750. p. 4.


“Taking Shettleworth as the jumping-off point, we offer the following working definition [of cognition]:

“Cognition comprises the sensory and other information-processing mechanisms an organism has for becoming familiar with, valuing, and interacting productively with features of its environment (exploring, exploiting, evading) in order to meet existential needs, the most basic of which are survival/persistence, growth/thriving, and reproduction....

“Information remains uncharacterized but can be explicated in the biological context, as follows:

“A state of affairs is information for an organism if it triggers a change in physiology or behaviour relative to that state of affairs. Whatever state of affairs induces a change in physiology or interactive potential in an organism is information for that organism.” Lyon, Pamela, Fred Keijzer, Detlev Arendt & Michael Levin. 2021. “Reframing cognition: getting down to biological basics.” Philosophical Transactions of the Royal Society: B. 376:20190750. 10.1098/rstb.2029.0750. p. 4; reference: Shettleworth, S.J. 1998. Cognition, Evolution and Behavior. Oxford UP.


“The community we speak of is called Reclaiming. We are part of the larger movement, called feminist spirituality, that critiques the patterns of domination embedded in patriarchal religions and reenvisions a spirituality that can liberate women and men. For us, that new vision is rooted in the Goddess, the earth being who embodies the cycles of birth, growth, death, decay, and renewal in nature and in our human lives.

“We are Pagans: we practice an earth-based spirituality rooted in respect for nature. We are Witches: our roots are in the initiatory Goddess traditions that arose in Europe and the Middle East, although our practice is strongly shaped by the multicultural traditions of this land....

“Feminist spirituality, Goddess religion, Paganism, earth-based spirituality, and Witchcraft are like circles that overlap in many areas and retain some distinct differences.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. xvi.


“A queen makes an ill wish: she would trade her twelve strong sons for one daughter. The girl is born; the sons are transformed into swans and fly away. Rose grows up in ignorance of their existence but with a gnawing sense of something amiss. When she finally learns the fate of her brothers, she decides she must find them and save them. And so she leaves the castle and sets out on her quest....

“The queen wishes for a daughter who will embody the Goddess herself, the full cycle of birth, growth, death, and rebirth. Our queen-priestess needs an heir, someone to whom she can pass on her power and knowledge of the mysteries unique to women. We might think that twelve fine, strong sons would be enough for any woman, but without a daughter, the cycle is not complete....

“The queen makes an ill wish. She would trade all twelve of her sons for a daughter. In the manner of fairy tales, her wish is granted....

“The Old Woman who appears is the Crone incarnate, guide and teacher who practices ‘tough love.’ She teaches not by imposing punishments but by making us face the consequences of our actions....

“Rose senses that something is wrong, something is missing. She doesn’t know what, but she knows that her world is not complete. her distress, her uneasiness, is the beginning of her quest.
An initiation journey often begins with the perception that something is wrong....

“So Rose asks uncomfortable questions, until finally she is answered with the truth. In this she functions as a model feminist heroine. But as soon as she learns the truth, she accepts responsibility for restoring her brothers. While most of us, faced with her situation, would weep, cry, engage a therapist, or form a support group for Adult Siblings of Avians, Rose simply determines to rectify the situation.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 5-6.


“We live today in a castle that has expelled many wild swans, many values that might open the heart to the wild and take us soaring on the wind. The work of this beginning chapter is, first, to recognize that something beyond the castle exists–that something, someone, is missing. We must be willing to keep asking questions until we find out what or who that is. If we choose to take on the task of healing, we will need the skills of magic, which can open a doorway in the walls that enclose us.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 8.


“The elders and wise women and men of the ancestral villages had special spiritual responsibilities. It was their job to keep peace between the people and the local spirits that held the power of weather patterns and plant and animal lives. The harmony between the visible and invisible worlds needed care and attention....

“The midwives and healers, the smiths, the poets and storytellers all had their roles to play in keeping the balance between the people and Mother Nature. When something slipped out of alignment, it had to be bent and woven back into the flow and harmony of nature. Knowledge of how to do that bending and weaving was the province of the wise–the art and craft of magic. Wicca, Witchcraft, Witch. These words come from the same roots as wicker, as in wicker furniture, which is made of willow twigs woven and bent together into a pattern.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 9.


“We drive the car, answer the phone, write checks with a logical, verbal, task-oriented, ‘grown-up’ part of ourselves that in Reclaiming tradition we call Talking Self. When we fall asleep, Talking Self falls asleep, too. But we are still conscious in some way, and sometimes we can remember a dream world of vivid sensation, powerful emotion, and a logic wholly unlike that of waking life, a dream world inhabited by Younger Self....

“So magic is the art of communicating with Younger Self intentionally in ritual, while awake, rather than waiting for a nightmare, accident, or illness to force us to pay attention. Younger Self may have known for years that a certain job wasn’t right for us, but Talking Self may not know until carpal tunnel syndrome sets in....

“For in Reclaiming tradition, the way to Deep Self lies through Younger Self. Deep Self is the part of us that is directly connected to, or even part of, the Goddess....

“Deep Self can be directly felt by Younger Self but not by Talking Self.

“So in order to recapture the simple, reliable presence of a divine power that can heal any hurt and bring a sad and sick world to rights, we have to learn to release the narrowness and prejudice of Talking Self, who has long believed that magic isn’t real.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 11-12.


“In Reclaiming, when we seek healing we often work with fairy stories. These stories promise, over and over, that if we set off on the path with nothing but courage, determination, and a kind heart, we will reach our goals no matter how impossible they may seem. The stories promise that in return for our greatheartedness, the universe will provide miraculous assistance that can bring about huge, impossible changes for the better. They are full of hope....

“The stories don’t recommend a spiritual way of life; instead, they assume a spiritual way of life.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 25-6.


“As Witches, we work with dream and story symbols in a different way. We don’t take them apart and analyze them for the benefit of Talking Self, any more than we would cut up a favorite pet to see how it worked....

“Instead of breaking down the symbols in a story, we try to let the symbols build and become even more detailed and mysterious. We engage Younger Self in sacred space and let our own most personal memories and associations crystallize onto the template of the story. We encourage the fairy story to apply more and more intimately to our own story, until it casts a new light on our old troubles, like sunrise through stained glass.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 28.


“In our fairy tale, Rose sets off on a quest to redeem her brothers. Something is very wrong in her world, and she decides to change it. In doing so, she becomes a priestess, one who takes on responsibility not just for her own spiritual well-being, but for the well-being of others.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 37-8.


“Rose leaves the castle and sets out to find her brothers. Not knowing where she is going, she wanders in the wilderness. She meets an Old Woman, and to her Rose gives half of her meager store of bread. ‘Follow the river to its end,’ the Old Woman advises, ‘and there you may meet your brothers’....

“On an initiatory journey, we’re not given a map....

“An initiatory journey is also a creative process, and every work of creation requires a period of wandering in the wilderness: juggling ideas and possibilities, doodling on the blank page, scribbling draft after draft only to discard them. Life works the same way.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 57.


“Rose reaches the river’s mouth, and there she finds her brothers. They are swans by day, but at night reassume the form of men. When the brothers realize who she is, they weep and wail, because they have made a vow to kill the first young girl they meet as a revenge for their misfortune. But the Old Woman appears again, to say, ‘Break that wicked vow, which you never should have made!’ Relieved and happy, the brothers agree.

“The brothers are undergoing their own initiatory journey. Swans by day and men by night, they live on the boundary between the human and the wild. They are shamans, who mediate between the human and nonhuman realms.

“In one sense, the brothers are entrapped in a divine possession. The Bird Goddess is one of the most ancient forms of the Goddess. Birds fly between earth and sky, linking vision to grounding. Water birds such as swans also link the life-giving waters to sky and land. Their long necks remind us of snakes, another ancient symbol of the Goddess of rebirth and regeneration....

“Their vow is ‘wicked,’ a word that comes from the Anglo-Saxon root wic- or weg-, related to Witch. Wic- means to bend or twist. Willow branches are pliable and can be twisted into ‘wicker’ baskets. Just as we can bend and change reality to create healing, so se can, through ignorance or fear, twist fate in the other direction, away from healing and balance....

“Revenge is not true power. To become empowered, we must acknowledge and relinquish that part of each of us that wants to get even. We cannot truly restore balance by equalizing the pain; we must undertake the longer and more difficult journey of healing....

“In general in our society, men are preferred over women. In this story that pattern is reversed. Rose, the daughter, is preferred over her brothers. She is the most loved and privileged one. Her task is what women ask of men, what all oppressed groups from time to time demand of those who have been their oppressors: to hear the rage and the pain, to witness without personalizing or defending, without needing to affirm that men have been oppressed too, or that rich people also have their problems. She is not asked to let herself be killed, but simply to hear that her brothers long to kill her.

“At this moment, the old woman appears. In a triumph of common sense she tells the brothers to simply break their vow. We’re not caught here in a Greek tragedy, where vows and prophecies work their destruction in spite of human will and intention. We’re in a different realm, where freedom is a possibility, where the power we’ve already gathered on our magical journey allows us, if we choose, to break the negative patterns of the past.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 99, 100.


“The brothers weave a basket to carry Rose with them in their journey across the sea, a journey that can be made only on the two longest days of the year–those that flank Midsummer Night. Resting one night on a tiny island in the sea, they fly to the magic land ruled by the dark fairy, the Fata Morgana.....

“The brothers are shamans, Witches. Not only can they fly to the realm of magic; they can carry Rose there. To create a ritual is to weave a basket, a container, in which we can be carried away to realms of magic and ecstasy....

“Up until now, Rose has followed her call, her intuition, her river. Now she must simply let go and allow herself to be carried away. In any initiation, there comes a moment when the initiate must give over control.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 145.


“Rose receives her task from the Fata Morgana: she must gather stinging nettles, beat out the fibers, spin the thread, weave the cloth, and sew twelve shirts–all without speaking, laughing, or crying out loud. When the twelve shirts are complete, she must cast them over the brothers, who will then be restored to full humanness.

“Rose has already faced many challenges, but in this section of the story she is given her true task. Her courage, generosity, perseverance, and willingness to surrender to ecstasy have proved her worthiness to undertake a great work of magic, and the task itself gives us a clue to the nature of the Goddess who is initiating her: the Weaver, ancestress of the Fates, who spin the thread of our lives, measure its length, and cut the cord at death.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 187.


“To know, to will, to dare, and to keep silent are the four powers of the mage in Western occult tradition. Rose’s initiation journey encompasses all four. First she must know the truth about her brothers. She must dare to leave her home and wander in the wilderness. Now she must learn to work with will, with focused concentration and intention, and with silence to complete her task. The work of this section of the story is to know the elements and plants that are our allies, to discover our true tasks and life purpose, and to learn the power of silence.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. p. 189.


“While Rose is spinning her thread outside the green cave one day, the king of the country rides by and, struck with her beauty, marries her. She bears a child, but his jealous mother steals the child and marks Rose’s mouth with blood, accusing her of being a Witch and of eating her baby. She bears a second child, only to have the acts repeated.

“Rose has learned to hold her focus in spite of pain and frustration. Now she must continue her concentration through love and loss. Rose is asked to live with the dedication of a priestess, but not to live as a hermit or an ascetic. She is able to love and be loved, to marry, to bear a child–all the life transitions that often distract us from our inner development and chosen tasks. But Rose keeps on weaving.

“Her weaving is her center, her magical intention. by holding to her intention, Rose stays centered and withstands all the projections thrown at her, even her mother-in-law’s jealousy.

“Jealousy is a powerful force in fairy tales. Almost universally, it serves as the negative motivation and greatest threat in these old stories. Jealousy is a primal emotion: dogs feel it; young children certainly feel it. In small communities, people often go to great lengths to avoid evoking the jealousy of their neighbors, and negative magic is almost always perceived as motivated by jealousy.

“A person of power must be able to withstand jealousy. A weaver of souls must be able to focus on the work without being swayed by other people’s perceptions, whether idolization or vilification....

“The work of this section is to know center: the center of the circle, the center of the ritual, the center of self that allows us to withstand projections and sustain our healing work.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 229, 230.


“The citizens ready a pyre to burn the young queen alive. Rose cannot speak to defend herself but keeps sewing and sewing. As she is tied to the stake there is a rush of air, and her twelve swan brothers beat out the flames with their wings. She throws the shirts over them, and all are transformed into men. The Old Woman appears one last time, holding the unharmed babies. But Rose has not yet completed the last arm of the last shirt. Her brothers embrace her, but the youngest does so with one human arm and one swan’s wing.

“The culmination of an initiation is a symbolic death and rebirth. Rose, having faced down jealousy and survived both love and loss, now faces death. She must remain focused on her task in spite of ear, weaving even in the dungeon.

“As we gather power, we must also face our fears of being a Witch, our fears of death, our fears of the consequences of taking action....

“In some versions of this tale, Rose literally dies at the stake but is revived and brought back to life. The old Rose, the child, the seeker, the patient worker at an impossible task, is dead. A new Rose is born who has completed her work....

“One sleeve still remains to be finished; one brother is left with one swan’s wing. The others have been fully brought back into human community, but the youngest is still marked by the wild. Unable either to fly or to perform many human tasks, he is caught, half and half.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp.276, 277.


“The One-Winged Brother [presumably a imagined continuation of the tale involving the youngest brother’s fate]

“You haunt the castle. When the feasting is over, when your sister has returned to the cares of her husband and her children, when your brothers ride out to hunt, you wander alone through empty rooms, your useless wing dragging at your side. Half of you is human, but half of you is still made for flight, and you yearn to soar on the updrafts and glide on the great currents, suspended, hovering, free. But you are earthbound, trapped. You will never fly again. And you will never ride with your brothers, for your wing catches in the trees and drags upon the ground. The maidens who chase after your brothers laugh at you, their hands covering their mouths to hide their mockery. And you cannot defend yourself. Your sister’s voice is restored, but now you are doomed to silence, mute as a swan.

“And when the loneliness becomes too much to bear, you steal away one night, to the cave where you lived so many years with your brothers, and with Rose always gathering, spinning, weaving. The cave is empty, silent, lonely, and you wander down to the shore. A small boat awaits you ....

“Lie down. See the stars above you. Feel the breeze that you once could ride to beyond the ends of the earth. Reach for it with all the longing for freedom within you, and raise your wing to catch the wind. Your wing becomes a sail; feel the wind push against it and the boat glide through the water, almost as swiftly as once you could sail through air....

“Until at last the boat comes to shore. Feel the bow scrape the sand; feel it move beneath you as you stand and step out and pull it up onshore after you. A mountain rises above you, a black shape against the pattern of bright stars n the sky. A path leads up the mountain, and you follow, beginning to climb.... But now, step by step, you climb. The breath rasps in your mute throat; the night is filled with voices, the calls of night birds, frogs, beasts. All have a voice, but you are only a mute not-swan, not-human thing, climbing and climbing, the only sound you make the rasping of your breath, while around you the night chorus sings the song of the wild.

“At last you reach the top. You pause for breath; you lay back and look up at the stars and cover yourself with your wing like a shroud. The stars are bright eyes in the night. Your ears are filled with the land’s voice, and even the stars begin to sing–high, bright notes like bells that ring and ring through all the space that separates you....

“Breathe in; breathe out. No sound can force its way past your throat. Stop trying. Just breathe. And listen.

“Listen deeply to the birds. Listen deeply to the frogs. Listen to the cat sounds, the howl of the coyote, the murmur of a stream, the stars singing. It all moves to the same rhythm, it all sings in a harmony that begins to fill you, until your body glows with, and your wing shines pearly and iridescent as the moon....

“The wild is within you; you are its voice. You will never belong fully to the castle, the realm of humans, for yours is a different task; to be the voice of the land, the stars, the wild things, to speak for them in the councils of the castle, to live on the borderlands, not one thing or another but always moving between, shaman, magician, Witch....

[After getting back in the boat and returning to the mainland]

“The cave stands above you. The castle awaits you. You will never be fully human, never wholly swan, but you are something more and less than both: the translator, the one who knows the language of birds and interprets the wind, the constant reminder in human halls that to be human is not all.” Starhawk & Hilary Valentine. 2000. The Twelve Wild Swans: A Journey to the Realm of Magic, Healing, and Action. HarperOne. pp. 312, 313, 314.


“The most sensible course of action for an organism does not simply follow from logical rules of inference. Before it can even use such rules, the organism must tackle the problem of relevance. It must turn ill-defined problems into well-defined ones, turn semantics into syntax. This ability to realize relevance is present in all organisms, from bacteria to humans. It lies at the root of organismic agency, cognition, and consciousness, arising from the particular autopoietic, anticipatory, and adaptive organization of living beings. In this article, we show that the process of relevance realization is beyond formalization. It cannot be captured completely by algorithmic approaches. This implies that organismic agency (and hence cognition as well as consciousness) are at heart not computational in nature.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 1.


“... algorithms cannot identify or solve problems that are not precoded (explicitly or implicitly) by the rules that characterize their small world. In such a world, everything and nothing is relevant at the same time.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 2.


“Before they can ‘infer’ anything, living beings must first turn ill-defined problems into well-defined ones, transform large worlds into small, translate intangible semantics into formalized syntax (defined as the rule-based processing of symbols free of contingent, vague, and ambiguous external referents). And they must do this incessantly; it is a defining feature of their mode of existence.

“This process is called relevance realization.... Indeed, we could say that an organism actively brings forth a whole world of meaning.

“In this article, we shall argue that the ability to realize relevance–to bring forth a world–is present in all organisms, from the simplest bacteria to the most sophisticated human beings.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 2.


“All of this involves a radically context-dependent generative dialectic called opponent processing–the continual establishment of trade-offs and synergies between competing and complementary organismic behaviors and dynamics. Such competing and synergizing processes also mediate an organism’s interactions with its living and non-living surroundings, interactions that are inherently and irreducibly semantic, in the sense of having value (i.e., relevance) for the organism as a unity which strives to persist in the face of the constant threat of decay.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 2.


“The theory of computation was intended as a model of specific human activities, not a model of the brain or physical reality in general. Consequently, assuming that the brain or the world in general is a computer means committing a category mistake called the equivalence fallacy. Treating the world as computation imputes symbolic (information) content onto physical processes that is only really present in our simulations, not in the physical processes that we model.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 4.


“We can define natural agency in its broadest sense as the capability of a living system to initiate actions according to its own internal norms. This capability arises from the peculiar self-referential and hierarchical causal regime that underlies the self-manufacturing organization of living matter.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 4.


“This [the organism’s enacting or bringing forth its own world of meaning] grounds the process of relevance realization in a constantly changing and evolving agent-arena relationship, where ‘arena’ designates the situated and task-relevant portion of the larger experienced environment.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 5.


“For problem solving to be tractable under real-world constraints, agents must rely on heuristics, make-shift solutions that are far from perfect. Unlike algorithms (strictly defined), they are not guaranteed to converge toward a correct solution of a well-posed problem in finite time. Still, heuristics are tried and tested to work well enough in a range of situations which the agent or its ancestors have encountered in the past, or which the agent deems in some way analogous to such past experiences.

“This notion of bounded rationality.... ... reflecting the notion of embodied bounded rationality, or evolved embodied heuristics....

“Yet, they [evolved embodied heuristics] leave one central issue untouched: how to link the use of specific heuristics to the identification of underlying relevant cues. The problem of relevance thus persists.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. pp. 5, 6.


“How, then, are we to understand relevance realization if not in terms of formal problem solving? One possibility is through an economic perspective, which frames the problem of relevance based on commitment, i.e., the dynamic allocation of resources by an agent to the pursuit of a range of potentially conflicting or competing goals. Opponent processing is seen as a meta-heuristic approach: the agent employs a number of complementary or even antagonistic heuristics that are played against each other in the presence of different kinds of challenges and trade-offs. The trade-offs involved can be subsumed under the general opposition of efficiency vs. resilience or, more specifically, as generality vs. specialization, exploration vs. exploitation, and focusing vs. diversifying....

“Such high-level adaptive dynamics can be embedded in a physical context through the notion of predictive processing. Predictive processing means that an agent iteratively and recursively evaluates the relevance of its sensory input through the estimation of prediction errors. It does this by measuring the discrepancy between expectations based on its internal models of the world and the sensory feedback it receives from its interactions within its current arena. Higher weights are assigned to input with low prediction errors, while perceptions with persistent larger errors are preferentially discounted. Particular importance is attributed to error dynamics, the selection of actions and cognitive strategies that rapidly reduce prediction errors in a particular stream of sensory input. Predictive processing can ground the economic account of relevance realization by connecting it to the underlying perceptual and cognitive processes that account for the dynamic and recurrent weighing of prediction errors.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 7.


“Constraints arise through the interactions between the component processes that make up the living system. Like the underlying flows, they are dynamic, but change at different time scales. Constraints can thus be formally described as boundary conditions imposed on the underlying dynamics. They decrease the degrees of freedom of the living system as a consequence of the restrictions that are placed upon it by the organized interactions of its constituent processes. An enzyme is a good example of a constraint: it alters the kinetics of a biochemical reaction without itself being altered in the process.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 8


“Evidently, organizational closure is causally circular: it is a form of self-constraint. In this way, the organization of the system becomes the cause of its own relative stability: this is what equips an organism with identity and individuality.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 8.


“Rosen’s central insight is that his (M,R)-system models are open to material (and energy) flows but are closed to efficient causation. This is a form of organizational closure, meaning that each processor has as efficient cause another processor within the organization of the system. Formally, each processor must be part of a hierarchical cycle of efficient causation. Such cycles represent a type of self-referential circularity that Rosen calls immanent causation, which represents more than mere cybernetic feedback, the latter being restricted to material causes (i.e., hierarchically ‘flat’) and only generating circular material flows. Hierarchical cycles, in contrast, consist of nested cycles of interacting processors that preserve their own pattern of interrelations over multiple scales of space and time....

“As a consequence of this hierarchical circularity, efficient cause coincides with final cause in living systems. This is precisely what is meant by autopoiesis or self-manufacture: the primary and most fundamental goal of an organism is to keep on producing itself. Biological organization is intrinsically and unavoidably teleological in this specific and well-defined sense.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 9.


“Even though the environment is a necessary condition for existence, an organism does not behave in a purely reactive manner with regard to external inputs. Instead, future stages of the system are dynamically presupposed by its own inherent organization at earlier points in time. This is exactly what we mean when we say an organism is its own final cause.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 10.


“The interactive dimension of natural agency is also called adaptive agency, because it is concerned with how an organism, once it has achieved basic self-manufacture, can adaptively regulate its state in response to its environment.... Agency is not only an organizational, but also an ecological phenomenon. It is as much about the relations of the agent to its arena, as it is about internal self-manufacture.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 11.


“What we have so far are three different dialectic processes, at three different levels of organization:

“1. the process of autopoiesis (self-manufacture)–internal to the organism, established through collective co-constitution of macromolecular biosynthesis....
“2. the process of anticipation–internal to the organism, but projective (about the environment)....
“3. the process of integrated adaptation–transjective (grounded in the relation between agent and arena), established through collective co-constitution of the intrinsic goals, repertoires of action, and affordance landscapes of an organism-environment system–which amounts to relevance realization in its broadest evolutionary sense, a continuous tightening of the agent-arena relationship and hence the organism’s grip on reality’.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 15.


“This, in a nutshell, summarizes the account of life we present here: agential emergentism. It shows parallels to enactivism in cognitive research, with its conception of life as adaptive sense-making. Seen from this perspective, relevance realization offers itself as the unifying core activity that allows agents to delimit and thereby enact their arena, the part of their large world that matters to them, that enables them to survive and thrive.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 15:1362658. 10.3389/fpsyg.2024.1362658. p. 18.


“Nevertheless, higher plants enjoy several features that were originally considered specific only for animals, including sexuality, immunity, self-non-self and kin recognition, goal-directed behaviour based on plant-specific cognition and communication, as well as on intelligence and sociality. These surprising animal-like features of higher plants result from the convergent evolution of flowering plants and animals. For example, mammals and flowering plants emerged some 180-130 MA ago and since then have been co-evolving at several levels.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. p. 1.


“Plants represent unique multicellular organisms as they not only have both autotrophic and heterotrophic organs, tissues and cells, but they also live in two contrasting environments: an underground pedosphere and an above-ground atmosphere.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. p. 1.


“Obviously, roots live in a much more friendly and stable environment compared to shoots. Moreover, roots are much more active in their social aspects as they engage in several intracellular symbiotic relations, as we will discuss in more detail below, with bacteria and fungi.... The upper-lower duality is inherently connected to a duality of the mode of existence as the aboveground part is autotrophic whereas the underground root part is heterotrophic, resembling fungi and animals....

“Importantly, a large part of the aboveground organs in mature plants are also heterotrophic as only a subset of the cells are photosynthetically active, primarily the mesophyll cells of leaves.... Most epidermal cells as well as all the cells of the vascular systems of leaves and stems, and many cells of flowers, are heterotrophic. Altogether, a significant amount of the cells in shoots are heterotrophic. The underground roots are completely heterotrophic....

“Importantly, the number of heterotrophic cells of any mature plant body outnumbers those of photosynthetic cells.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. pp. 1-2.


“Although these vascular systems [xylem and phloem] are studied mostly with respect to transport of solutes, minerals, hormones and other signals, as well as photosynthates, it is well known that vascular systems also allow rapid long-distance signalling and communication via electric, hydraulic and ROS/calcium waves. The phloem is especially relevant in this respect as it is one huge and ramified cable-like compartment spanning the whole plant body, connecting all plant organs into one unified huge axon-like super-cell. Intriguingly in this respect, phloem tubes provide a low-resistance medium allowing rapid spreading of plant-specific AP [action potential] throughout the plant body. This rapid electric signalling integrates the whole plant body into physiological and cognitive unity, allowing vascular plants to act as individualities having both plant-specific agency and cognition.

“We are not discussing plasmodesmata here, due to the tight space limitation, but these plant-specific direct cell-cell channels are very relevant for integrating plant tissues and organs into coherent plant bodies. They contribute to the spreading of plant-specific APs through the whole plant body. In contrast with APs in animals and humans, plant APs are not based on sodium ions, which are toxic to the pectinic plant cell walls, but on calcium ions. Nevertheless, APs in plants and animals have similar bio-electrical, cellular and communicative features and both evolved from very ancient membrane repair processes of early eukaryotic cells.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. p. 3.


“Numerous discoveries and findings support the Darwinian view of roots as acting in a social and cognitive manner controlling numerous microorganisms in the rhizosphere via chemical inter-kingdom communication and entering into symbioses with fungi and bacteria.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. p. 6.


“
Although many still consider plants as semi-living automata, evidence that vascular plants are cognitive, communicative and intelligent organisms is accumulating and new data are overwhelming.” Baluska, Frantisek & Stefano Mancuso. 2021. “Individuality, self and sociality of vascular plants.” Philosophical Transactions of the Royal Society: B. 376:20190760. 10.1098/rstb.2019.0760. p. 6.


“Learning is defined as a process leading to an experience-dependent behavioural response of a system. It requires that:

“(i) A sensory stimulus that originates either from the activities of the system or from the external biotic or abiotic world leads to a change in the internal state of the system (the stimulus is encoded).
“(ii) A memory trace of this change is retained (storage); retention requires active stabilization and involves valence mechanisms of positive or negative reinforcement.
“(iii) Future interactions with the stimulus or associated stimuli led to a change in the threshold of the behavioural response (recall).” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 2.


“Four major types of evolutionary transitions have been recognized:

“(i) Ecological transitions (e.g. from aquatic life to terrestrial life), which involve new integrated suites of physiological and morphological adaptations.
“(ii) Transitions that involve additions to the type of hierarchically nested variations that are selected–selections among genes; among genes and behaviours; among genes, behaviours and virtual non-symbolic representations (e.g. action plans); and among genes; behaviours, virtual representations and symbolic-cultural representations. Following Dennett, we call these transitions in intentionality.
“(iii) Hierarchically nested teleological transitions–from non-living to living systems, from non-sentient organisms to sentient ones and from non-reflective animals to reflective-rational ones.
“(iv) Informational transitions, which, as suggested by Maynard Smith & Szathmary, involve changes in the acquisition, encoding, storage and transmission of information that lead to higher-levels entities with greater division of labour and new levels of hierarchical control. Such changes include either increase in nested hierarchy (such as the transition from single cells to multicellular organisms made up of cells) or the addition of a new way of storing and using information (such as the transition from RNA as hereditary material and enzyme to DNA as hereditary material and proteins as enzymes). Both types of transition entail the addition of new and higher levels of information integration and top-down control within the individual.

“Neural transitions
“Maynard Smith & Szathmary focused on the transmission of information between generations that is determined by the genetic inheritance system, so the transition to neural organisms, which epitomizes a new way of transmitting information within an individual animal, was overlooked.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. pp. 2-3; reference: Dennett, D.C. 1995. Darwin’s dangerous idea: evolution and the meanings of life. NY: Simon and Schuster; Maynard Smith, J. & E. Szathmary. 1995. The Major Transitions in Evolution. Oxford UP.


“We recognize five major neural transitions, with the first two, on which we expand, occurring in phylogenetically early animals. The five transitions are: (i) the transition from non-neural to neural organisms that learn by neural habituation and sensitization; (ii) the transition to animals with a central nervous system (CNS) and flexible but limited associative learning (LAL); (iii) the transition to animals with open-ended (unlimited associative learning, with hierarchically organized brains enabling mental representations (subjectively experienced mappings of world, body and prospective actions); (iv) the transition to imaginative animals with additional hierarchical levels of neural and mental representations; and (v) the transition to symbolizing, culturally learning humans.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 3.


“The nervous system’s coordinating functions are enabled mainly by the plasticity conferred by the evolution of novel types of developmental exploration-stabilization processes. Exploration-stabilization processes are manifest at all levels of biological organization and are based on a common principle–the generation of variations from which only a subset is eventually stabilized (selected). Examples are the selection of genetic mutations in populations; selective stabilization of biochemical networks within a cell; developmental selection processes that occur during ontogeny in plants and animals and lead to homeorhesis; stabilization of exploratory motor behaviours. In all cases, variations that confer benefits, or, more generally, that lead to a set-point (an attractor state) are stabilized/selected. As the nervous system evolved, new levels of developmental selection were introduced: in addition to selection among neurons during embryogenesis, differential stabilization of synaptic connections takes place.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 3.


“Associative learning is defined differently by artificial intelligence scientists and psychologists. For the former, any change in the connection between elements as a result of their past activity counts as associative learning.... For psychologists, associative learning refers to learning that involves the formation of a conditional pairing between a non-reinforcing stimulus or action and a subsequent reinforcing stimulus, and they refer to learning by habituation and sensitization as ‘non-associative learning’. We use the term conditional learning or conditioning in this sense here....

“Such flexible conditioning, which in biological organisms depended on the evolution of a CNS, led to an enormous jump in adaptability, enabling animals to flexibly adjust their behaviour and physiology during their lifetime. It was probably one of the factors driving the greatest ecological diversification in the history of animals, the Cambrian explosion.

“Two types of conditioning are recognized: classical (Pavlovian) and operant/instrumental (Skinnerian/Thorndikian) conditioning....

“In Skinnerian or operant conditioning, the probability of eliciting a certain action changes as a function of its reinforcement history: actions that were followed by a positive (or negative) outcome will be more (or less) likely to occur in the future, under similar circumstances. For example, a rat can learn to press a lever when hungry if this action is followed by the delivery of food....

“The relation and co-dependence between the two types of conditioning were debated during the first half of the twentieth century. But, however the two processes were conceptualized, it was clear that under most ecological conditions, it is very difficult to tease apart classical and operant conditioning, because both usually occur during a single learning episode.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 5.


“With LAL, spontaneous and stochastic exploratory activities and preexisting simple reflex reactions can be flexibly combined, reinforced and recalled. Moreover, other non-compound (elemental) stimuli such as a flash of light or single actions like pushing a button, which are unrelated to a particular reward or punishment, can also become associated with the reinforcement and lead to a future anticipatory response. However, although the animal can learn about the value of stimuli and actions, it cannot discriminate between differently organized multimodal, compound, novel stimuli or complex action patterns; it can only learn if there is a temporal overlap between the CS and the US, or the action and the reinforcer; it has a very limited ability for cumulative learning, and cannot make decisions requiring a motivational trade-off among learned actions, or learned and reflexive actions, LAL is, therefore, distinguished from associative learning that enables compound multimodal discriminations, trace conditioning and cumulative learning (called unlimited associative learning, UAL).” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. pp. 5-6.


“If the connection between the presence of a brain and the capacity to learn through conditioning is not merely an artefact owing to our current scant and patchy information about the distribution of learning in animals, it may be an important clue for understanding the transition to associative learning. An integrating communication centre, a brain, seems to have been a necessary condition for the conditional, usually inter-related, world-learning and self-learning in animals.

“The evolution of brains was linked to the advent of bilateral symmetry.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 6.


“In all animals capable of conditional associative learning, there is also some differentiation within the brain into sensory and motor integrating centres and recurrent interactions between them.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 6.


“The evolution of increasingly complex associative learning culminated in what we call UAL. The learning capacities that distinguish UAL from LAL at the behavioural levels are (i) the ability to discriminate among novel compound stimuli that differ in the configuration of the elements of which they are composed (within the same modality and from different modalities) and among different motor action patterns; (ii) the ability to learn cumulatively, through second-order conditioning, pointing to a flexible value system; (iii) escape from immediacy–the ability to learn about a stimulus even when there is a temporal gap between the CS and the US or the action pattern and the reinforcer, pointing to working memory. The generativity and the ability for cumulative and recursive learning led to a further leap in cognitive adaptability.

“A survey of the learning literature suggests that these learning capacities are present in three phyla: in almost all vertebrates, some arthropods (including honeybees and cockroaches) and some cephalopod molluscs (the coleoid cephalopods: octopods, squid and cuttlefish).” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 6.


“The transition to UAL is, therefore, informational, intentional and teleological, and, we argue, contributed to the Cambrian ecological explosion.

“UAL was the basis for the evolution of more complex types of cognition. It culminated in the evolution of what Dennett called Popperian organisms, animals that can select among imagined, alternative actions without having to try them out....

“Like the transition to UAL, the symbol-based cognitive transition is informational, intentional and teleological, and has led to an ecological transition, to the era of the Anthropocene.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 7.


“We can, therefore, look at the relation between learning and evolution from three perspectives: evolution as learning, learning as evolution and the evolution of learning.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 7.


“The ability to distinguish between self-generated and world-generated stimuli, which is necessary for movement and is the basis of the distinction between self and world, depends on close coupling between interoception, proprioception and exteroception.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 8.


“... the implications of a two-tiered memory can be studied at the computational level: since all neural organisms have both epigenetic-intracellular memory and synaptic inter-cellular memory, and these two systems store information for different time spans, the learning capacity of a two-tiered system may be increased.” Ginsburg, Simona & Eva Jablonka. 2021. “Evolutionary transitions in learning and cognition.” Philosophical Transactions of the Royal Society: B. 376:20190766. p. 8.


“Central issues in the various debates [about how to demarcate the cognitive domain] are (a) the ongoing changes in the notion of cognition itself; (b) the very need and the possibility of a clear demarcation of cognition; and (c) formulating potential criteria–marks of the cognitive–for such a demarcation. So far, no clear consensus has emerged on any of these three issues, nor is such a consensus in sight. In this paper, I want to make progress on all three issues by asking a meta-question first: Why it is so hard to reach a consensus concerning the domain of cognitive science?

“The answer developed here builds on two ideas that are recently brought forward within the cognition discussions. First, cognition, as used within the cognitive sciences, must be considered a changeable theoretical term that has to be explicitly developed as such. Within the cognitive sciences, the concept of cognition has been enriched and developed on the basis of empirical findings and theoretical developments. Akagi considers such conceptual progress the ‘hard-won fruits of scientific inquiry (... that) pushed against pretheoretic intuitions’. Counterintuitive claims concerning cognition are not a vice when we aim to understand it scientifically. In the following, cognition will be explicitly cast as such a theoretical term that can develop in ways that diverge from its initial meaning....

“Second, cognition, as used within the cognitive sciences, should refer to an empirical domain constituting the target of explanation.” Keijzer, Fred. 2021. “Demarcating cognition: the cognitive life sciences.” Synthese. 198(Suppl 1):S137-S157. 10.1007/s11229-020-02797-8. p. S139; subquote: Akagi, M. 2028. “Rethinking the problem of cognition.” Synthese. 195(8):3547-3570. p. 3555.


“At this point, I propose a straightforward though radical way to proceed: cut the conceptual link between mind and cognition and start using them as two independent concepts that, over time, can come to reflect very different meanings. In this proposal, mind keeps its current meaning and usage, and will be left out of further consideration here. In contrast, cognition will be explicitly interpreted as a theoretical scientific concept that can–or rather must–be adapted to the material domain that constitutes its scientific target.” Keijzer, Fred. 2021. “Demarcating cognition: the cognitive life sciences.” Synthese. 198(Suppl 1):S137-S157. 10.1007/s11229-020-02797-8. p. S146.


“When living systems constitute the material foundation of cognitive phenomena, which aspects of living systems would be specifically relevant? In addition, how would this foundation impact on our understanding of cognition? To address the first question, I introduce a new concept, cobolism, to refer to the general means by which living systems manipulate and change the world.” Keijzer, Fred. 2021. “Demarcating cognition: the cognitive life sciences.” Synthese. 198(Suppl 1):S137-S157. 10.1007/s11229-020-02797-8. p. S149.


“I will use the phrase cobolism to refer to and conceptually bind together this additional cyclic organization [interaction between metabolism and the environment].

“In the past, various other concepts have been introduced with somewhat similar meanings, such as structural coupling and the interactive domain among others. Cobolism is related to such proposals but without a commitment to a specialized theoretical perspective, like autopoiesis or theoretical work on autonomy. Like metabolism, cobolism is a more general term intended for use in an empirical context. Cobolism is also an expandable concept that fits the various cobolic organizations found in nature. The strongest theoretical commitment is it having a cyclic organization, which ties otherwise unconnected structures, processes and events together into units that perform specific cobolic functions. Such cobolic cycles generate a living system’s interactions with the world, inside and outside.

“Thus the word ‘cobolism’ is intended to be complementary as well as analogous to metabolism. It is complementary as a co-(meta)bolism that connects and anchors the metabolic basis of a living system within its wider environment.... In this way, cobolism can play a conceptual role that has similarities to metabolism, both being general ways to systematically group a broad diversity of self-maintaining processes in a way that stress their wider relevance for maintaining the living system. The conceptual similarity between metabolism and cobolism also extends to the idea that both notions refer to a systmatic and cohering repertoire of processes that together constitute respectively the chemical and the interactive tools on which the organism depends for its existence.

“Cobolism provides a proper target domain for developing an updated and adequate account of what we take cognition to be....

“The proposal states that a cobolic organization provides a material basis, and thus a set of phenomena, to which the concept of cognition should be adapted.” Keijzer, Fred. 2021. “Demarcating cognition: the cognitive life sciences.” Synthese. 198(Suppl 1):S137-S157. 10.1007/s11229-020-02797-8. pp. S151-2.

SEE ALSO QUOTES 2023 for this Heylighen article
“While seemingly disparate, these different approaches [self-organization, systems biology, coevolution, synergy, symbiogenesis, niche construction, teleonomy, evo-devo, multilevel selection, major evolutionary transitions] have two assumptions in common. First, they are relational. Instead of reducing biological systems to independent units, such as genes or individuals, they investigate how these units interact and thus form part of a network of interdependencies that may give rise to a higher order system. Second, they emphasize autonomous action or agency. Instead of seeing organisms as passively undergoing the forces of natural selection, they investigate how systems and processes actively seek or produce fit, synergetic arrangements, while counteracting environmental influences that push them away from these preferred states. Putting the two assumptions together leads me to formulate a new, synthetic perspective that I will call relational agency. Simply formulated, this is an approach that sees the world as a network of interacting agencies rather than as a collection of independent objects subjected to external forces. In the remainder of this paper, I will try to formulate some foundational concepts and principles for this worldview.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. pp. 80-81.


“The roots of the worldview that I will call relational agency are actually much older than those of the objectcy worldview. The worldview of hunter-gatherers has been characterized as animism.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. p. 83.


“Some of these relationships [reactions between agencies as in chemical reactions or between different organisms] will be synergetic, in the sense that two or more agencies or reactions together can produce more of the conditions or resources they all need to continue functioning than each of them on its own. Others will be characterized by conflict or friction, in the sense that the activity of the one will impede the continued activity of the other(s). An agency surrounded by synergetic agencies will be more successful in achieving its goals (ultimately survival and multiplication) than one surrounded by agencies that have a relation of friction with it. Therefore, natural selection will tend to favor agencies profiting from synergies and to eliminate agencies suffering from frictions. There will be a general trend for evolution to promote synergetic relationships among agents, while weakening relationships characterized by conflict or friction.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. p. 91.


“Consider a special type of aquarium that does not exchange any matter with the outside world. Such a hermetically sealed, transparent bowl, called an ecosphere, contains air, seawater, shrimps, algae, and bacteria....

“The explanation [for why resources produced and consumed would remain perfectly balanced] is that the different agencies will adjust their production and consumption of resources until they are mutually adapted....

“Such a closed ecosystem illustrates a key concept in reaction networks: a (chemical) organization. This concept was introduced by Peter Dittrich, thus founding an approach known as chemical organization theory. In this theory, an organization is defined as a network of reactions and resources (also called ‘molecules’ or ‘species’) that is closed and self-maintaining....

“Interestingly, it can be shown that the attractors of the dynamical system defined by the network of reactions are all chemical organizations. This means that the system tends to spontaneously settle in one of these organizations, and that once there, it will remain there. In other words, such self-maintaining, closed networks tend to self-organize....

“This shows that fitness can in principle be defined in a purely relational manner: as the ratio of production to consumption in a network of reactions connecting different resources and agencies. This implies that the fitness of an agency is context-dependent: it can be large or small depending on the presence or concentration of other agencies and resources.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. pp. 91, 92, 93; reference: Dittrich, Peter & Pietro Speroni di Fenizio. 2007. “Chemical Organisation Theory.” Bulletin of Mathematical Biology. 69(4):1199-1231. 10.1007.s11538-006-9130-8.


“In the cell, the role of the resources is played by ‘passive’ molecules, such as glucose, ATP, and oxygen, which are consumed and produced by reactions in order to harness energy or build components. The role of the agencies is played by the enzymes, which catalyze and thus enable most of these reactions. Yet, the enzymes themselves are the product of more complex gene-expressing processes, which read a coding sequence of DNA and translate it into the right enzyme....

“For example, an antibiotic-resistant bacterium may have acquired a piece of DNA that codes for antibiotic-neutralizing enzyme. The entry of the antibiotic into the cell then functions as a triggering condition to express that stretch of DNA into the corresponding enzyme. This enables a reaction that consumes the antibiotic, transforming it into a molecule that is no longer toxic for the cell.

“From this perspective, the genes in a cell are functionally similar to the different agencies in a self-maintaining ecosystem.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. p. 95.


“... we should note that self-organization does imply a form of internal selection, in the sense that agencies or resources that do not manage to adapt to the rest of the emerging organization are eliminated.” Heylighen, Francis. 2023. “Relational Agency: A New Ontology for Coevolving Systems.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 79-103. MIT Press. p. 97.


“Three centuries after Newton we are, we believe, at a third major transition in science. We hope to make clear the evidence and need for this transition, and the wide, unexpected landscape for new science that can be glimpsed.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 142.


“Selection is downward causation. Selection acts on the whole organism, not its evolving parts. What gets to exist in the evolving biosphere is that which was selected. The explanatory arrows point upward. The selection of the whole alters the parts.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 144.


“The function of a part is that subset of its causal properties that sustains the whole.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 144.


“Any living cell or organism is a nonequilibrium physical system that is a Kantian whole which has the property that the parts exist for and by means of the whole. This provides a proper concept of function....

“Functional integration is always maintained, even as it transforms, because the functional evolution of the parts must always sustain the functioning Kantian whole upon which selection acts.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 152.


“Emergence is not engineering.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 153.


“An evolving biosphere is a self-constructing, functionally integrated blossoming emergence....

“An evolving biosphere is a propagating construction, not an entailed deduction.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 153.


“In a physical Kantian whole, the function of a part really is the subset of its indefinitely many causal properties that help sustain the whole.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 153.


“To unite the TAP [theory of the adjacent possible where ‘one or more things can give rise to one new thing’] process with the evolution of functionally integrated Kantian wholes, we have merely to add to TAP that things can act on the transformation by which things yield things, to speed or slow the transformation: that is, to catalyze or inhibit the transformation.” Kauffman, Stuart A. & Andrea Roli. 2023. “Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 141-159. MIT Press. p. 155.


“Biological systems are not only structurally hierarchical, but also functionally hierarchical: each layer solves unique problems in its own relevant problem space, exhibiting teleonomy.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 177.


“From the perspective of each embryonic stage, the prior stage has incorrect anatomy: it is a ‘birth defect’ that must be corrected by actuation of gene expression, physiology, and cell movement. One can view the progression of development as a series of repairs that drive the system toward the correct anatomical setpoint.

“Regulative development is thus a special case of the more generic process of regeneration: moving an incorrect state closer to the target setpoint. Many organisms can do this as adults, repairing drastic injury. Examples include salamanders (which can regenerate eyes, limbs, jaws, and other organs) and planarian flatworms (which regenerate every part of the body from even small fragments, while scaling the remaining tissue down so that perfect proportion results).” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. pp. 180-1.


“The paths through morphospace are sometimes associated with actual movements, such as the remodeling of tadpole to frog which creates largely normal frog faces even when starting with scrambled tadpole faces with all the organs in the wrong position: the primordia move around in novel paths until a correct frog face is reached, showing that genetics specifies not a machine with hardwired motions in specific directions but rather a process that can minimize error from a target morphology and thus handle novelty.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 183.


“The computational medium in which the collective intelligence of cells operates to so competently navigate morphospace is the same as that of the brain: bioelectric networks.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 185.


“Evolution exploits three main modalities to coordinate morphogenesis: biochemical signals, biomechanical forces, and bioelectric communication.... Importantly, control of morphogenesis and control of behavior are not only functionally isomorphic, but also share molecular mechanisms. This is not an accident, because nervous systems evolved by speed-optimizing ancient bioelectric circuits that evolved first to navigate morphospace and were then pivoted by evolution to navigate 3D space when nerves and muscles evolved.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 185.


“The most familiar goal-driven system, the brain, operates via a network of electrically active cells, whose resting potential is set by the activity of ion channels and can be propagated to their neighbors via gap junctions. Consistent with the fact that this architecture evolved from much more ancient cell types already using bioelectric signaling, all cells in the body do the same thing (but on slower timescales than neural spiking). Patterns of resting potential thus arise in tissues, and are a complex, nonlinear property of large numbers of cells driving coupled electric circuits. Such patterns are often instructive scaffolds for gene expression and anatomy, such as the ‘electric face’ observed in frog embryos which guides the position of the eyes, mouth, and other organs. The functional role of these bioelectric patterns is revealed by experiments in which ion channels are introduced or opened in ways that alter the standing bioelectric patterns; for example, specific potassium channel misexpression can trigger a ‘build an eye here’ pattern on the gut, resulting in the creation of an ectopic eye.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 185.


“Recent work suggests a unification of neural and non-neural physiology because all of the techniques of neuroscience are now being used outside the brain to understand development, regeneration, and cancer. The extreme portability of tools, concepts, and reagents from neuroscience (ion channel constructs, optogenetics, and computational models) suggests that the distinction between neurons and other somatic cell types is artificial. These techniques to not distinguish neural from non-neural tissues, revealing the opportunity to expand neuroscience well beyond neurons.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 187.


“A brief experience of a particular voltage state can change cellular decision making from ‘tail’ to ‘head,’ from ‘gut’ to ‘eye,’ and from ‘scar’ to ‘limb’; this is not micromanagement but large-scale setting of goals. Indeed, the target morphology–the shape to which cells regenerate after damage–can be permanently modified by transient changes of global bioelectric patterns. Genetically wild-type planaria can be induced to form two heads instead of a head and tail, and this pattern is then permanently propagated in the animals regenerating from subsequent cuts in plain water with no further manipulation.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 187.



“Teleonomy is also central to developing deeper definitions of intelligence, selves, organisms, stress, robustness, and so on that can survive the coming advances in biological and software engineering, which will produce novel living forms that bear little relationship to any touchstone within the tree of life on Earth–biobots, cyborgs, hybrots, and the like. What are the classic ‘model systems’ (from yeast to mouse) used in biological research models of? Teleonomy is a conceptual tool that allows us to move beyond the history of frozen accidents of evolutionary lineages and explore the truly general laws of biology instantiated by existing and novel beings. The science of cybernetics, and the deep lessons of neuroscience that extend well beyond neurons to address the scaling of goals in biological collectives, will be key components of this future.” Levin, Michael. 2023. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 175-197. MIT Press. p. 192.


“In this paper I question all the basic assumptions of this framework [that multicellularity was built very slowly by functional specializations and elaborate developmental programs] and present evidence that instead favors a view in which evolution of multicellular organisms is more directional and purposeful than the opportunistic, random-search-based scenario just described suggests. Concerning the specific elements of the standard picture, I show that (i) morphological motifs and patterns that arise during animal development are based on inherent material properties of cell clusters and are therefore readily accessible to these systems without repeated cycles of selection for marginally distinct variants; (ii) development does not depend on genetic uniformity of the embryo’s cells and therefore the beginnings of metazoan evolution were unlikely to have required it; (iii) functionalities that provided the physiological bases for specializations of differentiated cell types and organs were inherent to the single-celled organisms directly ancestral to the metazoans; (iv) a unique gene regulatory system whose origin accompanied animal evolution was capable of readily appropriating and parceling out cell functionalities to novel differentiated cell types; (v) the capacity of organisms to behave as autonomous agents, able to define their own boundaries and sustain themselves according to internal motives, was already present in unicellular antecedents; and (vi) novel organismal characters, drawing on intrinsic cellular or material properties, often appeared abruptly, and in preferred, or partly predictable directions, serving as enablements for new ways of life, rather than as adaptations to existing or emerging challenges.

“Among other heterodox implications, these proposed departures from the standard evolutionary narrative indicate a less deterministic relationship between genotype and phenotype than generally believed.” Newman, Stuart A. 2023. “Form, Function, Agency: Sources of Natural Purpose in Animal Evolution.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 199-220. MIT Press. pp. 199-200.


“These matters [5 points of article: innovations in developmental toolkit, physics of animal tissue changes, comparative analysis of transcription factors in metazoans, that all cells and tissues have agentive behavior, synchronization of transcriptional oscillators and bioelectrical pattern-memory], none of them controversial or even contested (though all with incompletely understood aspects) have not been considered together, but when they are, they gel into a coherent alternative to the Darwinian narrative of adaptive evolution by gradual natural selection. This new view accounts for several phenomena that have eluded satisfactory explanation in the standard framework. These include: (i) the abruptness of the appearance of animal forms in the fossil record; (ii) the tempo and mode of subsequent evolution (saltation, stasis, punctuated equilibria; (iii) discordances between phenotype and genotype; (iv) the recurrence of morphological motifs across the animal kingdom (inherency); (v) the use of a conserved developmental-genetic toolkit to generate analogous structures in lineages in which they were not present in common ancestors; (vi) the delay in appearance of phylum-characteristic body plans until mid-embryogenesis (the evolutionary-developmental hourglass); (vii) the origin of animal-characteristic differentiated cell types, and tissue and organ functions; (viii) the ability of interspecies embryo chimeras to develop into viable organisms with class-characteristic intermediate phenotypes; (ix) the nonadaptive origins of morphological characters; (x) adaptive appropriation of forms arising by unrelated processes; (xi) the ability of organisms to prevail in ecological settings in which they had no prior evolutionary history.” Newman, Stuart A. 2023. “Form, Function, Agency: Sources of Natural Purpose in Animal Evolution.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 199-220. MIT Press. p. 213.


“The conversion of some prebiotic chemical system, though of unknown identity, into simplest life would have involved an increase in size-mass of some nine orders of magnitude. (Molecular systems are typically of mass of ca. 10-21 g while that of a bacterium is ca. 10-12 g).” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. p. 259.


“The term ‘dynamic’ is applied [to term DKS] because the water that constitutes the fountain [using water fountain as metaphor for life] is continually turning over–same fountain, continually different water. The ‘kinetic’ term signifies that both the fountain’s existence and its nature depend on rates–the rate at which water is ejected from the fountain nozzle, and the rate at which it then falls away. Surprisingly but significantly, thinking about the stability/persistence of a water fountain in this fashion can offer useful insights into the nature of living things: as transient, yet persistent. Metaphorically speaking, living things can be thought of as ‘chemical fountains’....

“A chemical DKS system is defined as one that is in an energized, nonequilibrium, dynamic, cyclic state, in analogy to that physical DKS water-fountain system.” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. pp. 261-2.


“Kinetic analyses ... indicated that the evolutionary dynamics of a replicative system governed by DKS kinetics would be quite different from that of a simple replicative chemical system following traditional kinetics. The evolutionary directive would be toward dynamic kinetic more stable replicating entities, rather than toward faster replicators or thermodynamically more stable ones.” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. p. 264.


“In fact, the DKS concept allows the formulation of what could be considered an extension of the second law, one that could be applied, at least in principle, to both kinetic and thermodynamic systems. That extended principle has been termed the persistence principle. The principle may be stated most simply as follows: All material entities are driven from less persistent to more persistent forms.... Indeed, for replicative systems in the DKS state, the persistence principle enables the direction of change to be specified; that is, from less DKS stable to more DKS stable.” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. p. 264.


“A DKS perspective suggests that the evolutionary process for replicative systems does have a direction; toward more persistent forms, toward replicative systems of greater DKS.... In this context it is also important to note that the concept of natural selection, at the heart of Darwin’s epoch-making scientific contribution, can now be understood as a particular biological manifestation of a wider physical/chemical phenomenon: kinetic selection.” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. pp. 264-5.


“The DKS system and its environmental support system are umbilically linked. The DKS system’s existence is based on an ongoing dynamic interaction between the system and its supporting environment.” Pascal, Robert & Addy Pross. 2023. “Toward the Physicalization of Biology: Seeking the Chemical Origin of Cognition.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 257-274. MIT Press. p. 268.


“There are two major factors stimulating mobile DNA activity evident in the examples from [the table]: (1) biotic and abiotic stresses and (2) interspecific hybridization, which often leads to polyploidization. The significance of such inputs is to increase genomic innovation by mobile DNA when the conditions of life are most difficult. Note that interspecific hybridization is an indicator of such difficulty because it is most likely to occur when the within-species mating pool has declined. A more basic point to remember in thinking about evolutionary theory is that artificial generation of novel species has been practiced in agriculture for thousands of years by interspecific hybridization, never by selection alone.” Shapiro, James A. 2023. “Evolutionary Change Is Naturally Biological and Purposeful.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 275-298. MIT Press. p. 283.


“I argue that modern synthesis evolution fails because it is a foundationalist theory. The component processes of evolution are contact phenomena: they take place at the interface between the purposive organism and its conditions of existence. Foundationalist theories are incapable of representing contact phenomena as contact phenomena.” Walsh, Denis M. 2023. “Evolutionary Foundationalism: The Myth of the Chemical Given.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 341-362. MIT Press. p. 341.


“The barriers between genotype space and phenotype space secure the conviction that while the processes that occur within genotype space–replication, translation, mutation–are genuinely evolutionary processes, those that occur within phenotype space–adaptive innovations, developmental plasticity, ecological transmission, niche construction, social learning–generally are not. Consequently, the study of evolution, properly construed, is the study of the dynamics of genotype space. Phenotype space matters only insofar as it has implications for changes in genotype space.” Walsh, Denis M. 2023. “Evolutionary Foundationalism: The Myth of the Chemical Given.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 341-362. MIT Press. p. 348.


“Genetic inheritance alone is extremely robust, but highly insensitive....

“Taken together, the entire suite of inheritance mechanisms–genetic, epigenetic, parental effects, niche construction, ecological and cultural transmissions, learning–offers the phenomenon of intergenerational transmission the requisite balance between fidelity and responsiveness. Multiple modes of inheritance are much better for adaptive evolution than just one.” Walsh, Denis M. 2023. “Evolutionary Foundationalism: The Myth of the Chemical Given.” In: Evolution “On Purpose”: Teleonomy in Living Systems. Corning, Peter A., S.A. Kauffman, D. Noble, J.A. Shapiro, R.I. Vane-Wright & A. Pross (eds). pp. 341-362. MIT Press. p. 350.


“Futuyma notes that ‘The developmental response seems not to be an adaptation, even though it can have an advantageous effect.’ That sentence could serve as a definition of a novel phenotype at its origin: it is a developmental anomaly that, like a genetic mutation, can have evolutionary potential; then, if it has an advantageous effect, it may become established (genetically accommodated) under selection in a population.” West-Eberhard, Mary Jane. 2021. “Foreword: perspective on ‘plasticity’.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. ix-xxi. Boca Raton, FL: CRC Press. p. xiii; subquote: Futuyma, D.J. 2021. “How does phenotypic plasticity fit into evolutionary theory?” pp. 349-366 of same book.


“Developmental plasticity is a manifestation of pathways that connect the environment with the genome.” West-Eberhard, Mary Jane. 2021. “Foreword: perspective on ‘plasticity’.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. ix-xxi. Boca Raton, FL: CRC Press. p. xv.


“Over the past four decades, plastic responses have been documented across the phylogenetic spectrum–in bacteria, fungi, and lichens; algae and land plants; marine and freshwater invertebrates; insects, fish, amphibians, reptiles, and mammals–in response to contrasting states of a broad range of abiotic and biotic factors including temperature and humidity; concentration of O2 and CO2, pH, and other aspects of substrate and atmospheric chemistry; spectral quality, quantity, and diurnal pattern of light; type and availability of food and other resources; population density and social interactions; presence and density of competitors, predators, herbivores, pathogens, or mutualists; even vibration, touch, and acoustic stimuli.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. pp. 3-4.


“Plasticity encompasses all aspects of the phenotype in which expression varies as a result of environmental differences.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. p. 4.


“The fundamental insight that gene expression is environmentally sensitive provides our starting point: plasticity is an intrinsic property of organisms.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. p. 6.


“Adaptive transgenerational plasticity has been documented at the phenotypic level in a wide range of plant and animal taxa.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. p. 14.


“In mammals, parental stress or toxin exposure can lead to substantially different physiological and behavioral responses of juvenile and adult offspring to stresses they encounter.

“This point raises an important question about one of the main tenets of plasticity research. For over a century, the norm of reaction has been defined as a genotype’s characteristic repertoire of responses to alternative conditions–‘the expected phenotype of a given genotype as a function of the environment’. This view guides both experimental design and evolutionary modeling. Yet if inherited environmental effects can alter the responses of a given genetic individual to its current conditions, the norm of reaction cannot be considered a fixed genotypic property.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. p. 15.


“The recognition that gene expression is environmentally sensitive has put an end once and for all to the misleading idea that genes and environment are alternative causes of phenotypic variation. Instead, it is now widely understood that plasticity–the variable expression of a given genotype in different environments–is an intrinsic property of organisms.” Sultan, Sonia E. 2021. “Phenotypic Plasticity as an Intrinsic Property of Organisms.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 3-24. Boca Raton, FL: CRC Press. p. 16.


“Phenotypic plasticity is generally favored when the environment varies and different phenotypes are optimal in different environments, especially when costs of phenotype adjustments are low and predictive environmental cues are present. However, the time scale, or temporal graininess of environmental variation, matters. Developmental plasticity is particularly favored when the environment changes across generations, but is relatively consistent within generations. When the environment changes within a generation, more continuous phenotypic adjustments are favored, such as context-dependent expression of behaviors or enzymes. When conditions change over longer time frames but are consistent across successive generations, transgenerational plasticity can be adaptive. While variation tends to favor plasticity, extreme variation can disfavor such strategies. Highly variable environments, especially when reliable cues are lacking, result in the evolution of bet-hedging such as stochastic gene expression in microbes or dormancy periods in desert annuals.

“Theoretical models show that both temporal and spatial environmental variation can play a role in the evolution of plasticity.” Snell-Rood, Emilie C. & Sean M. Ehlman. 2021. “Ecology and Evolution of Plasticity.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 139-160. Boca Raton, FL: CRC Press. p. 141.


“Although we tend to forget this, gene expression is always context-dependent. Genes require an environmental cue to be expressed whether that cue is an external environment or the product of some other gene, and phenotypes can vary as a function of differences in those cues.” Schlichting, Carl D. 2021. “Plasticity and Evolutionary Theory: Where We Are and Where We Should Be Going.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 367-394. Boca Raton, FL: CRC Press. p. 368.


“Normal developmental sequences are often considered to be ‘canalized’ or ‘robust’ to genetic or environmental changes. Within the normal range of environments, novelties in canalized developmental pathways are most likely to be produced via mutation. However, outside of those environments where canalization has evolved, reaction norms are inherently plastic and thus also inherently prone to produce phenotypic novelty. ‘Hidden reaction norms’ represent plastic response to novel or infrequently experienced conditions for which there has been no selection for either canalization of a particular phenotype or an adaptive plastic response. Collectively, the hidden reaction norms of different genotypes express ‘cryptic genetic variation’. Such cryptic genetic variation has been suggested to represent a store of variability that can be revealed in novel environments or genetic backgrounds.” Schlichting, Carl D. 2021. “Plasticity and Evolutionary Theory: Where We Are and Where We Should Be Going.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 367-394. Boca Raton, FL: CRC Press. p. 368.


“We have proposed that multicellularity and differentiation may have arisen through genetic assimilation of sequential plastic responses to changes in internal environments, with novel developmental phenotypes initiated plastically and subsequently canalized so that adaptive developmental sequences are repeatable under broad conditions. In this scenario, the evolution of multicellular development is a recursive process alternating between plasticity and the evolution of robustness.” Schlichting, Carl D. 2021. “Plasticity and Evolutionary Theory: Where We Are and Where We Should Be Going.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 367-394. Boca Raton, FL: CRC Press. p. 372.


“One significant consequence of plasticity for evolutionary dynamics was pointed out by Sewall Wright–plasticity can hide genetic variation. A plastic response can move organisms to a different adaptive peak, even if allelic variation is available that might otherwise facilitate adaptive evolution. If the new environment is stable, we then have adaptation via plasticity but evolutionary stasis, a fundamentally different dynamic than adaptive evolution by allelic substitution.” Schlichting, Carl D. 2021. “Plasticity and Evolutionary Theory: Where We Are and Where We Should Be Going.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 367-394. Boca Raton, FL: CRC Press. p. 378.


“The SET [standard evolutionary theory] is in no danger of being eclipsed–it is still clearly fundamental for understanding the evolution of populations subject to selection and drift, but it just as clearly is not an encompassing theory of evolutionary processes.... The standard view of a genotype mapping to a single phenotype with a particular fitness is misleading at best, and likely inaccurate for most genes: via plasticity, a single genotype can produce multiple phenotypes, and each of those phenotypes will have its own environment-dependent fitness.

“I propose that a view of evolution as a recursive process involving both the generation and sorting of variation is a more accurate and flexible perspective. Many of the topics embraced by supporters of an EES [extended evolutionary synthesis] are related to the generation of variation. The SET, on the other hand, encompasses a wealth of theory about the sorting of variation. A full view sees new phenotypes produced via new mutation or exposure to new ‘environments’ (including new genetic backgrounds, new developmental milieus, and new external conditions), followed by processes that sort such variation–selection, drift, and gene flow.” Schlichting, Carl D. 2021. “Plasticity and Evolutionary Theory: Where We Are and Where We Should Be Going.” In: Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies. Pfennig, David W. (ed). pp. 367-394. Boca Raton, FL: CRC Press. p. 382.


“What the two readings of our title [‘Evolution Evolving’ – evolution itself is evolving or theory is evolving] have in common–and the principal thesis that we defend in this book–is that developmental processes do more than impose constraints on selection: they also help explain adaptive evolution, and they do so in every bit as fundamental a sense as the far-better-established converse assertion that evolutionary processes explain developmental mechanisms.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. x.


“...our objective is to provide a picture of what a developmentalist take on evolution might look like....” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. xi.


“Mojave Desert woodrats feed on a toxic diet [creosote bushes], thanks to bacteria that they reliably inherit by consuming soil and feces in their environment.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 4.


“... laboratory researchers in 2014 were astonished by some laboratory mice that mysteriously exhibited a fear experimenters had trained into their grandparents. That is not supposed to happen!...

“The Emory University researchers showed that when mice were conditioned to be frightened of a particular smell, their offspring, and their offspring’s offspring, retained this fear. That is because the odor entrainment had modified the Olfr151 gene, which encodes the olfactory receptor specific for this odor, by removing a methyl group from it. Remarkably, this demethylation of the Olfr151 gene was also seen in the sperm of these mice, and indeed their offspring’s sperm.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 5.


“Neither inherited microbiomes nor animal cultures nor epigenetic inheritance is rare in nature, as this book will make clear. A veritable cornucopia of resources other than genes are now known to be passed down the generations, including components of both egg and sperm, hormones, symbionts, epigenetic changes, antibodies, ecological resources, and learned knowledge.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 9.


“Current controversies concerning extragenetic inheritance (a.k.a. nongenetic inheritance), whether developmental mechanisms constrain or facilitate evolution (i.e., developmental bias), whether developmental responses to environmental change can direct genetic change (i.e., plasticity-led evolution), and how the activities and outputs of organisms modify selection (i.e., niche construction), relate to interactions between Lewontin’s subprocesses. An exciting implication of the aforementioned new data is that the evolutionary process itself evolves, as the characteristics of evolving populations and their modes of inheritance influence how natural selection operates.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 10-11.


“Strictly, developmental processes create the landscape for selection, since a phenotype cannot be selected before it exists.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 14.


“For instance, in birds, running is associated with unequal-length phalanges and perching with more equal-sized toe bones.... In raptors and some other birds, for instance, an elongation of the bones at the fingertips has apparently evolved convergently in multiple lineages, under selection for grasping. Thus, even the deviations from ‘normality’ exhibit regularities. Evolution is far from an ‘anything goes’ affair.

“Such studies are exciting as they help to make evolutionary biology a more predictive science. They also help to explain why some adaptations exist and others do not, and why some characters are more evolvable than others..... For the moment is suffices to point out that when biases in the generation of phenotypic variation are understood as ‘constraints,’ they can at best explain why evolution or adaptation has not occurred.... From an evo-devo perspective, developmental bias partially explains why evolution and adaptation do occur, rather than what do not, since it is focused on the variation that is commonly produced.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 15, 16.


“But there is more to the distinction between ‘bias’ and ‘constraint’ than that–a fundamental difference that lies at the very heart of evolutionary causation. Developmental processes bias the variation that is subject to selection, but those developmental mechanisms themselves evolve through natural selection. In continual interactive cycles, developmental processes bias what gets selected, but then selection modifies the developmental processes that create developmental bias. This process of reciprocal causation guides the evolution of morphology, and indeed all aspects of the phenotype.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 16.


“Development does not really have a beginning or an end, but rather flows continuously down the generations.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 52.


“These fishes [cichlids] are known for their rapid speciation, with diversity linked to adaptations in their craniofacial skeleton associated with feeding specializations. Remarkably, it turns out that the behavior of the fish as larvae is an important source of phenotypic variation in their head and jaw shapes. During vertebrate development, skeletal elements are laid down as cartilage and then later ossified into bone. Immediately after the cartilaginous lower jaw forms, but before the beginning of bone deposition, the fish start rapidly opening and closing their mouths....

“From a traditional (i.e., ‘genetic program’) evolutionary perspective, such ‘self-stimulatory’ mechanisms seem quite peculiar–why not simply grow bones of the right length without having to generate additional force? In contrast, from a developmental perspective, it is expected that phenotypic evolution will capitalize on existing regulatory interactions. An advantage of the reliance on mechanical stress [i.e. the rapid opening and closing] is that the organism’s morphology can be adjusted according to its current internal and external environment, helping to ensure it remains adaptive even in the face of substantial genetic or environmental perturbation.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 64, 65.


“Organisms harness the outside world to their own ends, and many environmental factors contribute importantly to the regulatory systems that compose development.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 66.


“In fact, many developmental systems operate by generating variation (i.e., ‘exploring’ possibilities), largely at random, testing variants’ functionalities and selecting good solutions for regeneration, in an iterative loop. These phenomena, known as ‘exploratory mechanisms,’ resemble adaptation by natural selection, except that they allow for information gain by the individual organism within its own lifetime, rather than the acquisition of genetic information in a population over multiple generations....

“Exploratory mechanisms are ‘Darwinian’ developmental processes that generate adaptive phenotypes iteratively through producing variation, testing variant performance, and regenerating or retaining valuable functions. Examples include adaptive immunity; brain development; the growth of blood vessels and plant roots and branches; the cytoskeleton; and learning and exploratory behavior in animals....

“New [blood] vessels expand into all regions of the body, stabilizing where needed by attraction to hypoxic conditions. The size of the vessels grows in response to blood flow, which is a function of demand. Similarly when an organ uses up oxygen, it actively promotes its own vascularization because the lack of oxygen triggers expression of a gene that produces the growth factor responsible for the differentiation of the vascular cells. The result is a well-distributed system capable of servicing virtually every cell in the body....

“Exploratory mechanisms are adaptive because rapid exploration of a large space of possibilities combined with feedback (e.g., reward/punishment) allows information to be gained from the current environment....

“These properties of exploratory mechanisms confer major advantages in robustness and flexibility; they are tolerant of mutation, internal failure, environmental novelty, noise, errors, and injury. Within limits, they are anatomically self-correcting in relation to functional demands. They can adapt to evolutionary changes in other parts of the organism. For example, if sensory fields grow or shrink, then the corresponding cortical areas adjust automatically, while changes in tracheal and blood vessels require few, if any, mutations to accommodate to changes in morphology. Cascades of exploratory mechanisms in development can lead to coordinated change across several systems; for instance, when–without genetic change–muscle, nerve, and vascular systems respond appropriately to changes in bone growth.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 68, 69, 70-71.


“... we extract five general principles of development that are relevant to the study of evolution.

“First, it is apparent that development is modular, by which we mean its components possess their own intrinsic dynamics and integrated structure.... Development is modular and combinatorial....

“In sum, that development is modular at many levels allows (1) combinatorial associations to specify different tissue types, (2) the recruitment of one module into another module, and (3) the expression of genes independently in different tissues.

“A second key principle is that development is ‘epigenetic,’ by which we mean it occurs through interactions between regulatory elements above the level of the gene, including interactions between cells, and interactions between tissues....

“A third principle is that development is constructive, by which we mean that development is a coordinated integration of many sources of potential information, not just those arising from nuclear genes. The organism creates a developmental trajectory by constantly responding to, and altering, internal and external states. This can be contrasted with the widespread view of development as directly ‘programmed’–that is, unfolding according to rules and instructions specified within the genome. In fact, developmental causation flows from ‘higher’ levels of biological organization (e.g., cell-cell interaction, the immediate environment) that regulate gene expression, and back again, to generate proteins and cell behavior....

“A fourth insight is the interchangeability of the phenotypic consequences of a change in DNA and a change in internal or external environment....

“Finally, one of the most important implications of development for evolutionary biology is that, because of epigenetic developmental interactions in regulatory networks, random genetic change does not typically result in random phenotypic variation; that is, phenotypic variation is structured and biased.... The random mutation of any gene or regulatory element will inevitably be processed by a developmental system, with the effects integrated into one or more preexisting GRNs [gene regulatory networks]....

“These then are the five general principles that we emphasize as being particularly important to the field of evolutionary biology: development is modular, epigenetic, constructive, interchangeable with respect to genetic and environmental inputs, and prone to generate biased phenotypic variation.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 72, 73, 75-77, 79.


“Moreover, there can be no information without a mechanism of interpreting the DNA sequence as information. The zygote inherits DNA; it does not inherit ‘genes.’ Genes and gene products are constructed anew in each cell in the developing embryo by the relationships among DNA, nucleosomes, transcription factors, and RNA-splicing factors. Only certain regions of the DNA sequence are constructed into genes, and different regions of the genome can be genes in different cell types. The interpretation of What is a gene? is done by the cell, or a higher-order structure. Development is all about the interpretation of relationships.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 78.


“... while many components of the regulatory machinery are gene products, the tracing of causality back to genes is an entirely arbitrary convention. The regulatory machinery of cells determines gene transcription, which produces gene products that make up the regulatory machinery, which determines transcription, and so forth in cycles that flow down the generations. Tracing causality back to genes is seductive by logically flawed.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 78.


“In such debates [role of development in evolution], the developmental mechanisms that make random genetic variation give rise to structured phenotypic variation have commonly been thought of as constraints: features of organisms that hinder, or even prevent, populations from evolving adaptively. The term ‘constraint’ often implies that some regions of phenotypic space that might otherwise be adaptive are not available to natural selection because they cannot be generated by the developmental system. Proponents and critics of neo-Darwinism commonly portrayed constraints as acting in opposition to natural selection, or as providing an alternative explanation to it. The traditional viewpoint has been that while constraints may make certain regions of morphospace inaccessible or difficult to reach, for other regions natural selection has full reign to explain evolutionary outcomes.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 87.


“These experiments [mutation-accumulation experiments where successive generations of simple organisms are not subject to selection so that mutations can accumulate] reveal that the relationship between genotype and phenotype exhibits reliable and common features, patterns that theoretical models are able to reproduce. One such general conclusion is that the probabilities of generating particular phenotypes through random genetic change vary by many orders of magnitude. Some wing shapes or flower forms are millions of times more likely to arise than others. In other words, strong developmental bias is the norm. A second is that only a tiny fraction of the imaginable phenotypic variation is observed in nature. Phenotype space is largely empty. A third, and perhaps the most provocative, finding is that it is often possible to predict which phenotypes will be found in nature, and thus to account for evolutionary change, using knowledge of which phenotypes are easily generated through mutation.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 92.


“This responsiveness of the phenotype to the environment is known as phenotypic plasticity, and it means that genetically similar individuals can exhibit strikingly altered traits when exposed to different conditions.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 100.


“How phenotypic plasticity might play an important role in evolution becomes clearer if we regard an organism’s traits as produced by regulatory interactions during development. Environmental cues are frequently processed by the same regulatory networks as genetic mutations, and, like genetic change, environmental perturbations commonly lead to biased phenotypic variation. In some cases, phenotypic variation is adaptively biased, and in other it is not, but either way plasticity can contribute to adaptive evolution by influencing which phenotypes are exposed to selection, and in which environments they appear.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 100.


“To investigate this [how fish native to surface waters but related to Mexican cave fish would react to being subject to cave conditions], [researchers at the U. of Maryland] raised surface-dwelling A. mexicanus in complete darkness. Intriguingly, they found that these fish came to exhibit many of the traits of their blind cave fish conspecifics, including changes in the thickness of retinal layers in the eye, increased resistance to starvation, decreased metabolic rate, changes in hormone levels, downregulation of the expression of genes involved in visual perception and circadian regulation, and upregulation of genes associated with fat storage. Hence phenotypic plasticity allowed precursors of many cave-related traits to appear in surface fish within a single generation. Apparently, surface-dwelling fish already had this ability to alter their physiological and behavioral phenotypes when reared in darkness, and in cave fish these phenotypes have become exaggerated and stabilized. Exposure to the stress of constant darkness seems to mobilize multiple developmental mechanisms, including endocrine signaling, activation of heat-shock proteins, and other transcriptional changes, which collectively instigate major changes in the operation of the gene regulatory networks (GRNs) underlying many cave fish traits. These morphological changes are accompanied by behavioral adjustments–for instance, when foraging, dark-raised surface fish increase their reliance on the lateral line (a sensory organ that allows fish to detect movement and pressure changes in water) compared with fish reared in standard lighting.

“Of the traits that change on exposure to the dark, some, such as increased starvation resistance or decreased metabolic rate, became more like those of the blind Mexican cave fish, with plasticity seeming to facilitate adaptively beneficial traits. Others, however, shifted in the opposite direction.... Phenotypic plasticity may not consistently generate adaptive changes, but these and other experiments suggest that it may consistently increase selectable variation in relevant phenotypes....

“Obviously, plasticity alone does not explain the blind Mexican cave fish’s traits, which are also known to have been subject to genetic change.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 101-3.


“The parallel evolution of cave fish populations [both in and outside of caves] makes sense once it is recognized that multiple cave fish traits do not evolve independently but are connected through interacting developmental mechanisms derived from underlying regulatory networks, as we saw for the domestication syndrome [where Darwin noticed how many wild animals once domesticated acquired a suite of similar traits like curly tails, floppy ears, smaller teeth, smaller brains, frequent estrus cycles, etc.]....

“Some cave fish traits are most likely analogous to the floppy ears or curly tails in domesticated animals–neutral or mildly deleterious traits indirectly favored by selection because they are produced by the same mechanisms as characters that were selected directly.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 105.


“As populations adapt, the environmental responsiveness itself may evolve, since selection can favor enhanced plasticity (e.g., leading to the evolution of a polyphenism), reduced plasticity (canalization or genetic assimilation), or no evolutionary change. However, it is not the extent of plasticity that matters–the key point is that the evolutionary trajectory the population will follow, the rate at which it evolves, and any equilibrium approached may be guided by the characteristics of the plastic response.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 107.


“The above line of reasoning–often labelled ‘plasticity-led’ or ‘plasticity-first’ evolution–entails that the appearance of adaptive novelty does not typically require mutation, but results from developmental reorganization and the incorporation of environmental inputs. The key point is not that plasticity comes first (after all, evolutionary responses may capitalize on the prior existence of relevant genetic variation), but that plasticity directs, or ‘leads,’ adaptive evolution.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 109.


“Thus, the diversity of a clade is not determined solely by the diversity of external environments but may also depend on the plasticity of the ancestors.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 111.


“The insect wing constitutes a classic example of an evolutionary novelty. At the phenotypic level, insect wings lack obvious homology to other insect appendages and cannot easily be understood as a refinement of preexisting structures.... Whatever variation once existed has been lost deep in time and is unavailable for present-day comparative evolutionary analyses. Lacking both obvious correspondence to other traits and significant phenotypic variation within or across populations of closely related species, constructing an adequate framework in which to address the issue of where insect wings come from has proven challenging. As a consequence, evolutionary biology has accumulated a great deal of information about the quantitative and population genetic architecture of insect wing size and shape, but comparatively little on the origin of the insect wing.

“Evolutionary developmental biology provides a different way of thinking about evolutionary novelty. Evo-devo focuses on how traits are made during development, and how the process of building a particular trait compares to that of other traits, regardless of whether they do or do not share obvious homology. This makes a difference, because new complex traits often arise through the co-option and reuse of existing developmental circuitry....

“One hypothesis proposed that wings evolved as novel structures on the body wall, and another that wings evolved from preexisting outgrowths (exites) of the leg. These hypotheses appeared to be in conflict, but evolutionary developmental biologists have shown how both can be correct. Experiments by Heather Bruce and Nipam Patel suggest that leg segments in the common ancestor of contemporary insects and crustaceans were incorporated into the body wall of the newly evolved insects, and only later were co-opted to form wings. The authors conclude that” ‘both the leg exite and body wall theories are correct, but each is relevant to different phylogenetic time points: crustacean leg exites evolved into body wall lobes, then subsequently into wings.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 174-6; reference: Bruce, H.S. & N.H. Patel. 2020. “Knockout of crustacean leg patterning genes suggests that insect wings and body walls evolved from ancient leg segments.” Nature Ecology and Evolution. 4:1703-12.


“Dung beatles also show how disrupting the self-inhibition of developmental modules can lead to striking evolutionary innovations. When Edurardo Zattara and colleagues experimentally downregulated the expression of otd, an entirely novel compound eye was produced in the middle of the beetle forehead. Even more impressive was the finding, from behavioral tests, that the new eye was at least partially functional and integrated with the central nervous system. Perturbing the level of one transcription factor cannot possibly explain all aspects of the development of this highly complex organ. Rather, the finding illustrates how simple developmental signals can switch ancient circuitry on and off, with the effect of incorporating core processes into development, or removing other processes, resulting in novel structures. If a novel compound eye were to be favored by selection, there would be no requirement for all the individual components of the new eye to coevolve: in one fell swoop an existing eye-making package can be taken ‘off the shelf’ and utilized in a novel location.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 177; reference: Zattara, E.E., A.L.M. Macagno, H.A. Busey & A.P. Moczek. 2017. “Development of functional ectopic compound eyes in scarabaeid beetles by knockdown of orthodenticle.” PNAS. 114:12021-26.


“... mathematician Alan Turing used reaction-diffusion models to show that two or more diffusing substances can interact to generate wavelike patterns that are able to explain a host of features in nature, including spots, stripes, and vertebrate limb development. The diffusion of interacting morphogens in the developing limb bud generates waves of cell activity, leading to the production of stripes of precartilage, from which cartilage and bone will later form. Because a dynamical system with a fixed wavelength constrained to a bounded region must produce a discrete number of waves, the result is a discrete number of digits....

“Vertebrate digit patterning has been subject to extensive experimentation and theoretical analyses using Turing models, which provide strong support for this ‘self-organization’ explanation. In addition to increasing the amount of limb-bud tissue to be divided up into digits, a second way in which more fingers and toes can arise is by reducing the wavelength of the Turing pattern and thereby generating thinner digits. Sure enough, in embryonic mice, experimental manipulation of the dose of distal Hox genes, which modulates the wavelength of the Turing-type mechanism, was found to generate progressively more severe polydactyly.... Thus, it appears that a self-organizing Turing-type mechanism is deeply conserved in tetrapod phylogeny.

“There are several important aspects of these findings. First, they go against the expectation, dating back to Darwin, that changes must be continuous and of small effect to be viable....

“Second, genes do not directly specify the number of digits. While a mutation can trigger the production of extra digits, it is not directly responsible for their construction, which involves many genes and signaling pathways, as well as environmental factors; nor does the mutation dictate how many fingers or toes there will be, which varies among individuals with the same genotype. Rather, the mutation throws a switch in the existing developmental architecture, making a difference in the final outcome; it is the cause of the difference, not the cause of fingers and toes. The integrated ensemble of bone, muscle, tendon, nerves, and blood vessels that constitutes these organs is produced by a complex regulatory system, and control over the number of digits lies at the level of communication between cells, and interactions between internal and external regulatory elements.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 183, 184.


“That virtually all major (i.e., phylum-level) body plans were established in the Cambrian explosion, just over five hundred million years ago, has been one of the most intriguing evolutionary conundrums for decades. An understanding of regulatory interactions in development is beginning to help scientists to explain this, together with some other enigmas concerning the emergence of phenotypic diversity over time. Despite the immense diversity of animal forms that has arisen since the Cambrian, no radically different animal morphologies have appeared. In 2006, Davidson and Erwin suggested that the rapid evolutionary diversification of body plans during the Cambrian was caused by the evolution of particular GRNs. They were able to identify component parts, or modules, of animal GRNs (called kernels), which play key roles in the development of body parts and as a consequence became highly conserved.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 185; reference: Davidson, E.H. & D.H. Erwin. 2006. “Gene regulatory networks and the evolution of animal body plans.” Science. 324:1318-1320.


“Before evo-devo, the traits of organisms tended to be regarded as either homologous or not. However, diving into the black box and investigating the mechansims and organizational principles of development has revealed unanticipated complexity in the historical relations among species. Characters became like onions–layered, from genes to pathways to cell types to tissues to organs–with homology potentially manifest at any and every level.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 188.


“Biological anthropologist Terrence Deacon proposed a rule specifying that if brain regions become disproportionately large, then, as they evolve, they would tend to ‘invade’ and become connected to regions that they did not innervate ancestrally. This would increase the influence of the enlarged areas over other brain regions and make them more important to brain functioning. One consequence of evolution creating larger brains by ‘stretching’ brain development is that brain regions that mature relatively late become disproportionately large. The two largest (and among the latest-developing) structures in the human brain are the neocortex and the cerebellum and, as predicted, they became increasingly embedded in complex neural networks during the course of human evolution, and exert considerable influence over them.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 213-4; reference: Deacon, T. 1990. “Rethinking mammalian brain evolution.” American Zoologist. 30:629-705.


“The remarkable green sea slug leads its adult life as a nomadic leaf. This strange animal feeds on yellow-green algae, extracting the chloroplasts and incorporating them into the cells that line its digestive tract, and then engages in photosynthesis. Thereafter, the sun provides it with virtually all the metabolic energy it needs to survive and develop. The green sea slug is unusual because, unlike, say, coral, the chloroplasts are incorporated as naked organelles that somehow remain functional, so it is the animal’s own cells, rather than its symbionts, that carry out photosynthesis. The chloroplasts are not transmitted to the slug’s offspring in the eggs or sperm–the larvae must seek out algae and feed for several days to acquire their own. The ingested chloroplasts turn the animal green, allowing it to live as a plant.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 227.


“Central to any understanding of evolution is a conception of how organisms work. We have suggested that developmental processes cause adaptive change, by determining phenotypic responses to genetic and environmental perturbations, as well as which traits are expressed together and what impact they have on reproductive success or survival.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 228.


“Richard Lewontin and Elliott Sober, among others, have emphasized the distinction between variational and transformational explanations for change in a population. For instance, imagine a class of school children who perform better in their subject at the end of the school year compared with the beginning. That could be because good teaching has, on average, improved the pupils’ knowledge and understanding so that they mean score has increased–a transformational explanation. Alternatively, the increase might have occurred because lower-scoring pupils were disproportionately likely to drop out during the school year, or were sent to another class for remedial schooling–a variational explanation. In the latter case, no individual student needs to have improved for the average score in the class to increase. Darwin’s theory of evolution by natural selection provides a variational explanation for change in species over time, while, in marked contrast, Lamarck’s earlier account of evolution offered a now-discredited transformational explanation. However, as the classroom example illustrated, both types of explanation may be reasonable.

“The distinction between variational and transformational explanations is relevant here because close inspection of the interactions between the subprocesses of natural selection reveals a significant but poorly recognized role for transformational explanations in adaptive evolution, alongside the established variational explanation provided by fitness differences.” Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. pp. 238-9.


“‘organisms do not adapt to their environments; they construct them out of the bits and pieces of the external world.’” Quote by Richard Lewontin in: Lala, Kevin N., T. Uller, N. Feiner, M.W. Feldman & S.F. Gilbert. 2024. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton U.P. p. 247; Lewontin, R. 1983. “Gene, organism, and environment.” In: Evolution from Molecules to Men. Bendall, D.S. (ed). pp. 273-285. Cambridge UP. p. 280.


“In all cells, excitability is underpinned by the thermodynamics of interfaces. Interfaces are formed by biomembranes that bind regions with different ionic compositions. Excitability emerges as a biophysical consequence of charge separation across biological membranes. This is regulated by the passage of ions between different cellular compartments through ion channels or biochemical signals initiated by metabotropic receptors. These ionic currents then regulate effector systems, including the cilium or contractility apparatus. The ionic homeostasis of the compartments is maintained by active pumping by ATPase pumps.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 1.


“Among motile organisms, strategies for navigation are often diverse and highly organism-specific. There are three major strategies for cells to track gradients of external cues (e.g. chemicals, light, temperature), which we shall refer to as stochastic navigation, spatial sensing and helical klino-taxis....

“In addition, there are passive forms of orientation, which we will not discuss in detail here. These include magnetotaxis in some proteobacteria and a euglenid alga. There are further idiosyncratic forms of environmental tracking that do not fall into any of the above navigation categories, such as active regulation of buoyancy in non-motile diatoms in order to move up and down in the water column.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 2.


“Chemotaxis in Dictyostelium [eukaryote] is perhaps the best studied from a biophysical perspective. In the absence of gradients, pseudopods extend randomly, but extensions become localized when gradients are detected. Cells can sense gradients, cells extend pseudopodia stochastically and retain the ones oriented towards a source of chemoattractant.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 4.


“Another unusual form of spatial sensing is found in the cyanobacterium Synechocystis, which is able to follow directional light cues. These cells act as spherical microlenses to focus incoming light to the opposite side of the plasma membrane. This localized stimulus induces motility in the direction of light.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 4.


“This [klinotaxis] is arguably the most sophisticated of cellular navigation strategies and occurs almost exclusively in eukaryotes.... During helical turns in a stimulus field, the cell tracks periodic changes in the stimulus, particularly in the direction perpendicular to the helix axis. By bending the helical trajectory in the stimulus direction, the cells can actively steer and migrate deterministically. Thus, helical klinotaxis is fundamentally different from stochastic navigation, and generally both more efficient and more robust to noise than other navigation types.

“Diverse eukaryotes from distinct phyla use helical klinotaxis to track chemical gradients (such as diffusing from a food source.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 4.


“Among prokaryotes, cell-cell fusion is rare, and mostly only incomplete and reversible. In some haloarchaea, the exchange of genetic material can occur through incomplete cell-cell fusion, during which cells are connected by cytoplasmic bridges.... Among cells of the spirochaete Borrelia, frequent outer membrane fusion and occasional inner membrane fusion were observed. A recent study reported complete interspecies cell-cell fusion with large-scale exchange of cellular components between the bacterial Clostridium ljungdahlii and Clostridium acetobutylicum.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. pp. 5-6.


“Eukaryotes are particularly susceptible to mechanical stimuli and changes in membrane geometry. Many eukaryotes exhibit mechanosensitivity. This allows them to respond actively to hydromechanical signals transmitted remotely through the fluid, without need for direct contact with a potential predator or prey.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 6.


“Stimuli that have the potential to harm or kill demand more immediate detection. This is fundamentally distinct from navigation or exploration, in terms of the timescales available for response. Most motile species harbour a form of phobic or emergency response distinct from their steady state locomotion....

“These fast reactions are usually induced by action potentials–unidirectional electrical pulses involving fast, regenerative changes in membrane potential. While all cells display some electrical activity, phylogenetic evidence suggests that the capacity to propagate action potentials may have been an ancestral eukaryotic trait supported by the LECA [last eukaryotic common ancestor]. These may have emerged in response to accidental membrane damage and sudden calcium influx. Bioelectrical signalling in the form of action potentials occurs orders of magnitude faster than any other signalling modalities, e.g. chemical diffusion, protein phosphorylation etc.

“In order to initiate fast escape responses, these may have been coupled directly to the motility apparatus–particularly to flexible, membrane-continuous structures such as cilia and pseudopodia.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 7.


“Eukaryotes manipulate their membrane potential to achieve transitions between different behaviours. Complex bioelectric sequences have been recorded in association with integrated feeding and predation behaviours in Favella.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 7.


“Action potential-like phenomena in prokaryotes are dissimilar from classical eukaryotic action potentials. The former are less reproducible, slower and exhibit a broader distribution in pulse amplitude and duration.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 7.


“In this section, we give an overview of the cellular innovations that contributed to the emergence of new forms of excitability during eukaryogenesis. These are (i) an extended repertoire of membrane receptors, channels and pumps, (ii) motility by cilia and pseudopodia, (iii) endomembranes and mitochondria as ionic compartments and intracellular capacitors, (iv) a flexible and reconfigurable membrane, (v) a larger size, (vi) new strategies for sensing.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 7.


“In eukaryotes, there is a vastly expanded repertoire of membrane channels, pumps and receptors, distributed across a highly compartmentalized cell. Comparative genomics indicates much of this diversity evolved during eukaryogenesis in stem eukaryotes and was present in the LECA.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 8.


“The regulation of motility, contractility, mechanosensation, tactic and temperature responses all rely on membrane excitability. The complexification and diversification of ion channels and receptor pathways was one of the major innovations that underpinned the evolution of the new forms of excitability in eukaryotes.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 8.


“The levels of free calcium are low in the cytoplasm and high in the endoplasmic reticulum (ER). Intracellular Ca2+ is kept low by the action of the plasma membrane calcium-transporting ATPase (PMCA), which counters the influx of Ca2+ at the plasma membrane. The influx of Ca2+ into the ER in turn is controlled by the sarcoplasmic/endoplasmic reticulum calcium ATPase Ca2+ pumps (SERCA). The ER and plasma membrane calcium systems are interlinked....

“The core Ca2+ transport systems of ER and plasma membrane channels and pumps have homologues across diverse eukaryotes and were likely present in the LECA.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 8.


“There may be as many as 18 distinct motility types across all forms of life. Among these, notable eukaryotic motilities include free-swimming by cilia and migration by pseudopodia.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 9.


“... cilia stand out with a unique propulsion-generating machinery that is very different from that of bacterial flagella or archaella. Bacterial flagella and archael archaella are extracellular structures, composed only of a few proteins plus a rotary motor and membrane-embedded base structure, whereas membrane-bound cilia have over 500 proteins. Unlike either of the prokaryotic structures, which are driven by rotary motors from one end, dynein motors populate the entire length of cilia. This is known as distributed force actuation, in stark contrast with boundary actuation (from only one end) in the prokaryotic appendages.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 9.


“The eukaryotic cell is distinguished from prokaryotic cells by a complex endomembrane topology. The endomembrane system contains several charged compartments–multiple membranous structures, including the ER, the vacuole and mitochondria, often with closely stacked lamellae (e.g. ER, plastids). This sophisticated structural organization evolved during eukaryogenesis and is critical to eukaryotic excitability.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 10.


“Another important aspect of eukaryotic endomembrane organization is the presence of several charged compartments with distinct ionic composition. These distinct compartments function as closed cellular capacitors. The compartments are separated by membrane layers with low conductivity that form a physical barrier between the conductive internal and external fluid. These cellular capacitors actively release and replenish charges, gated by channels and pumps, which alter potential differences across membranes. In neurons, the speed of charge propagation from a synapse is inversely proportional to the specific capacitance (Cm, capacitcance per unit area of the membrane) of the membrane. For the plasma membrane of animal cells, this is estimated to be approximately 1 μF cm-2.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 10.


“We propose that in eukaryotes, the presence of multiple circuits consisting of these capacitors and their gating machineries represents a novel form of information storage and parallel processing not seen in prokaryotes. These capacitors, and the control of their rapid charging and discharging by active currents, form new types of cellular logic gates. The organization of the eukaryotic endomembrane system has three important functional consequences for cellular capacitance. First, the network of thin membranes creates a large surface area for charge storage and high capacitance–bilayer membranes are typically only 5 nm thick....

“Second, membrane topology, comprising nested or closely apposed membranes, greatly influences charge distribution. Where multiple membranes are stacked in parallel, resistances add reciprocally, while capacitances add linearly. the placement of different capacitors in a cell influences charge redistribution, particularly during dynamic phenomena such as motility and feeding. Membrane-bound organelles can be as close as 10 nm from the plasma membrane. This physical proximity further ensures that coordinated signalling, or cross-talk, can occur near-synchronously across the different compartments....

“The third feature of the system is its ability to create and sustain nonlinear cycles of charging and discharging–a form of rapid bioelectric signalling.... These currents propagate throughout the cell, introducing temporal delays and thereby controlling the timing of signalling events, as has been demonstrated in nerve cells....

“We conclude that during eukaryogenesis, the evolution of compartmentalized capacitors significantly increased the degrees of freedom available for intracellular electrical signalling, making critical contributions to eukaryotic excitability and behaviour. This critical function of the complex eukaryotic endomembrane system as a master regulator of behaviour and physiology extends beyond its bioenergetic or metabolic advantages. By analogy with electronics, eukaryotic cells constitute a complex and dynamic network of coupled resistors and interleaved capacitors as charge sources of sinks, which are associated with multiple time constants. Collectively, these circuits and motifs function as timers, frequency filters, tuners and logic gates, whence complex behaviours can ensue.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. pp. 10-11.


“Except when covered by a cell wall, as are some fungal and plant cells, eukaryotic cells are morphable and undergo shape changes not seen in prokaryotes. One of the key steps of eukaryogenesis was the loss of the rigid glycoprotein cell wall of the archaea-derived host cell....

“The flexible plasma membrane in eukaryotes was a prerequisite for the evolution of total cell fusion, engulfment and membrane dynamics.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 11.


“Eukaryotic membranes not only have diverse pumps and channels but also have thousands of distinct lipid species compared with only hundreds in prokaryotic membranes.... Lipid diversity also encodes organellar identity (different compartments made up of different lipid species), thus preventing them from coalescing. This diversity may have indirectly contributed to maintaining distinct capacitative identities for electrical signalling in organelles....

“Membrane shape depends on a complex interplay of proteins and lipids, and is highly sensitive to the heterogeneous distribution of lipids, which promotes the formation of bends and curvatures.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 12,


“The constant spatio-temporal remodelling and turnover of membranes is a eukaryotic trait. Vigorous membrane turnover is observed in some species of Acanthamoeba, at an estimated complete turnover rate of several times per hour.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 12.


“Thus large, slowly moving cells will sense more effectively by spatial comparison....

“By contrast, small fast-moving cells use temporal sensing. Below a certain size (approx. 1 μm), cells become severely limited by rotational diffusion, so prokaryotes cannot maintain their orientation for long enough to steer deterministically toward gradients. Such cells must adopt stochastic random walks.” Wan, Kirsty Y. & Gaspar Jekely. 2021. “Origins of eukaryotic excitability.” Philosophical Transactions of the Royal Society: B. 376:20190758. 10.1098/rstb.2019.0758. p. 15.


“Our main thesis in the following text will be that self-activity, i.e., agency, is immanent in living organisms. There is no life without agency.” Rosslenbroich, Bernd, Susanna Kuemmell & Benjamin Bembe. 2024. “Agency as an Inherent Property of Living Organisms.” Biological Theory. 19:224-236. 10.1007/s13752-024-00471-7. p. 225.


“We define agency as the overall autonomous activity of the organism to maintain life functions, to establish and defend its processual relative autonomy, and to operate within the environment. It consists in the capacity of the system to perform the processes of its immediate existence as a living organism within a certain self-organized and inherited time structure and to respond actively to internal and external conditions and signals.

“This definition includes several aspects. A first aspect is that we include internal life-sustaining activities on the one hand, and engagements with the environment on the other hand within one principle....

“A second aspect is that agency and autonomy are interrelated, but not the same. In our definition agency focuses on the self-activity that generates the processes and the activity in the environment, while autonomy focuses on the capacity of resilience and flexibility of the organism. However, they are strongly interrelated, as an organism needs agency in order to generate autonomy and establishes autonomy in order to be an agent. So, they are like the familiar two sides of the same coin.

“A third point is that we set this organismic principle apart from a causal explanation. Agency as the overall autonomous activity is produced continuously and of its own accord by the organism.” Rosslenbroich, Bernd, Susanna Kuemmell & Benjamin Bembe. 2024. “Agency as an Inherent Property of Living Organisms.” Biological Theory. 19:224-236. 10.1007/s13752-024-00471-7. p. 226.


“The main emergent property of nervous systems is behavior: sedentary animals often have eliminated or strongly reduced their nervous systems, especially compared to ambulatory life stages (e.g. Tunicata - sea squirts), or never evolved any, as in the Porifera (sponges).” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 55.


“Thus, arguably, the most important question in neuroscience is whether there is a common organization to all behavior, and if so, what that organization looks like....

“Given the importance of the question, it is hardly surprising that the history of neuroscience is replete with hypotheses aiming to unify all behaviors under a common explanatory framework. However, the diversity of such hypotheses is relatively low. The literature is dominated by essentially two opposing hypotheses, one that sees nervous systems as passive organs (also called the sensorimotor hypothesis) and one that perceives them as active.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 55.


“This discovery of what we now call central pattern generators – neural circuits that can generate oscillatory activity in the absence of any stimulation – challenged the notion of nervous systems being passive organs. While ‘pacemaker’ neurons – neurons firing spontaneously, without requiring synaptic input – were hypothesized to exist for quite some time, it wasn’t until the 1960s that spontaneously firing neurons were actually discovered. Whereas these ‘pacemaker’ neurons fire either tonically or rhythmically, we now know that neurons can also show arhythmic, probabilistic spontaneous firing patterns, consistent with nonlinear dynamics.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 55.


“The dominant, passive perspective emphasizes the instructive properties of the environment and holds that stimulus and response are neuronally coupled such that any internal (i.e., cognitive) processes merely serve to modulate the sensorimotor coupling. Clearly, this approach has proven scientifically very productive. The second, active perspective emphasizes the control that behavior exerts over the environment and purports that intrinsic processes are the primary driver of behavior, and that external stimuli merely serve to modulate this cognitive process of generating actions....

“Passive-static perspective – [paraphrased from a figure] intrinsic activity/cognition modulates the causal link from stimulus to response. Active-dynamic perspective – stimulus modulates the causal link from intrinsic activity/ cognition to action....

“A less obvious corollary of this dichotomy is that there is an additional distinction between the two perspectives that cannot be readily captured in a figure: the passive perspective holds that the brain is static with ongoing fluctuations playing a subordinate role (if they are not considered noise), while the active perspective entails constant, ongoing dynamics at the heart of the functioning principle.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 56.


“Textbook reflexes such as the classic knee-jerk reflex are often depicted as consisting of only two neurons, a sensory neuron and a downstream motor neuron. A classic, minimal input-output system....

“A simple experimental manipulation allows for a controlled stimulation of this reflex. Precisely reproducible stimulation is passed to the reflex arc via a cuff electrode, placed around the spinal nerve comprising both sensory and motor fibers. An electromyogram (EMG) records the contractions of the muscle. Stimulation via the cuff electrode leads to two signals in the EMG, the early M-Wave, mediated by the direct stimulation of the motor neuron by the electrode [where the signal from the sensory neuron goes through the cuff and then immediately into the last, short segment of the neural connection to the motor neuron] and the later H-Reflex, mediated by the synaptic connection between the stimulated sensory neuron and the motor neuron [back at the spinal column].

“With this preparation one can now elicit the H-Reflex with always the exactly identical stimulation and measure the reflex amplitude with high precision. If one now stimulates the cuff electrode several times a day over weeks in experimental animals such as mice, rats or monkeys, the reflex amplitude shows considerable variability that arises from a number of disparate sources....

Superficially, it may seem as if this connectivity mimics closely the passive-static organization, with intrinsic processes modulating the reflex. However, this variability is central and not peripheral to the function of these reflexes, as we will see. The evidence for this centrality comes primarily from studies where the variability was used to operantly condition the H-Reflex. In such experiments, half of the experimental animals were rewarded with food whenever the amplitude of their H-Reflex was above baseline and half of the animals were rewarded for below-baseline H-Reflex amplitudes. In the course of such training, the animals which were rewarded for larger H-Reflex amplitudes increased their responses up to approximately double while the animals rewarded for smaller amplitudes decreased theirs up to about half.

“Such plasticity is remarkable in its own right for such a supposedly simple system and on its own raises doubts about the hypothesis that reflexes are simple input-output systems, responding always with the same response to the same triggering stimulus. However, when studying the more general consequences of the conditioning, it becomes clear that the input-output concept is at best superficial and at worst not even false. Observing the gait of the conditioned animals, it was suspicious that they did not appear to limp or exhibit any other gait-related abnormalities. This was surprising because these reflexes are engaged at every step and make coordinated locomotion possible. More detailed study of the animals revealed compensatory plasticity in the other legs to ensure the gait of the animals was not affected by the change in reflex amplitude of one particular joint in one leg.

“These results demonstrate that the actual mode of operation of stretch reflexes is actually the opposite of an input-output system, despite, at first, appearing to match a passive-static system perfectly: during walking, at every step when the reflex is elicited, a small change in amplitude is eliciting a response from the environment of the reflex providing feedback as the effectiveness of the reflex in controlling gait. The reflex generates an output (a change in amplitude) and evaluates re-afferent feedback to adjust the reflex-amplitude to current walking conditions. Reflexes are thus output-input systems, generating spontaneous output (a change in amplitude) and evaluating the consequences of these probing actions, reminiscent of trial and error problem-solving. As much of this spontaneous variability is not related to environmental stimuli, the definition of cognition above would include such internal generation of spontaneous behavioral activity as a cognitive process. Thus, reflexes do not seem to serve as good examples of the stimulus-response concept, rather the opposite. This is a case where the connectivity of the circuit may look deceivingly similar to a passive-static system, but studying its function, it becomes clear that it constitutes an active-dynamic system.

“This adaptive, cognitive component in behaviors as extreme as stretch reflexes not only challenges the notion that reflexes can be classified as ‘responses’ at all, it also begs the question how one can classify less extreme behaviors as responses? Surely, as soon as more neurons are involved, a behavior can only become less response-like and contain more ‘cognitive’ components?” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] pp. 58-9.


“Recording from all neurons in a leech ganglion, it is possible to reconstruct a state space reduced into three dimensions and follow the ganglion on its walk through state space over time. What emerges is an image of each trial starting on a trajectory similar to all other trials, but then quickly diverging towards a swim-space or the crawl-space. Such behavior is consistent with the dynamics of nonlinear systems: at first, nearby parameter sets evolve similarly, only to later diverge exponentially. One can also see a second hallmark of nonlinear systems in the leech data: basins of attraction or multistability. The walks through state space are not random even though they show a high degree of variability. The walks, instead, roughly seem to follow tracks that can be distinguished as swimming or crawling in the nerve recordings. These are all very familiar properties known from dynamical systems theory and evince a highly dynamical system, pushed by external stimuli sometimes into this basin of attraction, sometimes into the other. This image is starkly at odds with the passive-static input-output concept still prevalent in neuroscience.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 61.


“The sea slug uses its radula, a tongue-like organ, to grasp seaweed and pull it off the substrate and into its mouth. The animal’s vision is poorly developed and the smell or the superficial texture of the seaweed are unreliable predictors of its biomechanical properties, such as toughness or size. The different kinds of seaweeds it feeds on not only vary dramatically in these biomechanical properties before the animals arrive, but they can also change in response to herbivory, or once the animal has started to ingest. Therefore, Aplysia has no other choice than trying out how to best ingest the seaweed it is encountering. This process manifests itself not only in a high variability of behavioral parameters between each feeding attempt, but also during each attempt. Studying the slug nervous system and the biomechanics of the feeding system it controls, it was discovered that Aplysia is searching the state space of its motor system for the behavioral parameters that will get the job done. It does so by not only starting each attempt with a different set of parameters, but also by modifying these parameters online, during the feeding bout, while it is experiencing the responses of the seaweed. In the course of these adjustments, the animal not only varies the timing of when the neurons become active and how strongly, but also recruits different neurons into the sequence if the task requires it. What to the outside observer appears as two identical behaviors can be two neuronally very different processes. Thus, analogous to the stretch reflexes changing its parameters to probe the responses of the environment, also here, the feeding behavior is highly variable to quickly find suitable behaviors where no pre-arranged sequence can solve the problem. Feeding behavior in Aplysia, when studied on the neuronal level, is also organized in an output/input fashion. This feedback-based organization allows the animal to make moment-to-moment decisions while it is walking through state space to most efficiently find the basin of attraction in state space adequate for the particular food source (i.e., the solution space).” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 62.


“P. dumerilii [a marine polychaete that is a model system for the last common ancestor of all bilaterian animals] possess only sensory neurons that make direct contact with the ciliated cells that propel the animal in the water and have therefore been classified as ‘the simplest sensorimotor system’.

“In the first, dispersal phase of their development, P. dumerilii larvae are positively phototactic, while in the later stage, before metamorphosis to the adult worm, they become negatively phototactic.... The locomotor behavior of the animal is ongoing, even in the absence of any light hitting the photoreceptors. The movements of the larva are non-directional or random without stimuli to guide them, but they are ongoing even without any sensory input. The light activates the photoreceptors which, in turn, inhibit the ciliated cells on the ipsilateral side, such that the animal rotates towards the light by virtue of the ciliated cells contralateral to the light. If anything, this system would be classified as a motor-sensory system, as the behavior clearly is antecedent to the sensory stimulus inhibiting part of the already ongoing motor activity.

“These physiological results in an extant model for the Urbilaterian contribute to the hypothesis that early nervous systems evolved to organize a new method of animal motility: muscles. These early nervous systems first evolved to control muscle tissue. Only later were sensory organs connected to the motility organs, likely by feeding back re-afferent sensory input. Passive responses, to the extent that they can be unequivocally identified, are not primitive, but highly derived traits.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 64.


“Evidence from animals where explanted nervous systems survive for extended periods shows that even completely deafferented nervous systems are capable of generating coordinated motor programs that correspond to movements in intact animals.... The observation that a general concept of behavioral control is one of disinhibition, i.e., that appropriate behaviors are selected not by activating them, but by releasing them from tonic inhibition, is also consistent with an active-dynamic concept of nervous system function.

“These data suggest a view of nervous systems as constantly active dynamic systems that meander through state space, meta-stably switching between different attractor states while wobbling about, ready to be pushed into other states, e.g., by particularly salient stimuli.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 64.


“In the dynamic-active perspective of brain function, operant conditioning becomes central to understanding the organization of behavior in general: nearly every action will be generated in order to avoid aversive or obtain appetitive feedback. In this context, any exploratory behavior, whether it is concerned directly with sensory feedback or in order to explore a novel environment, is based on actions that are not based on antecedent stimuli.” Brembs, Bjorn. 2021. “The brain as a dynamically active organ.” Biochemical and Biophysical Research Communications. 564:55-69. [Unsure numbering] p. 65.


“Bernd-Olaf Kueppers proposes the formula of ‘Life = Matter + Information’, arguing that life should be understood as a dynamic interaction between matter and information, where information guides the processes that sustain living systems.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 1; reference: Kueppers, Bernd-Olaf. 2016. “The nucleation of semantic information in prebiotic matter.” In: Domingo, E. & P. Schuster (Eds). Quasispecies: From Theory to Experimental Systems 392, Current Topics in Microbiology and Immunology. pp. 23-42.


“To obtain a definition of information that can be used in biology, we refer to the triadic model of semiotics originally developed by philosopher Charles Peirce, namely ‘sign, object, and interpretant’ and replace interpretant with interpreter. Thus, in this paper, information is defined as follows:

“Information is defined as the process by which an interpreter connects a sign to an object.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 2.


“For biological information, interpreters are molecular machine. In this paper, biological information is defined as follow:

“Biological information is defined as the process by which molecular machines connect signs to objects.

“Or, in more detail,
“Biological information is defined as the process by which molecular machines connect signs or stimuli from the environment to molecules, physicochemical functions or processes.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 2.


“Therefore, for genetic information, the molecular machine consists of ribosomes, tRNAs, aminoacyl-tRNA-synthetases, elongation factors (such as EF-Tu and EF-G in prokaryots or eEF1A and eEF2 in eukaryotes), etc.; for signal transduction, molecular machines are membrane receptors.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 3.


“To illustrate how genetic information breaks the translational symmetry in physical laws, consider a sequence composed of the first 40 nucleotides of a mRNA gene, which contains five CGA triplets...

“There is no reason for the five CGA triplets to perform different functions in physicochemical processes, which is exactly what translational symmetry in physical laws requires, that is, the function of a CGA triplet does not depend on its position in the mRNA. However, during elongation, the ribosome moves three nucleotides at a time along the mRNA template toward the 3' direction, setting up a triplet reading-frame and effectively separating the gene nucleotide sequence into a gene codon sequence. Using hyphens to indicate codon boundaries, the above nucleotide sequence becomes
XXX-XXX-CGA-XXX-XCG-AXX-XXC-GAX-XXX-CGA-XXX-XXC-GAX-X

“This shows that the first (from the left) and fourth CGA triplets are codons encoding the amino acid proline, whereas the second, third and fifth CGA triplets are not codons and do not encode the amino acid proline. The codon CGA is universally decoded as the amino acid proline, indicating that it is not environmentally sensitive. On the other hand, due to the triplet reading-frame, the same CGA triplet located at different positions may be interpreted differently, indicating that it is positionally sensitive. This suggests that the function of a CGA triplet in translation depends on its position in the mRNA, showing that translational symmetry in physical laws no longer holds in genetic information.

“The irreversibility of translocation, in which ribosomes only move forward and not backward along the mRNA template, renders the entire genetic information process irreversible, violating the (microscopic) time-reversal symmetry in physical laws and the principle of microscopic reversibility in chemistry. In addition, the ribosome moves three nucleotides along the mRNA template with each elongation, resulting in the role of a nucleotide triplet in translation depending on its position in the mRNA, thus breaking the translational symmetry in physical laws. Consequently, genetic information is non-physicochemical.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 4.


“G-protein-linked receptors and enzyme-linked receptors also connect ligands and second messengers. Second messengers are associated with conformational changes in the receptor, which are triggered by, but not determined by, the binding of the ligand to the membrane receptor. Therefore, the ligand and second messenger are not directly connected to each other through physical laws. Experimentally, there are literally hundreds of ligands (hormones, growth factors, neurotransmitters, etc.), while only four known second messengers (cyclic AMP, calcium ions, inositol trisphosphate, and diacyclglycerol), indicating that the ligands and second messengers belong to two independent domains. In addition, laboratory experiments have shown that the same ligands can activate different second messengers, and different ligands can activate the same second messenger. This suggests that the correspondence between a ligand and second messenger depends on the biological context and is not determined merely by the laws of physics.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 4.


“The chemical arbitrariness of codon - amino acid assignments can be said to establish a language-like symbolic relationship between codons and amino acids. In this sense, genetic information is symbolic... Kalevi Kull emphasized that code’s being an arbitrary mapping can be defined as a relation that cannot be deduced from universal physical laws. Therefore, assigning an amino acid to a cognate (anti)codon does not follow the laws of physics.” Wang, Richard Liangchen. 2025. “Life is chemistry plus information.” BBA Advances. 7:100162. 10.1016/j.bbadva.2025.100162. p. 5; reference: Kull, Kalevi. 2020. “Codes: necessary, but not sufficient for meaning-making.” Constr. Found. 15:137-139.


“Meanwhile, in the relevant literature, ‘cause’ and ‘effect’ both are intuitive concepts. These have never been properly defined excepting that a notion of causation requires that causes and effects are well defined local factors and that there is asymmetry in determination of their relationship.” Yurchenko, Sergey B. “Is information the other face of causation in biological (conscious) systems?” [No other info to cite per Google Scholar; page numbering by online pdf] p. 1.


“In its most general form, reductionism is a physics-grounded postulate that the parts unilaterally determine the behavior of the whole. This is based on the three assumptions (Kim 1999): (i) once the microscale properties of a system are fixed, its macroscale properties are fixed too (supervenience); (ii) causal power resides fully at the microscale (micro causal closure); and (iii) if all the causal work is done at the microscale, there is no room for any causal contribution at the macroscale (macro causal exclusion). In contrast, holism, grounded in biology, neuroscience, and social sciences, argues that the whole is more than its parts: once the importance of complexity is recognized, there is an additional difference between observations of system components at the microscale and observations of the system at the macroscale (Anderson 1972, Bar-Yam 2004). Yurchenko, Sergey B. “Is information the other face of causation in biological (conscious) systems?” [No other info to cite per Google Scholar; page numbering by online pdf] p. 5; references: Kim, J. 1999. “Making sense of emergence.” Phil. Stud. 95(1): 3-36; Anderson, P.W. 1972. “More is different: broken symmetry and the nature of the hierarchical structure of science.” Science. 177: 393-396; Bar-Yam,Y. 2004. “A mathematical theory of strong emergence using multiscale variety.” Complexity. 9(6): 15-24.


“Complex dynamical systems exhibiting spontaneous self-organization give rise to emergent phenomena with various examples in nature like that of the (flickering) murmuration of starlings. Thus, global observables, associated with some supervenient macro-variable, can indeed provide synergistic information that cannot be obtained from local observables.” Yurchenko, Sergey B. “Is information the other face of causation in biological (conscious) systems?” [No other info to cite per Google Scholar; page numbering by online pdf] p. 19.


“What about the main question of this paper: Is information the other face of causation? The answer depends on how these two are conceptualized. Since information is epistemologically derived from causation, and they both are observer-dependent, the answer can be, yes. Moreover, there is now information theory based on the Shannon’s measure of uncertainty that allows to make exact mathematical predictions, but there is no theory of causation. In this sense, information (an epistemic map) is better than causation (an ontic territory).” Yurchenko, Sergey B. “Is information the other face of causation in biological (conscious) systems?” [No other info to cite per Google Scholar; page numbering by online pdf] pp. 19-20.


“The holist dictum ‘The whole is greater than the sum of its parts’ is observer-dependent and can be rephrased as ‘The view from above is better than the view from below.’ It is impossible to ‘see’ a multiscale modular hierarchy by looking at its elementary basis. Thus, the view from above indeed provides information gain, not affecting linear causal chains at any scale of description. On the other hand, the information gained at a macroscale is more than epistemic since it captures genuine (weakly emergent) properties of self-organization of complex systems, which cannot be inferred exclusively from microscale descriptions.” Yurchenko, Sergey B. “Is information the other face of causation in biological (conscious) systems?” [No other info to cite per Google Scholar; page numbering by online pdf] p. 20.


“The ideal of romantic love burst into Western society during the Middle Ages. It first appeared in our literature in the myth of Tristan and Iseult, then in the love poems and songs of the troubadours. It was called ‘courtly love’‘; its model was the brave knight who worshiped a fair lady as his inspiration, the symbol of all beauty and perfection, the ideal that moved him to be noble, spiritual, refined, and high-minded. In our time we have mixed courtly love into our sexual relationships and marriages, but we still hold the medieval belief that true love has to be the ecstatic adoration of a man or woman who carries, for us, the image of perfection.” Johnson, Robert A. 1983. We: Understanding the Psychology of Romantic Love. HarperSanFrancisco. p. xiii.


“French chemist Anselme Payen was the first to discover an enzyme, diastase, in 1833. A few decades later, when studying the fermentation of sugar to alcohol by yeast, Louis Pasteur concluded that this fermentation was caused by a vital force contained within the yeast cells called ‘ferments’, which were thought to function only within living organisms. He wrote that ‘alcoholic fermentation is an act correlated with the life and organization of the yeast cells, not with the death or putrefaction of the cells.’

“In 1877, German physiologist Wilhelm Kühne (1837–1900) first used the term enzyme, which comes from Ancient Greek ἔνζυμον (énzymon) 'leavened, in yeast', to describe this process The word enzyme was used later to refer to nonliving substances such as pepsin, and the word ferment was used to refer to chemical activity produced by living organisms.

“Eduard Buchner submitted his first paper on the study of yeast extracts in 1897. In a series of experiments at the University of Berlin, he found that sugar was fermented by yeast extracts even when there were no living yeast cells in the mixture. He named the enzyme that brought about the fermentation of sucrose ‘zymase’. In 1907, he received the Nobel Prize in Chemistry for ‘his discovery of cell-free fermentation’.

“The biochemical identity of enzymes was still unknown in the early 1900s. Many scientists observed that enzymatic activity was associated with proteins, but others (such as Nobel laureate Richard Willstätter) argued that proteins were merely carriers for the true enzymes and that proteins per se were incapable of catalysis. In 1926, James B. Sumner showed that the enzyme urease was a pure protein and crystallized it; he did likewise for the enzyme catalase in 1937. The conclusion that pure proteins can be enzymes was definitively demonstrated by John Howard Northrop and Wendell Meredith Stanley, who worked on the digestive enzymes pepsin (1930), trypsin and chymotrypsin. These three scientists were awarded the 1946 Nobel Prize in Chemistry.

“The discovery that enzymes could be crystallized eventually allowed their structures to be solved by x-ray crystallography. This was first done for lysozyme, an enzyme found in tears, saliva and egg whites that digests the coating of some bacteria; the structure was solved by a group led by David Chilton Phillips and published in 1965. This high-resolution structure of lysozyme marked the beginning of the field of structural biology and the effort to understand how enzymes work at an atomic level of detail. Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“The International Union of Biochemistry and Molecular Biology have developed a nomenclature for enzymes, the EC numbers (for "Enzyme Commission"). Each enzyme is described by "EC" followed by a sequence of four numbers which represent the hierarchy of enzymatic activity (from very general to very specific). That is, the first number broadly classifies the enzyme based on its mechanism while the other digits add more and more specificity.

“The top-level classification is:

EC 1, Oxidoreductases: catalyze oxidation/reduction reactions
EC 2, Transferases: transfer a functional group (e.g. a methyl or phosphate group)
EC 3, Hydrolases: catalyze the hydrolysis of various bonds
EC 4, Lyases: cleave various bonds by means other than hydrolysis and oxidation
EC 5, Isomerases: catalyze isomerization changes within a single molecule
EC 6, Ligases: join two molecules with covalent bonds.
EC 7, Translocases: catalyze the movement of ions or molecules across membranes, or their separation within membranes.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Enzymes are usually much larger than their substrates. Sizes range from just 62 amino acid residues, for the monomer of 4-oxalocrotonate tautomerase, to over 2,500 residues in the animal fatty acid synthase. Only a small portion of their structure (around 2–4 amino acids) is directly involved in catalysis: the catalytic site. This catalytic site is located next to one or more binding sites where residues orient the substrates. The catalytic site and binding site together compose the enzyme's active site. The remaining majority of the enzyme structure serves to maintain the precise orientation and dynamics of the active site.

“In some enzymes, no amino acids are directly involved in catalysis; instead, the enzyme contains sites to bind and orient catalytic cofactors. Enzyme structures may also contain allosteric sites where the binding of a small molecule causes a conformational change that increases or decreases activity.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Enzymes must bind their substrates before they can catalyse any chemical reaction. Enzymes are usually very specific as to what substrates they bind and then the chemical reaction catalysed. Specificity is achieved by binding pockets with complementary shape, charge and hydrophilic/hydrophobic characteristics to the substrates. Enzymes can therefore distinguish between very similar substrate molecules to be chemoselective, regioselective and stereospecific.

“Some of the enzymes showing the highest specificity and accuracy are involved in the copying and expression of the genome. Some of these enzymes have ‘proof-reading’ mechanisms. Here, an enzyme such as DNA polymerase catalyzes a reaction in a first step and then checks that the product is correct in a second step. This two-step process results in average error rates of less than 1 error in 100 million reactions in high-fidelity mammalian polymerases. Similar proofreading mechanisms are also found in RNA polymerase, aminoacyl tRNA synthetases and ribosomes.

“Conversely, some enzymes display enzyme promiscuity, having broad specificity and acting on a range of different physiologically relevant substrates. Many enzymes possess small side activities which arose fortuitously (i.e. neutrally), which may be the starting point for the evolutionary selection of a new function.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Enzymes can accelerate reactions in several ways, all of which lower the activation energy (ΔG‡, Gibbs free energy)

“By stabilizing the transition state:
Creating an environment with a charge distribution complementary to that of the transition state to lower its energy
“By providing an alternative reaction pathway:
Temporarily reacting with the substrate, forming a covalent intermediate to provide a lower energy transition state
“By destabilizing the substrate ground state:
Distorting bound substrate(s) into their transition state form to reduce the energy required to reach the transition state
“By orienting the substrates into a productive arrangement to reduce the reaction entropy change (the contribution of this mechanism to catalysis is relatively small)

“Enzymes may use several of these mechanisms simultaneously. For example, proteases such as trypsin perform covalent catalysis using a catalytic triad, stabilize charge build-up on the transition states using an oxyanion hole, complete hydrolysis using an oriented water substrate.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Enzymes are not rigid, static structures; instead they have complex internal dynamic motions – that is, movements of parts of the enzyme's structure such as individual amino acid residues, groups of residues forming a protein loop or unit of secondary structure, or even an entire protein domain. These motions give rise to a conformational ensemble of slightly different structures that interconvert with one another at equilibrium. Different states within this ensemble may be associated with different aspects of an enzyme's function. For example, different conformations of the enzyme dihydrofolate reductase are associated with the substrate binding, catalysis, cofactor release, and product release steps of the catalytic cycle, consistent with catalytic resonance theory. The transitions between the different conformations during the catalytic cycle involve internal viscoelatic motion that is facilitated by high-strain regions where amino acids are rearranged.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Allosteric sites are pockets on the enzyme, distinct from the active site, that bind to molecules in the cellular environment. These molecules then cause a change in the conformation or dynamics of the enzyme that is transduced to the active site and thus affects the reaction rate of the enzyme. In this way, allosteric interactions can either inhibit or activate enzymes.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Some enzymes do not need additional components to show full activity. Others require non-protein molecules called cofactors to be bound for activity. Cofactors can be either inorganic (e.g., metal ions and iron–sulfur clusters) or organic compounds (e.g., flavin and heme). These cofactors serve many purposes; for instance, metal ions can help in stabilizing nucleophilic species within the active site. Organic cofactors can be either coenzymes, which are released from the enzyme's active site during the reaction, or prosthetic groups, which are tightly bound to an enzyme. Organic prosthetic groups can be covalently bound (e.g., biotin in enzymes such as pyruvate carboxylase).” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Coenzymes are small organic molecules that can be loosely or tightly bound to an enzyme. Coenzymes transport chemical groups from one enzyme to another. Examples include NADH, NADPH and adenosine triphosphate (ATP). Some coenzymes, such as flavin mononucleotide (FMN), flavin adenine dinucleotide (FAD), thiamine pyrophosphate (TPP), and tetrahydrofolate (THF), are derived from vitamins. These coenzymes cannot be synthesized by the body de novo and closely related compounds (vitamins) must be acquired from the diet. The chemical groups carried include:

the hydride ion (H−), carried by NAD or NADP+
the phosphate group, carried by adenosine triphosphate
the acetyl group, carried by coenzyme A
formyl, methenyl or methyl groups, carried by folic acid and
the methyl group, carried by S-adenosylmethionine

“Since coenzymes are chemically changed as a consequence of enzyme action, it is useful to consider coenzymes to be a special class of substrates, or second substrates, which are common to many different enzymes. For example, about 1000 enzymes are known to use the coenzyme NADH.

“Coenzymes are usually continuously regenerated and their concentrations maintained at a steady level inside the cell. For example, NADPH is regenerated through the pentose phosphate pathway and S-adenosylmethionine by methionine adenosyltransferase. This continuous regeneration means that small amounts of coenzymes can be used very intensively. For example, the human body turns over its own weight in ATP each day.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“As with all catalysts, enzymes do not alter the position of the chemical equilibrium of the reaction. In the presence of an enzyme, the reaction runs in the same direction as it would without the enzyme, just more quickly....

“The rate of a reaction is dependent on the activation energy needed to form the transition state which then decays into products. Enzymes increase reaction rates by lowering the energy of the transition state. First, binding forms a low energy enzyme-substrate complex (ES). Second, the enzyme stabilises the transition state such that it requires less energy to achieve compared to the uncatalyzed reaction (ES‡). Finally the enzyme-product complex (EP) dissociates to release the products.

“Enzymes can couple two or more reactions, so that a thermodynamically favorable reaction can be used to "drive" a thermodynamically unfavourable one so that the combined energy of the products is lower than the substrates. For example, the hydrolysis of ATP is often used to drive other chemical reactions.” Wikipedia entry on “enzyme”. Accessed May 21, 2025.


“Catalysts enable pathways that differ from the uncatalyzed reactions. These pathways have lower activation energy. Consequently, more molecular collisions have the energy needed to reach the transition state. Hence, catalysts can enable reactions that would otherwise be blocked or slowed by a kinetic barrier. The catalyst may increase the reaction rate or selectivity, or enable the reaction at lower temperatures.” Wikipedia entry on “catalysis”. Accessed May 21, 2025.


“Collectively, these experiments suggest that enzymatic catalysis could be understood in terms of physical organic principles. One striking feature, seen repeatedly, is that the catalytic elements in an active site are precisely positioned for their function.” Benkovic, Stephen J. & Sharon Hammes-Schiffer. 2003. “A Perspective on Enzyme Catalysis.” Science. 301:1196-1202. August 29. p. 1197.


“Although enzyme systems involve the motions of many atoms, typically the free-energy profile is projected onto a single collective reaction coordinate, and the transition state is identified with the configuration at the top of the free-energy barrier [in a graph of the collective coordinate].” Benkovic, Stephen J. & Sharon Hammes-Schiffer. 2003. “A Perspective on Enzyme Catalysis.” Science. 301:1196-1202. August 29. p. 1198.


“Recent theoretical studies indicate that thermally averaged, equilibrium motions representing conformational changes along the collective reaction coordinate play an important role in enzymatic reactions. These motions are averaged over the fast vibrations of the enzyme and occur on the time scale of the catalyzed chemical reaction. They reflect the conformational changes that generate transition-state configurations conducive to the chemical reaction and thereby influence the activation free-energy barrier.” Benkovic, Stephen J. & Sharon Hammes-Schiffer. 2003. “A Perspective on Enzyme Catalysis.” Science. 301:1196-1202. August 29. p. 1198.


“This active-site plasticity has been exploited in rational strategies to reshape enzyme specificities where often single substitutions change a substrate specificity. Linoleate 13-lipoxygenase, for example, is changed to a 9-lipoxygenating species by a His Val mutation that demasks a positive charge at the bottom of the active site and changes the orientation of the fatty-acid substrate. In a more extreme example, a stretch of 13 amino acids within the active site of Thermus aquaticus DNA polymerase 1 was extensively randomly mutated, giving rise to a library of ~8,000 active mutants. Several mutants show polymerase activity higher than that of wild-type enzyme, and others have the ability to incorporate ribonucleotide analogs. On the other hand, manipulating the specificity of aspartate aminotransferase to favor valine required changes in 17 amino acids, only one of which is within the active site.” Benkovic, Stephen J. & Sharon Hammes-Schiffer. 2003. “A Perspective on Enzyme Catalysis.” Science. 301:1196-1202. August 29. pp. 1200-1201.


“... the majority of enzymes are far from being highly efficient and/or highly selective catalysts. A chemist may wonder whether the rate of all reactions can be accelerated to the same level or if there are some fundamental chemical constraints that result in reaction- or mechanism-specific rate limits. From an evolutionary perspective, on the other hand, this ‘mediocrity’ may reflect limited physiological demand; that is, no fitness benefit for higher enzyme performance. Indeed, the fluxes of metabolic reactions catalyzed by enzymes differ widely, and demand for high catalytic efficiency should vary accordingly.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8786.


“Among the most well-known physicochemcial constraints acting on enzymatic rates is the diffusion rate limit: the point at which collisional frequency alone dictates the rate of substrate conversion into product.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8788.


“Despite their rarity, those enzymes that do approach the diffusion rate limit (so-called perfect enzymes) are important to our understanding of enzymes in general. For example, a perfect enzyme, by definition, makes no futile encounters with its substrate. By comparison, only 1 out of 104 encounters of an average enzyme with its substrate are productive, with the overwhelming majority being futile (resulting in substrate dissociation). The dominance of futile encounters stems from several factors, including conformational heterogeneity. Both enzymes and substrates adopt multiple conformational states, only a subset of which is catalytically competent. Consistent with this interpretation, many of the known perfect enzymes have notably simple substrates, such as carbon dioxide or dihydroxyacetone phosphate.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8788.


“... it is unclear if reactions with fast spontaneous rates tend to have fast catalyzed rates as well. Or have enzymes been primarily shaped by physiological demands, such that their catalytic efficiencies bear no correlation to the spontaneous rates of the reactions they catalyze?

“To systematically examine this question, we compared catalytic efficiencies across various hydrolysis reactions. A variety of biochemical bonds are cleaved via hydrolysis, with large differences in inherent reactivity.... As can be seen [in data chart], the kcat values of hydrolytic enzymes are widely distributed, yet neither the median kcat values not the maximum kcat values within a given reaction class show a clear relation to typical spontaneous reaction rates.

“Overall, the median values of kcat are remarkably consistent between different hydrolytic reaction classes, as well as with the full set of enzymes.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8789.


“For many catalyzed reactions, there appear to be multiple catalytic solutions, that is, fundamentally different mechanisms and corresponding active-site architectures. So, do different catalytic solutions dictate different rates? To answer this question, enzymes catalyzing the same reaction were classified as analogues or homologues and their catalytic properties were compared.

“Analogous enzymes are enzymes that catalyze the same reaction yet are evolutionary[ily] unrelated. Accordingly, they have a different overall structure (fold) and may also apply a different catalytic mechanism that is, in turn, achieved via a completely different active-site architecture....

“If homologous enzymes tend to have more similar kcat values than analogues that catalyze the same reaction, it suggests that evolutionary origin, and by extension a protein’s fold and catalytic mechanism, may have a role in shaping enzyme rates. Conversely, if enzymes that are analogues, and are therefore evolutionarily and structurally unrelated, tend to have kcat values about as similar as homologues, then evolutionary origin, mechanism, and active-site architecture have a relatively minor role in shaping enzyme rates.

“To examine whether such trends exist, we performed a pairwise comparison of homologous and analogous enzymes for the entire collection of enzymes in BRENDA. A comparison of pairs of homologues to pairs of analogues catalyzing the same reaction shows a mere 1.5 average difference in rates for a give reaction.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. pp. 8789-90.


“The preceding sections suggest that physicochemcial constraints play a relatively minor role in shaping the kinetic parameters of enzymes.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8790.


“Apart from chance, an evolutionary factor that is routinely underestimated, we should keep in mind that rate (kcat/KM or kcat) is not the only, and possibly not even the primary, enzymatic trait under selection. Selectivity (or accuracy) and regulation are also under selection, and both of these traits often trade off with turnover rate (kcat) and/or catalytic efficiency (kcat/KM). For enzymes evolving under such trade-offs, kcat would be anticorrelated with enzyme levels, as exemplified by Rubisco, whose rate trades off with CO2/O2 selectivity and whose cellular levels tend to be exceedingly high. Trade-offs between tight regulation and high rate have also been observed.... Another explanation regards a trait that is potentially under selection: secondary, moonlighting functions. Many metabolic enzymes, for example, also act as transcriptional regulators, and the latter role may shape their cellular levels. Additionally, most metabolic enzymes are part of complexes, ranging from tight associations with well-defined stoichiometry to transient complexes and metabolons.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8792.


“Overall, it appears that many enzymes operate below their catalytic capacity; namely, they are generally expressed at levels higher than needed to support the flux of the reaction they catalyze. Furthermore, it appears that, for a given organism, many if not most enzymes could readily evolve toward higher kcat values. However, although higher catalytic efficiency may allow reduced cost, owing to lower enzyme levels, it might trade off with other critical traits such as regulation or secondary functions and/or might disturb complex stoichiometry.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8792.


“Convergent evolution of chemically similar, if not identical, active sites is a common process. For example, three carbonic anhydrase families with completely different folds but with strikingly similar active-site architectures have been identified. These enzyme families most likely arose via completely independent evolutionary origins, although common ancestry at very early stages (parallel evolution) is nearly impossible to rule out.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. pp. 8792-3.


“The convergence of active-site configurations that execute similar reactions via the same mechanism suggests that, given 20 amino acids and a finite set of available cofactors (organic or inorganic), by and large there seems to be one relatively simple, readily accessible, catalytically competent active-site configuration. That convergence of active-site architecture is the outcome of chemical constrain[t]s is also manifest in independently emerged active sites possessing mirror-image architectures, for example, in metallolactonases. Obviously, when a cofactor is the key catalytic element, as is the case with PLP enzymes or with metalloenzymes, similar active sites can evolve time and again while converging on the same chemistry.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8793.


“Overall, it appears that chemical constraints limit the space of evolutionary solutions. However, for the majority of enzyme classes, multiple mechanisms, and accordingly, multiple active-site architectures, have emerged independently. Furthermore, even within the same mechanism, there exist variations on the theme that indicate multiple alternative catalytic configurations. This chemical diversity suggests that, despite chemical constrain[t]s, de novo emergences of enzymatic active sites have occurred repetitively and throughout evolutionary time.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8794.


“By considering the entire repertoire of known natural enzymes, we can better understand the various driving forces and constraints that have shaped natural enzymes. The signatures of chemical constraints are visible, be they thermodynamic and kinetic constraints (e.g., the diffusion rate limit), or constraints acting on catalytic mechanisms and thereby on active-site architectures. Nevertheless, evolution seems to have largely overcome hurdles related to high activation barriers and complex reaction mechanisms. Further, multiple independent catalytic solutions have emerged for many reactions and, overall, their catalytic efficiencies do not differ much.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8794.


“Mutations generally arise one at a time, and rate improvement in one enzyme is typically insufficient to exert an advantage or may even be deleterious to the overall metabolic network. Thus, while it could well be that the vast majority of enzymes have the potential to evolve a significantly higher catalytic efficiency, such a global improvement is evolutionarily inaccessible.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8794.


“Finally, it appears that chemical constraints dictate the composition and geometry of active-site residues. Nonetheless, the same active-site chemistry, as well as alternative active-site chemistries, seem to have emerged time and again. It may well be that some folds and active-site architectures are evolutionarily related in the very distant past and have since diverged beyond recognition. However, a more likely hypothesis is that the de novo emergence of a new enzyme may not be as improbable as is generally assumed. Not much is known on how active sites emerge de novo in scaffolds devoid of catalytic capabilities. However, two de novo emergences of natural enzymes from noncatalytic proteins have been recently unraveled. In both cases, gradual and smooth emergence of catalysis, including stereo- and regioselectivity, could be reconstructed by starting from ligand binding pockets that exhibited no catalysis.” Davidi, Dan, Liam M. Longo, Jagoda Jablonska, Ron Milo & Dan S. Tawfik. 2018. “A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.” Chemical Reviews. 118:8786-97. 10.1021/acs.chemrev.8b00039. p. 8794.


“A complete description of proteins requires a multidimensional energy landscape that defines the relative probabilities of the conformational states (thermodynamics) and the energy barriers between them (kinetics).” Henzler-Wildman, Katherine & Dorothee Kern. 2007. “Dynamic personalities of proteins.” Nature. December 13. 450:964-72. 10.1038/nature06522. p. 964.


“To avoid past semantic confusion about the term protein dynamics, we define it as any time-dependent change in atomic coordinates. Protein dynamics thus includes both equilibrium fluctuations and non-equilibrium effects. The fluctuations observed at equilibrium seem to govern biological function in processes both near and far from equilibrium; therefore, we focus on these motions. Non-equilibrium effects are also called dynamical effects (the source of confusion), and they have a minimal effect on the overall rates of biological processes.” Henzler-Wildman, Katherine & Dorothee Kern. 2007. “Dynamic personalities of proteins.” Nature. December 13. 450:964-72. 10.1038/nature06522. p. 964.


“Turnover happens on the timescale of these tier-0, collective, large-amplitude motions [micro second to millisecond]. However, small-amplitude atomic thermal fluctuations occur on the picosecond timescale.... Increased picosecond dynamics were observed in the same places where the local backbone conformation must change for lid closure [small change in enzyme before larger conformation change can occur] to occur.... This striking correspondence suggests that the physical origin of the catalytically important collective domain motions (microsecond to milliseconds) is the fast-timescale (picoseconds to nanoseconds) local hinge motions [of a ‘lid’] ....

“This example illustrates how the hierarchy of protein dynamics in space and time arises from the protein structure encoded by the amino-acid sequence and is ultimately connected to enzyme function. Tier-0 transitions are improbable, and therefore slow, events that arise from many individual attempts by local groups to overcome the energy barrier. The low success rate results from the collective nature of these large-scale motions.” Henzler-Wildman, Katherine & Dorothee Kern. 2007. “Dynamic personalities of proteins.” Nature. December 13. 450:964-72. 10.1038/nature06522. p. 971.


“Biological function is ultimately rooted in the physical motions of biomolecules. Many biological processes are controlled by alterations in rates and relative populations rather than by a simple ‘on-off’ switch. For example, enzymes speed up chemical reactions, and changes in intracellular ion concentrations trigger complex neurological processes. Considering the immense rate enhancements and equilibrium shifts that are achieved in biological systems, it is easy to overlook the fact that only small changes in free energy (around a few kT) account for these effects, owing to the exponential dependence of both the rate and the populations on the free-energy difference. In other words, the breaking of a few hydrogen bonds or van der Waals contacts in a protein, which contains hundreds to thousands of such interactions, can turn on a signalling cascade or catalyse a chemical reaction. Importantly, intrinsic protein dynamics can happen only in this free-energy range of several kT.” Henzler-Wildman, Katherine & Dorothee Kern. 2007. “Dynamic personalities of proteins.” Nature. December 13. 450:964-72. 10.1038/nature06522. p. 971.


“Because biological function is the property selected by evolution, we propose that the conformational substates sampled by a protein, and the pathways between them, are not random but rather a result of the evolutionary selection of states that are needed for protein function. Signal transduction, enzyme catalysis and protein-ligand interactions occur as a result of the binding of specific ligands to complementary pre-existing states of a protein and the consequent shifts in the equilibria. In other words, the dynamic landscape is an intrinsic property (or ‘personality’) of a protein and is encoded in its fold, and the ligand does not induce the formation of a new structure but, instead, selects a pre-existing structure.” Henzler-Wildman, Katherine & Dorothee Kern. 2007. “Dynamic personalities of proteins.” Nature. December 13. 450:964-72. 10.1038/nature06522. p. 971.


“A protein does not exist in a unique conformation but can assume a very large number of somewhat different conformations or conformational substates. A particular substate is characterized by the coordinates of all atoms, including the hydration shell.... If a protein had just a single conformation, it could not function and would be dead like a stone.

“Experiments show that the protein motions fall, crudely speaking into two classes, slaved and nonslaved. Non-slaved motions are nearly independent of the motions in the solvent. Slaved motions have the same temperature dependence as the configurational dielectric fluctuations in the solvent, but are slower. This observation has consequences, both for the function of proteins and for the understanding of the energy landscape. Consider first the function. Exit and entrance of ligands such as CO and O2 are slaved, they are controlled by the environment. It is as if the drawbridges [figurative for what gets into the protein] ... were controlled from the outside of the castle! Slaving also changes the interpretation of the barriers between protein substates. Initially they were assumed to be given by enthalpy barriers intrinsic to the protein, but because the solvent determines the temperature dependence of the slaved transition rates, the internal barriers must be entropic. To open a gate, the protein must make a random walk in the energy landscape and the number of steps must be very large. Such a random walk is only possible if the protein has a sufficient number of substates or, in other words, has sufficient entropy. The logical place for the entropy is the bailey [the bulk of the protein], the part of the protein away from the active center. This model could explain the size of proteins.” Frauenfelder, H., B.H. McMahon & P.W. Fenimore. 2003. “Myoglobin: The hydrogen atom of biology and a paradigm of complexity.” PNAS. 100(15):8615-7. 10.1073/pnas.16336888100. p. 8616.


“Molecular recognition refers to the process in which biological macromolecules interact with each other or with various small molecules through noncovalent interactions to form a specific complex. This process has two important defining characteristics: (i) specificity, which distinguishes the highly specific binding partner from less specific partners; (ii) affinity, which determines that a high concentration of weakly interacting partners cannot replace the effect of a low concentration of the specific partner interacting with high affinity.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 1.


“In analogy with any spontaneous process, protein-ligand binding occurs only when the change in Gibbs free energy (ΔG) of the system is negative when the system reaches an equilibrium state at constant pressure and temperature. Because the protein-ligand association extent is determined by the magnitude of the negative ΔG, it can be considered that ΔG determines the stability of any given protein-ligand complex, or, alternatively, the binding affinity of a ligand to a given acceptor.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 3.


“ΔG can also be parsed into it enthalpic and entropic contributions with the following fundamental equation:

ΔG = ΔH - TΔS

where ΔH and ΔS are change in enthalpy and entropy of the system upon ligand binding, respectively, and T is the temperature in Kelvin.

“Enthalpy is a measure of the total energy of a thermodynamic system, i.e., the sum of the internal energies of the solute and solvent and the amount of energy required to make room for the system (calculated as the product of the system volume and the pressure). ΔH is negative and positive in the exothermic (i.e., formations of the energetically favorable noncovalent interactions between atoms) and the endothermic (i.e. disruptions of the energetically favorable noncovalent interactions) processes, respectively. For binding process, ΔH, or the binding enthalpy, reflects the energy change of the system when the ligand binds to the protein. The binding enthalpy in a non-strict sense is generally treated as the changes in energy resulting from the formations of noncovalent interactions (van der Waals contacts, hydrogen bonds, ion pairs, and any other polar and apolar interactions) at the binding interface. However, the heat effect of a binding reaction is a global property of the entire system, including contributions not only from the solute, but also from the solvent, and it is barely conceivable to form favorable interactions without disrupting any others. In fact, the change in enthalpy upon binding is a result of forming and disrupting many individual interactions, including the loss of the hydrogen bonds and van der Waals interactions formed between the protein and solvent and between the ligand and solvent, the formation of the noncovalent interactions between the protein and ligand, and the solvent reorganization near the complex surfaces. These individual components may make either favorable or unfavorable contributions, and the net enthalpy change is a result of the combination of these contributions....

“ΔS is a global thermodynamic property of a system, with its positive and negative signs indicating the overall increase and decrease in degree of the freedom of the system, respectively. The total entropy change associated with binding (the binding entropy ΔS) may be parsed into three entropic terms:

ΔS = ΔSsolv + ΔSconf + ΔSr/t

where ΔSsolv represents the solvent entropy change arising mainly from surface burial that results in solvent release upon binding, which often makes a favorable contribution to the binding entropy due to its large positive value; ΔSconf represents the conformational entropy change reflecting the changes in the conformational freedom of both the protein and ligand upon binding, which may contribute favorably or unfavorably to the binding entropy because the degree of freedom of the complex may increase or reduce as compared to those of the unbound, free protein and ligand; ΔSr/t represents the loss of translational and rotational degrees of freedom of the protein and ligand upon complex formation, which reduces the number of particles in solution and contributes unfavorably to the binding entropy.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. pp. 4-5.


“In fact, both the protein folding and protein-ligand binding processes are driven by the decrease in total Gibbs free energy of the system. The only difference between them is the presence and absence of the chain connectivity, which leads to two different terms: intramolecular and intermolecular recognition and binding.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 5.


“... two thermodynamic quantities, the enthalpy change and entropy change, determine the sign and magnitude of the binding free energy. We therefore consider ΔH and ΔS as the driving factors for protein-ligand binding. The contributions of ΔH and ΔS to ΔG are closely related. For instance, the tight binding resulting from multiple favorable noncovalent interactions between association partners will lead to a large negative enthalpy change, but this is usually accompanied by a negative entropy change due to the restriction of the mobility of the interacting partners, ultimately resulting in a medium-magnitude change in binding free energy. Similarly, a large entropy gain is usually accompanied by an enthalpic penalty (positive enthalpy change) due to the energy required for disrupting noncovalent interactions. This phenomenon–the medium-magnitude free energy change caused by the complementary changes between enthalpy and entropy–is called the enthalpy-entropy compensation.

“It should be noted that this phenomenon has been a subject of debate for decades. The main criticisms are that the compensation could be (i) a misleading interpretation of the data obtained from a relatively narrow temperature range or from a limited range for the free energies; (ii) the result of random experimental and systematic errors; and (iii) the result of data selection bias. Nevertheless, enthalpy-entropy compensation has been very frequently observed in thermodynamic binding studies of biological systems, and analyses of collected calorimetric data for protein-ligand binding and results from theoretical studies suggest that it is a genuine and common physical phenomenon, although stringent criteria for the assignment of true compensation effects must be adhered to.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 5.


“Three different models, the ‘lock-and-key’, ‘induced fit’ and ‘conformational selection’, have been proposed to explain the protein-ligand binding mechanisms.... ...the lock-and-key model cannot explain the experimental evidence that a protein binds its ligand when their initial shapes do not match well. This leads to the induced fit model, which assumes that the binding site in the protein is flexible and the interacting ligand induces a conformational change at the binding site. Because the induced fit mechanism takes into account only the conformational flexibility of the ligand-binding site, this model seems to be suitable for proteins showing merely minor conformational change after the ligand binding. In addition, both the lock-and-key and the induced fit models treat the protein as a single, stable conformation under given experimental conditions. However, most proteins are inherently dynamic and the conformational selection model takes into account this inherent flexibility. The conformational selection model, which derives from the free energy landscape (FEL) theory of protein structure and dynamics, postulates that the native state of a protein does not exist as a single, rigid conformation but rather as a vast ensemble of conformational states/substates that coexist in equilibrium with different population distributions, and that the ligand can bind selectively to the most suitable conformational state/substate, ultimately shifting the equilibrium towards this state/substate.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 6.


“The perfectly matched interfaces between the protein and the ligand under the key-and-lock model make it possible for the initial collision to trigger a complete displacement of the water networks surrounding the interaction interfaces, thus producing a large amount of the solvent entropy. In addition, under the rigid hypothesis, there is no change in the conformational entropy. Therefore, for the lock-and-key binding to proceed, the solvent entropy gain should be large enough to overcompensate for not only the positive enthalpy change arising from the desolvation process, but also the negative entropy change caused by the loss of rotational and translational motions of the ligand.

“Indeed, the negative enthalpy change rising from the favorable interactions (such as van der Waals forces, hydrogen bonding, electrostatic, and dipole-dipole interactions) can also contribute to the lowering of the system’s free energy, but the solvent entropy gain arising from the displacement of the water molecules plays a dominant role in lowering the free energy. Therefore, it is reasonable to conclude that the lock-and-key binding is a entropy-dominated process.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 8.


“For the binding to take place under the induced fit model, the lack of perfect surface complementary[ity?] between binding partners necessitates multiple tentative collisions to achieve an appropriate match between the interacting sites. The initially established contacts (negative enthalpy change) between the matched sites should be strong enough to provide the encounter complex enough strength and longevity so that induced fit takes place within a reasonable time. In addition, the amount of the released constrained water molecules upon encounter complex formation, although smaller than that in the lock-and-key model due to the imperfectly matched interacting sites, can also make a favorable contribution to the stability of the encounter complex. The subsequent induced fit is in essence a process of adjusting conformation of the binding site to suit the needs of the incoming ligand, ultimately leading to maturation of the encounter complex into a fully bound complex. This process is also accompanied by the release of the water molecules and, moreover, because of the excellent shape match between the binding partners in the fully bound complex, the amount of released water in the overall process of the induced fit binding can be expected to be as much as that of the lock-and-key binding. As a result, the solvent entropy gain also contributes favorably to the induced fit binding. Nevertheless, the net entropy change of binding is determined by the three entropic terms, i.e., ΔSsolv, ΔSconf, and ΔSr/t .... In the case of the induced fit binding, it can be speculated that the ΔSconf term is negative since the formed favorable noncovalent interactions between the binding partners restrict the conformational freedoms of the interacting interfaces. Such an unfavorable ΔSconf term, together with the unfavorable (negative) ΔSr/t term (due to the loss of rotational and translational degrees of freedom of the binding partners, tends to compensate for the favorable (positive) ΔSsolv term, ultimately leading to a relatively small net entropy change compared to the net enthalpy change.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 9.


“In addition, the conformational selection model takes into account the distribution and redistribution of the populations of protein conformational states/substates, which allow a protein to interact with multiple structurally distinct binding partners and accommodate mutations through shifts of the dynamic FEL, as, as such, is evolutionary advantageous.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 10.


“Under the background of the funnel-like FEL, the lock-and-key may be viewed as an ‘extremity’ of the conformational selection. A high-rigidity protein has a very smooth folding funnel where there is no ruggedness around the bottom of the funnel, thus resulting in only one conformer that occupies a single free energy well in the global free energy minimum region. A high-flexibility protein has multiple free energy minima (or wells) within which ensembles of different conformational states/substates are located. However, the conformational selection model assumes that the selective binding occurs only in one free energy well that contains the most suitable conformer for binding, thus resembling the lock-and-key binding occurring in the single, global free energy minimum well. The difference between these two models is that conformational selection induces a population shift and the redistribution of the states/substates, whereas the population shift cannot be presented in the lock-and-key model. Another difference between these models, as proposed by Nussinov et al is the ‘selected object’, which is a conformer out of many different conformers in the ensemble of the same protein for the conformational selection model and a protein out of many different proteins for the lock-and-key model. As a result, they suggested that the lock-and-key mechanism addressed the question of which protein-out of the many in the cell–will be bound by a given ligand.

“The induced fit involved in the conformational adjustments is a key step in the conformational selection mechanism, and the enhanced interactions resulting from this step could accelerate the population shift, implying that induced fit can extend and optimize conformational selection. For the binding process to proceed in the classical induced fit mechanism, the selective initial interactions must be strong enough to maintain the encounter complex for a relatively long time, which indicates that induced fit also contains the step of selecting the appropriate initial conformation or, alternatively, the ‘conformational selection’ plays a role in induced fit.

“For a ligand to bind to a given flexible protein, there has been much debate as to whether the conformational selection or the induced fit is the governing mechanism....

“The above results point to the conclusion that both the ligand concentration and the timescale of protein dynamics play a role in shifting the binding mechanism between the conformational selection and induced fit.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. pp. 10-11; reference: Nussinov, R., B. Ma & C.J. Tsai. 2014. “Multiple conformational selection and induced fit events take place in allosteric propagation.” Biophys. Chem. 186:22-30.


“Since all three distinct conceptual models have been observed experimentally, it is important to keep in mind that all three mechaniisms may exist both in a simultaneous or in a sequential manner, covering a broad spectrum of binding events.” Du, Xing, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji & Shu-Qun Liu. 2016. “Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.” International Journal of Molecular Sciences. 17:144. 10.3390/ijms17020144. p. 11.


“We found that uniform neurite growth [for a C. elegans worm] maintains brain geometry. The shape and relative position of every neurite in the brain was largely established by birth. From birth to adulthood, the total length of neurites increased 5-fold, similar to the 5-fold increase in body length....

“The total number of chemical synapses increased 6-fold (~1300 at birth to ~8000 in adults). Except for the first larval stage (L1), synapse number increased in proportion to neurite length, maintaining synapse density across development.... In the adult, ~90% of neurons are left-right symmetric pairs in position, morphology, and connectivity. Some of these neurons exhibited left-right asymmetry in connectivity at birth.” Witvliet, Daniel, Ben Mulcahy, James K. Mitchell, Yaron Meirovitch, Daniel R. Berger, Yuelong Wu, Yufant Liu et al. 2021. “Connectomes across development reveal principles of brain maturation.” Nature. 596(7871):257-261. 10.1038/s41586-021-03778-8. [page numbering from author copy] p. 257.


“From birth to adulthood, we found that non-uniform synapse addition reshapes the connectome. New synapses create new connections and strengthen existing connections. We define a connection as a pair of cells connected by one or more chemical synapses. At birth, the brain’s 204 cells were interconnected by ~1300 synapses among ~800 connections. Over maturation, ~4500 new synapses strengthened most connections that were present at birth. The mean synapse number per connection increased from 1.7 at birth to 6.9 at adulthood. Approximately 1200 new synapses formed new connections between previously non-connected cells, resulting in a 2.4-fold increase from the number of connections at birth.” Witvliet, Daniel, Ben Mulcahy, James K. Mitchell, Yaron Meirovitch, Daniel R. Berger, Yuelong Wu, Yufant Liu et al. 2021. “Connectomes across development reveal principles of brain maturation.” Nature. 596(7871):257-261. 10.1038/s41586-021-03778-8. [page numbering from author copy] p. 257.


“Synapse addition did not occur uniformly across the brain. Preferential synapse addition occurred in multiple contexts. First, new connections were more likely to form between neurons that shared larger physical contact areas at birth....

“Second, synapse addition preferentially strengthened inputs to ‘hub’ neurons, neurons with already more connections at birth. Hub neurons disproportionately strengthened existing input connections over time and disproportionately established more new input connections. However, hub neurons did not disproportionately increase their outputs. Thus, maturation progressively focuses the flow of information onto the most highly-connected neurons at birth.

“Third, synapse addition selectively strengthened a cell’s individual connections.... Thus, each cell appears to regulate the strengthening of its outputs but not its inputs.

“Unlike mammals where synapse pruning is a hallmark of early development, we did not observe systematic synapse elimination. In the C. elegans brain, synaptic connections are rarely removed; instead, a diminished connection is mediated by selectively strengthening other connections.” Witvliet, Daniel, Ben Mulcahy, James K. Mitchell, Yaron Meirovitch, Daniel R. Berger, Yuelong Wu, Yufant Liu et al. 2021. “Connectomes across development reveal principles of brain maturation.” Nature. 596(7871):257-261. 10.1038/s41586-021-03778-8. [page numbering from author copy] p. 257.


“Thus, one global pattern of brain maturation augments signal flow from sensation to action, making the brain more reflexive with age.” Witvliet, Daniel, Ben Mulcahy, James K. Mitchell, Yaron Meirovitch, Daniel R. Berger, Yuelong Wu, Yufant Liu et al. 2021. “Connectomes across development reveal principles of brain maturation.” Nature. 596(7871):257-261. 10.1038/s41586-021-03778-8. [page numbering from author copy] p. 257.


“One reason self-similarity that has become a topic of interest for cognitive scientists is the fact that such patterns seem to be associated with a variety of cognitive phenomena. Self-similarity is exhibited by, for example, the spatial organization of physiology associated with cognition and, especially, the temporal activity of such physiology, as well as cognitive behavior itself. Another reason for the interest in self-similarity is that such structures are associated with healthy systems. ‘Healthy’ has various meanings here, such as efficient and maximum information processing, organismic homeostasis, and system adaptability and integrity. Self-similarity is a common feature of both natural and artificial systems (e.g., cardiovascular system, central nervous system, protein-protein interaction networks, social networks, and World Wide Web). Additionally, fractals are widely implicated as a diagnostic tool, where reductions in the fractal structure are associated with diseases–for example, deterioration of the spatial fractal structure of white matter is associated with neurodegenerative disease and temporal fractal structure of heartbeats as an indicator of heart disease. Due to this fact, assessing the degree of self-similarity that a phenomenon exhibits–spatial or temporal–can serve part of the case that the phenomenon is a single, well-functioning system. Accordingly, a collection of elements can be understood as a single ‘system’ if it exhibits self-similarity (e.g., fractals, power laws, scale invariance, etc.) via its organization and/or dynamics, which indicates that it is maximizing information processing, and maintains homeostasis while being balanced between adaptability and stability.” Favela, Luis H., Mary Jean Amon, Lorena Lobo & Anthony Chemero. 2021. “Empirical Evidence for Extended Cognitive Systems.” Cognitive Science. 45:e13060. 10.1111/cogs.13060. p. 4.


“Addressing our overarching research question–‘Are person-plus-tool-systems extended cognitive systems?’–required us to address the following related questions: First, are self-similar dynamics exhibited by movements during affordance judgments of aperture pass-through-ability? Second, if self-similar dynamics occur during the task, how do they compare across the three modalities (i.e., judgments made with vision, rod, or Enactive Torch)? Third, if self-similar dynamics do occur to a similar degree across the three modalities, two of which involve nonbiological tools outside the body periphery, does that mean participants become person-plus-tool systems in order to perform the task? We hypothesized that participants’ arm movements while wielding haptic tools during the affordance-judgment task would exhibit self-similar dynamics.” Favela, Luis H., Mary Jean Amon, Lorena Lobo & Anthony Chemero. 2021. “Empirical Evidence for Extended Cognitive Systems.” Cognitive Science. 45:e13060. 10.1111/cogs.13060. p. 5.


“The current work lends support to extended cognition by demonstrating that the dynamics involved in affordance judgments can extend through the body and tools as perceptual judgments are made regarding action capabilities in an environment. Self-similar dynamics indicative of a healthy and adaptive single system are robustly present when using SSDs [sensory-substitution devices] such as the rod and Enactive Torch, across both tools and regardless of aperture width, trial order, participant characteristics, or participant judgments.” Favela, Luis H., Mary Jean Amon, Lorena Lobo & Anthony Chemero. 2021. “Empirical Evidence for Extended Cognitive Systems.” Cognitive Science. 45:e13060. 10.1111/cogs.13060. p. 20.


“The immune system is a cellular network capable of distinguishing between self, non-self, missing-self, and aberrant-self, including misplaced cells and aberrant intracellular and extracellular molecules. Functions of the immune system include detection, recognition, and elimination of pathogens, foreign substances, cancer cells, or damaged cells. It also plays a key role in inflammation, tissue repair, tissue remodeling, and regulation of immune response magnitude. A properly functioning immune system maintains a balance between responding to harmful and tolerating harmless agents or, in some cases, even tolerating harmful agents. In addition to classical immune functions, the immune system also regulates the nervous system, behavior, metabolism, thermogenesis, and participates in the fight-or-flight response.

“The immune network encompasses dozens of distinct immune cell subsets, which communicate with each other and other cells through various means, including cytokines, chemokines, various receptors, cell-to-cell interactions, exosomes and macrovesicles, the complement system, hormones, and neuronal signaling. Along with the ability to interact at a distance by utilizing different molecules, immune cells are motile and can enter and exit the vascular system. As a result, they can migrate across various tissues and organs, which facilitates the coordination of immune processes and immune functions throughout the entire body. A key aspect of this system is the integration of diverse physiological information across distance in the organism, toward an adaptive response in a variety of changing conditions....

“The human immune system is thus composed of a complex network of numerous specialized cells distributed across the body.” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 5.


“It is also important to note that the innate immune response is not solely the property of specialized immune cells. Epithelial cells, endothelial cells, and fibroblasts also express various types of pattern-recognition receptors that detect pathogen-associated molecular patterns and damage-associated molecular patterns (originating from the host’s own stressed, injured, or dying cells).... Therefore, non-immune cells participate in the early stages of the immune response by secreting antimicrobial peptides, pro-inflammatory cytokines, and chemokines, that alert, recruit and activate immune cells, thereby initiating the cascade of immune response.” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 6.


“An essential feature of the immune system is its ability to acquire memory–a key property of cognitive systems. This process occurs in both the innate and adaptive immune systems, leading to a more robust and rapid response upon re-exposure to a stimulus.” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 6.


“In addition to immune memory to antigen, adaptive immune cells undergo ‘training’ during their development and maturation–a process called ‘selection.’ During selection, cells that can recognize various antigens without exhibiting self-reactivity are chosen for survival and continue to mature, while those that do not meet these criteria are eliminated through apoptosis.... The innate immune system also has examples of ‘training’ for functional competence and self-tolerance. For instance, natural killer (NK) cells, a population of innate immune cells, undergo a process called ‘NK licensing’ or ‘NK cell education’ during their development. During the licensing process, immature NK cells are tuned for responsiveness, resulting in the generation of licensed or unlicensed NK cells. Licensed NK cells are more functionally competent and responsive compared to unlicensed NK cells; however, both subpopulations are important parts of the immune system.

“The described characteristics of the immune network are aligned with cognitive processes such as perception, attention, decision-making, communication, problem-solving, learning, and memory.” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 6.


“Moreover, not only individual cells, but also specialized organs and networks may equally have a limited ‘short-sighted’ view of the world of the organism it composes and ensure self-preservation. Hence one may speculate that biological self-organization in the human body emerges as a ‘crowd wisdom’ not only at the inter-cellular level subserving a given network (neural, immune, endocrinologic, etc.). Importantly, it emerges also at the inter-networks level....” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 7.


“The radical approach here is that standard mental categories such as perception, memory, perception and emotion may be ill-suited to investigating not only unconventional examples, such as slime mold memory, but also even the brain basis of behavior. This shift in focus invites neuroscience to consider the coupling between large-scale circuits and complex naturalistic behaviors by taking into account how the temporal evolution of behavior is linked to dynamic brain changes.

“Our proposal, although compatible with this approach, takes a step further and questions the very distinction between i) cognitive processes, supported by neural cells in the brain; ii) and bodily processes, supported by non-neural cells in the body. Rather, we suggest, all cells process information, make decisions, interact with each other, and as such, actively contribute to the survival of the biological organism as a whole.

“This view echoes the enactive approach outlining that the interaction process itself constitutes an irreducible domain of dynamics which can be constitutive of individual agency and social cognition.” Ciaunica, Anna, Evgeniya V. Shmeleva & Michael Levin. 2023. “The brain is not mental! coupling neuronal and immune cellular processing in human organisms.” Frontiers in Integrative Neuroscience. 10.3389/fnint.2023.1057622. p. 9.


“In addition to changes in spatial orientation and color, plants also alter their relationship with the environment by architectural changes. Plants, for example, decrease their exposure to harmful conditions by reducing their interface with negative environmental factors; a case in point is ‘self-pruning’ wherein the collective protoplasm of an overly shaded plant limb is deconstructed and its molecular components partially salvaged for reallocation elsewhere in the plant. Therefore, the collective biomass of a plant, composed solely of living cells, is constantly reorienting itself in space, contracting from negative environmental cues, and extending toward positive rewards. In plants, growth that is unrelated to the unfolding body plan, is behavior, and environmentally altered morphogenesis (i.e., phenotypic plasticity) falls under the purview of behavior.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 3.


“CnD [continuous, nondestructive] measurements reveal a profound botanical truth not discussed in today’s textbooks: plants are literally pulsating. Ultradian rhythms occur in growth, membrane potential, cytosolic calcium, Ca2+ and H+ fluxes, nutrient uptake, water uptake, transpiration, auxin transport, respiration, photosynthesis, isoprene emission and nitric oxide levels....

“Ultradian rhythms deserve to be a major research focus in plant biology. Information can be communicated not just by amplitude but by frequency as well.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 6.


“Frequency-dependent signaling and communication occur in diverse plant processes.

The flowers of Oenothera drummondi, for example, sweeten their nectar within minutes when stimulated by the sound frequency of a flying bee pollinator but not by the sound frequency emitted by a non-pollinating fly.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 7.


“Excitation causes profound changes in a multitude of plant physiological processes, including respiration, photosynthesis, transpiration, gas exchange, growth, turgor, phloem unloading, water absorption, and systemic plant defenses. In addition to their rapidity, the magnitude of the physiological changes evoked by an AP [action potential] can be astounding. In the thalli of the liverwort Conocephalum conicum, for example, the induction of an AP causes a 30- to 70-fold increase in the respiration rate within 6 seconds. Thus, plant APs are energetically very expensive. As such, it is possible that some higher mental functions in plants may not be constitutively expressed. Plants may only be fully ‘intelligent’ or ‘sensitized’ when conditions require that they be. It is also possible that the sensory and/or integrative capabilities of domesticated crops may be reduced compared to their wild relatives, mirroring the decrease in intelligence that accompanied animal domestication.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 8.


“Convergence also underlies plant and animal excitability. For example, the APs of plants and animals both fulfill the three sine qua non that define APs: 1) they are all-or-none, 2) they self-propagate away from the site of stimulation, and 3) in their wake, there occurs a refractory period. The ion channels involved in the depolarizing phase of APs in animal neurons, however, are largely different from those that serve the same function in plant cells. Plant and animal APs evolved convergently....

“Thus the gap junctions of animal cells and the plasmodesmata of plant cells arose convergently; the APs of plants and animals arose convergently; the habituation of plant and animal cells arose convergently; and virtually everything relating to multicellularity in plants and animals arose convergently.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 11.


“Recently, however, there has been a ground-shifting discovery relating to the South American vine Boquila trifoliolata, a woody vine that rambles through the canopy of temperate rainforests in southern Chile. What makes Boquila so fascinating is its astounding feats of leaf mimicry. B. trifoliolata leaves mimic the shapes, colors, leaf orientations, petiole lengths, and vein conspicuousness of host plants over which they grow. An individual Boquila vine that over the course of its rambling growth traversed the canopies of three hosts, mimicked each of them in their respective areas of proximity. This finding led the original discoverers to speculate that volatile signals or horizontal gene transfer may underlie the mechanism by which the mimicry is achieved. These two hypotheses, however, were dashed by the discovery that B. trifoliolata leaves also mimic the ‘leaves’ of artificial plastic plants, albeit not as well as they do the living forms of plants. Since neither volatile signals nor horizontal gene transfer are involved in B. trifoliata’s remarkable feats of mimicry, it is necessary to reconsider much more seriously Haberlandt’s radical idea, resurrected by Baluska and Mancuso, that certain leaf cells act like ocelli to produce vision in plants.” Minorsky, Peter V. 2024. “The ‘plant neurobiology’ revolution.” Plant Signaling & Behavior. 19(1):e2345413. 10.1080/15592324.2024.2345413. p. 11; reference: Baluska, F. & S. Mancuso. 2016. “Vision in plants via plant-specific ocelli?” Trends Plant sci. 21(9):727-730. 10.1016/j.tplants.2016.07.008.


“For our purposes, the crucial upshot of this ‘driving’ view of causation in the brain [classical view of stimulus “driving” response], within the context of neuroscience’s search of neural mechanisms, is that it paints a picture of the causes of behaviour that is inherently reductive in three key ways. First, it suggests a vertically reductive perspective in which, while one might conveniently and even effectively describe the processes of behavioural control in terms of mental states, like beliefs or desires, or cognitive operations or decisions, these are not seen as the right level for a truly causal explanation. Instead, it is the so-called neural ‘vehicles’ of these states (i.e. the activity of neural mechanisms) that are taken to be doing the ‘real’ causal work in driving the downstream behavioural effect. From this perspective, mental and cognitive stages are explained away as mere epiphenomena....

“Second, this approach entails a horizontally reductive perspective in that it assumes that we can decompose the nervous system into various neural parts and isolate the explanatorily relevant causes of any specific behaviour to the activity of just some of those parts, allowing us to effectively ignore its wider neural context. From this perspective, the organism itself–as a causal agent–recedes from view or even disappears entirely from causal explanations of its own behaviour....

“Lastly, and less obviously, such approaches also imply a view of behaviour that is temporally reductive. Viewing an organism’s behaviour as being driven into action primarily by the activation of a specific neural mechanism strongly implies that all one needs to know about the causes of a given behaviour is the currently active patterns of neural activity. From this perspective, behaviour is depicted as the outcome of an entirely Markovian neural process. Neither the historical context that shaped these neural processes, nor the organism as a diachronic entity with extension and continuity in time, are considered relevant to the causal explanation of its behaviour....

“Crucially, such a view ignores the fact that the patterns of neural activity mean something to the organism and that the causality in the system depends on that meaning.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] pp. 3-4.


“A common folk conception of causation simply equates causes with physical forces. On this view, a cause is an event that produces an outcome through a transfer of energy–what List and Menzies call some causal ‘oomph’, as in one billiard ball hitting another. This is known in the philosophical literature as a ‘producing’ notion of causation....

“An alternative conception of causation, popular in the philosophical literature is a broader notion known as ‘difference-making’ or ‘dependence’ causation. Under this view, causes are thought of as counter-factual difference-makers–that is, a cause is taken to be any variable that could have changed how some event unfolded, had it been different to how it actually was. This captures the intuition that when we think of A as a cause of B happening, we usually mean that if A had not been the case, B would not have occurred.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 4; reference: List, C. & P. Menzies. 2017. ‘My Brain Made me Do It: The Exclusion Argument Against Free Will, and What’s Wrong With It.” In: Beebee, Hitchcock & Price (ed?) Making a Difference: Essays on the Philosophy of Causation. Oxford UP.


“... how a neuron responds to incoming activity depends, in large part, on the configuration of its synaptic connections and on other biophysical parameters of the cell (like its current membrane potential). That is, the weights and nature of the synapses between neuron A and neuron B, taken within the context of all of B’s other presynaptic inputs, and of the electrophysiological properties of B as a whole, collectively embody what Tse has termed the neuron’s ‘criteria’ for firing–the conditions that must be met for a neuron to ‘release its effect’.

“These criteria specify the types of presynaptic input the neuron would need to receive in order to produce an action potential (and, by extension, the types of input for which the neuron will remain inactive). These can include, for example, a threshold for firing based on number of action potentials arriving over a certain time window. More commonly, however, they specify complex spatiotemporal patterns of input to which the neuron is causally sensitive. For example, a neuron, due to its configuration of excitatory and inhibitory synapses, may require a particular spatial pattern of inputs for it to ‘release its effect’, such as those instantiating a logical AND/OR gate. Another neuron might be sensitive to particular temporal pattern, such as a certain rate or timing of inputs.

“A neuron’s criteria for firing are therefore a type of dependence cause: By changing the criteria (e.g. by changing the weights of its incoming synapses), one can exert control over whether the neuron will fire or not, given the same set of presynaptic inputs. Tse labels this type of causation ‘criterial causation’.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] pp. 4-5; reference: Tse, P.U. 2013. The Neural Baqsis of Free Will: Criterial Causation. MIT Press.


“... the ability to change a neuron’s criteria through synaptic reconfiguration, sometimes in real time, is ultimately at the heart of how the brain generates behaviour.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 5.


“And to, instead [of the driving view of causation], incorporate the notion of criterial causation into our conceptual toolkit, wherein, due to their sensitivity to types of input, downstream neurons ought to be viewed as, in an important sense, interpreting the signals they receive. That is, it forces us to consider how and why a neuron came to be configured such that it responds to its inputs in the way that it does.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 5.


“Aristotle’s formal cause ... is generally taken as referring to the essence or set of properties that makes an object or system that kind of thing and no other; that is, the characteristic way in which the material is organized (its form). For our purposes, the important parallel would be with the configuration of the nervous system....

“Lastly, Aristotle’s notion of a final cause asks the question: Why did something happen? For what purpose? It thus allows that having a purpose can, in its own right, be a cause of something happening. The concepts of formal and final causes are thus essentially diachronic–they reflect the way the system has come to be configured by past events, and the future-directed functionalities that the system enables.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 6.


“... we suggest, that a neuron’s ‘criteria’ for firing gets set: The configuration of the system embodies a set of constraints that structure the flow of energy into a postsynaptic neuron in such a way that sets conditions on the types or patterns of presynaptic actions potentials to which the postsynaptic neuron will be causally sensitive.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 7.


“We therefore argue that the organisation of the system and the dynamical constraint regime it embodies are a key part of the causal story of any given behaviour, and are therefore in need of explanation if we are to fully understand how behaviour is being generated.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 8.


“In his account of mental causation, Dretske introduces a helpful distinction between triggering causes and structuring causes of behaviour. A triggering cause is an event, stimulus or condition that initiates the process that ultimately leads to the performance of, for example, a mouse’s feeding behaviour. A triggering cause could therefore be the onset of a food stimulus. Similarly, the triggering cause of a car engine starting could be the turning of a key in the ignition.

“A structuring cause, on the other hand, is an event that helps to create or shape the process itself; that is, the process that gets initiated by the triggering cause and that leads to the execution of the behaviour in question. A structuring cause could therefore be the wiring of a car or the event(s) that help to shape the neurophysiology of the mouse.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 8; Dretske, F. 1988. Explaining Behavior: Reasons in a World of Causes. MIT Press.


“Talk of final causes and organismal purposiveness can appear somewhat vague and perhaps even magical. However, we contend that Dretske’s work on structuring causes helps to operationalise it in concrete terms. In particular, Dretske emphasises the role of learning and experience in shaping the neurophysiology of an organism. In the language of constraints, this means that the personal history of the organism causes changes to the global constraint regime, thereby acting as a structuring cause of its subsequent behaviours in a way that is entirely natural and non-mysterious. Likewise, the idea of a final cause, within this framework, does not need to entail some kind of retrocausality, with a future state reaching back in time to influence current behaviour; it is simply the current possession of a goal state (towards a desired future end) that has causal power in the system.

“What this means is that one of the main causes of an organism’s behaviour is quite literally its own historical interactions with the world and its past experiences.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] pp. 8-9.


“As Michael Silberstein puts it: ‘For any particular synchronic-frame or still-shot of a biological system at a time t with some duration d, the determining features include diachronic multiscale interactions (context sensitivity) and global constraints outside the time-slice in question’.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 9; reference: Silberstein, M. 2021. “Constraints on Localization and Decomposition as Explanatory Strategies in the Biological Sciences 2.0.” In: Neural Mechanisms: New Challenges in the Philosophy of Neuroscience. Calzavarini, F. & M. Viola. pp. 363-393. Springer Nature.


“... individual neurons or populations of neurons are tuned to respond to macroscopic patterns (i.e. spatiotemporally extended types) of incoming activity, rather than the specific details. This is true, for example, for neurons that respond to the rate of inputs over some time window, but which do not distinguish temporal patterns within such windows. And it is true for populations of neurons that are selectively responsive to low-dimensional (macroscopic) patterns in their inputs, rather than the high-dimensional (microscopic) details of each individual presynaptic neuron’s firing.... This also aligns with the important observation that the lack of firing of given neurons can be just as causally effective in the system as the firing of neurons.

“This kind of sensitivity to macroscopic patterns is observed empirically and is consistent with theoretical work demonstrating the efficacy of macroscopic causation. In this view, what happens in the system is sensitive to the macrostates that subsystems within it occupy (and how they are interpreted by other subsystems), rather than the details of the microstates by which they are transiently realised. This is broadly akin to the way in which, in language, we are generally sensitive to the word that is being uttered, rather than to the specific acoustic and prosodic features of how it was uttered on that specific occasion.

“Of course, any given macrostate must always be instantiated by some specific microstate at a given moment and one could argue that is where the real causation lies–at the lowest level of physical detail. However, two considerations argue against this interpretation.

“First, due to the inherent noisiness of neuronal signalling and molecular and cellular processes in general, the microscale details of the system at any moment will not fully determine (in the sense of causally necessitating) what happens next. This does not mean that the outcome will necessarily be settled by some particular random jigglings of jitterings at the molecular scale, however. What it does, in the words of physicist George Ellis, is introduce some causal slack into the system. This means... that the organisation of the system can come to embody some higher-order constraints that really do have causal efficacy over how the system evolves.

“Second, and as a result of this causal slack, these systems also come to be sensitive to higher-order patterns, or macrostates that are multiply realisable–that is, where any given macrostate may be realised by many different microstates that are causally equivalent within the system. If one understands causation in a counterfactual sense, then what this means is that the causal sensitivity of the system, in these cases, lies at the level of these coarse-grained patterns, rather than the details of their neuronal instantiations. That is, many changes to the microstate will not affect the outcome, unless they also change the coarse-grained macrostate in a way that the downstream neurons are sensitive to....

“In this sense, then, the system is causally sensitive to patterns or types of activity, constituted by equivalence classes of microscale details, which are established by the criterial configuration of downstream neuron(s). In other words, it is the configuration of the neurons interpreting the signals which generates the equivalence classes, by virtue of their sensitivity to patterns and insensitivity to details – effectively, a filtering or categorisation of their inputs. This is a form of, what we would call informational causation: The system becomes causally sensitive to information that is, to a large extent, created by the downstream neuron(s), not simply received or transmitted to them. The meaningful information in the system (i.e. what counts as ‘signal’/pattern) is not inherent in the presynaptic inputs themselves; it inheres in the active and selective interpretation of those inputs.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] pp. 9-10.


“We have already seen how the informational economy (embodied in the physical, dynamical configurations of the system, the constraint regime it enacts, and the neuronal ‘criteria’ this creates) is shaped by the system’s historicity, such that it comes to reflect or instantiate the subjective perspective of the organism itself....

“More precisely, they [patterns of neural activity that can be meaningful for an organism] represent an inference or belief about the existence of some objects out in the world that are the causes or sources of the incoming sensory data. An internal pattern can usefully represent such an object by virtue of ‘standing in exploitable relation to it’. That is, having such an internal representation allows the organism to take some action in relation to the object, which it could not do otherwise. This relates to the second criterion–that such internal representations be useful for something, where the usefulness depends on their ‘content’... Such internal representations are not just referential, they are also, potentially at least, consequential.” Potter, Henry D. & Kevin J. Mitchell. 2025. “Beyond Mechanism–Extending Our Concepts of Causation in Neuroscience.” European Journal of Neuroscience. 61:e70064. 10.1111/ejn.70064. [7] p. 10; subquote: Shea, N. 2018. Representation in Cognitive Science. Oxford UP.


“... self-modeling networks (also called reified networks) can be used to model multi-order adaptive biological, mental and social processes in a convenient manner. Such networks use nodes for specific network states (called self-model states) to represent some of their own network characteristics, thus enabling them to change over time.” Treur, Jan. 2024. “On Structure, Dynamics, and Adaptivity for Biological and Mental Processes: a Higher-Order Adaptive Dynamical System Modeling Perspective.” In: Samuelson, S.L. Frank, M. Toneva, A. Mackey & E. Hazeltine (Eds.). pp. 4283-4291. Proceedings of the 46th Annual Conference of the Cognitive Science Society. [3] p. 4283.


“A similar self-modeling network model [regulation added to an existing bio-network] has been applied ... to model a case study of evolutionary processes....
‘Also of relevance here, one form of disgust, pathogen disgust, functions in part as a third-order adaptation, as disease-avoidance responses are up-regulated in a manner that compensates for the increases in vulnerability to pathogens that accompany pregnancy and preparation for implantation – changes that are themselves a second-order adaptation addressing the conflict between maternal immune defenses and the parasitic behavior of the half-foreign conceptus.’

“This quote considers three levels of adaptation for the first trimester of pregnancy. But also considering the occurrence of pathogens a form of adaptation for the wider ecological context, pathways for the following four adaptation orders can be distinguished; via its dynamics, each of these adaptations controls the pathway of the previous adaptation:

“First-order adaptation Pathogens occur, with pathways negatively modulate [sic] the existing pathways for good health.

“Second-order adaptation An internal defense system occurs, with pathway negatively modulating the pathogens pathway.

“Third-order adaptation For pregnancy, a pathway is added to downregulate the defense system’s pathway during the first trimester, thus protecting the half-foreign conceptus.

“Fourth-order adaptation Disgust during first-trimester pregnancy adds a pathway to make the downregulation of the immune system less strong via the behavioural immune system: by disgust potential pathogens in the external world are avoided so that less risks are taken.” Treur, Jan. 2024. “On Structure, Dynamics, and Adaptivity for Biological and Mental Processes: a Higher-Order Adaptive Dynamical System Modeling Perspective.” In: Samuelson, S.L. Frank, M. Toneva, A. Mackey & E. Hazeltine (Eds.). pp. 4283-4291. Proceedings of the 46th Annual Conference of the Cognitive Science Society. [3] p. 4287; subquote: Fessler, D.M.T., J.A. Clark & E.K. Clint. 2015. “Evolutionary psychology and evolutionary anthropology.” In: Buss, D.M. (ed). The Handbook of Evolutionary Psychology. pp. 1029-1046. Hoboken: Wiley.


“A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field....

“The term population doctrine describes the belief that the population, not the neuron, is the fundamental unit of computation.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3055.


“For a single-unit neurophysiologist, the canonical analysis is a neuron’s peristimulus time histogram (PSTH). For a population neurophysiologist, it is a neural population’s state space diagram. Instead of plotting the firing rate of one neuron against time, the state space diagram plots the activity of each neuron against one or more other neurons. At every moment in time, the population is at some neural state: it occupies some point in neuron-dimensional space or, identically, produces some vector of firing rates across recorded neurons. Time is a function that links neural states; it turns sequences of neural states (or sets of PSTHs) into trajectories through the state space.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3055.


“Recasting population activity as a neural state can suggest new hypotheses. As vectors in neuron-dimensional space, neural states both point in some direction and have some magnitude.... However, it may be more surprising that this second feature–neural state magnitude–also matters: it predicts how well objects will be remembered later.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3055.


“Representation is the process by which one instantiation of some phenomena is replicated in another form. An apple can be represented in a painting or in a pattern of activity across a group of neurons. It seems almost trivial to use the word representation to refer to the neural correlate of a stimulus, memoranda, cognitive process, or action, yet the word is contentious....

“Avoiding the term representation leaves us using convoluted language to describe patterns of neural activity that correspond to some event. It is not incorrect, in our view, to say that part of a neural state space represents a task demand or that a neural trajectory represents a sensorimotor transformation, provided we remember that representation is not necessarily the function of that pattern of activity.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3057.


“There is some evidence that multiple patterns of activity could implement the same function at different moments in time. This idea is known as multiple realizability in philosophy of mind. For example, in uncertain environments, decision-makers often pass through some period of exploration between longer periods of following some rule or policy. Exploration produces the kinds of sudden jumps in neural activity we introduced with the concept of distances, but it also disrupts long-term autocorrelations between neural states and promotes new learning. The pattern of activity that implements a policy after exploration is not the same as the pattern that existed before it, even when subjects are just returning to an old policy. The ability to implement the same policy via slightly different neural states could offer some benefits in nonstationary environments, but it also implies that the neural state spaces may have many sloppy dimensions–dimensions along which neural activity can vary without affecting cognition and behavior–and a smaller number of stiff dimensions in which comparatively small differences between neural states can have big implications for cognition and behavior.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] pp. 3057-8.


“Activity of neurons tends to be correlated, because the wiring between neurons constrains the patterns of neural activity that are possible, so neural states often only vary along a small number of dimensions in the neural subspace. To put it another way, there is a lot of white space in our state space diagrams: neural activity tends to occupy fewer neural states than it would if each neuron made an independent, random contribution to population activity. The part of the neural state space that contains the states that we observe is called the neural manifold.

“We have at least two notions of a manifold. The first–the one we referred to when we said that neural recordings are a low-dimensional projection of an entire manifold of neural activity–might be better called the Manifold: this is the space that encompasses all possible states, the states we would observe if we could record forever, from all neurons. However, another common usage refers to the space containing on-task neural states recorded from a small number of neurons during a finite period of time. This subtle distinction is why there is no guarantee that a manifold will be the same across tasks, states, or time....

“Because manifolds are spaces, they have geometric properties, including dimensionality – meaning the number of dimensions that are needed to describe them.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3058.


“Population neurophysiology has its own object of study, characteristic set of methods, and suite of key concepts that give us new ways to reason about how neurons behave collectively, rather than as individuals. We have introduced 5 of these concepts here: (1) the neural states that provide a snapshot of a pattern of activity across the population, (2) the manifold that encompasses the neural states that are possible (Manifold) or at least observed (manifold), (3) the coding dimensions and (4) the subspaces that link neural states to behavior and cognition, and (5) the dynamics that map activity from neural state to neural state, guiding how trajectories evolve through time and across the state space.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3063.


“Conceptually, we should acknowledge that the neural population doctrine has a weakness that is not shared with the single-neuron doctrine. The limits of a neuron are obvious–it has cell walls–but what are the limits of a population? Are its boundaries the set of recorded neurons? The tissue surrounding the electrodes? The edges of the Broadman area? The skull?... ... but for now, the term is ambiguous. It is not always immediately clear whether a paper shares our notion of population or when the term population is distinct from related terms, like neuronal ensembles.” Ebitz, R. Becket & Benjamin Y. Hayden. 2021. “The population doctrine in cognitive neuroscience.” Neuron. October 6. 10.1016/j.neuron.2021.07.011. [3] p. 3063.


“There are two views on the relationship between cognition and the brain that are largely implicit in the literature. The Sherringtonian view seeks to explain cognition as the result of operations on signals performed at nodes in a network and passed between them that are implemented by specific neurons and their connections in circuits in the brain. The contrasting Hopfieldian view explains cognition as the result of transformations between or movement within representational spaces that are implemented by neural populations.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 1.


“Representation is a term widely used in neuroscience and refers to any informative, guiding neural signal....

“In contrast to this neuroscientific usage, here we argue that cognition requires a more elaborate and restrictive notion of representation. Representations have content – they are about something. They are evaluable, such as for truth, success, accuracy and the like. They are detachable, capable of existing in the absence of their typical causes. They can be combined and interact in various systematic ways. Finally, they are produced and used by the system in order to generate behaviour. To be clear, these constraints on representation imply that not every sensory or motor state is a representation. For example, if a particular sensorimotor state cannot be activated in the absence of its typical cause, then that state is not a representation....

“These properties should not be thought of as legislating the use of the term ‘representation’ but, rather, as a proposal about how to understand the computational role of the states posited by explanations of cognition in neuroscience. For example, the detachability of representations is a claim about the causal structure of the nervous system. In particular, an explanation that requires that representations are detachable must be organized such that the presence of the representation is not stimulus bound.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] pp. 1-2.


“In contrast to the Sherringtonian view, the Hopfieldian view emphasizes the distributed nature of computation for cognition in neural system just as Hopfield illustrated how distributed neural networks could perform computations. The approach couches its operations and representations in terms of transformations between neural spaces. Implementationally, massed activity of neurons is described by a neural space that has a low-dimensional representational manifold embedded within it. These neural spaces may be comprised of neural ensembles, brain regions or distributed representations across the brain. These representations and transformations are realized by the aggregate action of neurons or their subcomponents, but explanations of cognition do not need to include a biophysiological description of neurons or their detailed interconnections. Single neurons can play a role only as second-level explainers of cognitive phenomena, explanatory only by virtue of their contributions to neural spaces.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 5.


“Algorithmically, Hopfieldian computation consists of representational spaces as the basic entity and movement within these spaces or transformations from one space to another as the basic operations. The representations are basins of attraction in a state space implemented by neural entities (be they single neurons, neural populations or other neurophysiological entities) but agnostic to implementational details (although, as a matter of fact, most Hopfieldian computations are focused on neural populations).” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 5.


“The Hopfieldian view faces a number of its own apparent difficulties. Examples abound of information carried by single neurons, including sensation, navigation (grid cells in the entorhinal-hippocampal cortices) or learning (such as temporal difference prediction errors conveyed by dopamine cells).... Grid cells are neurons that tile the space within which the animal finds itself. These cells seem to have particular representational contents that are proposed to play a central role in explanations of spatial cognition and navigation, constituting a cognitive map of the environment. They are also proposed to play a role in internally directed cognitive search. Such single-neuron representations appear to be at odds with the focus on populations at the heart of the Hopfieldian approach.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] pp. 6-7.


“The divide [between Sherringtonian and Hopfieldian interpretations of neural functioning] then boils down to a clash between the neuron doctrine that maintains that single neurons are the basic explanatory unit for cognition and the population doctrine that maintains that the central explanatory role will be played by neural populations....

“In fact, both the Sherringtonian and Hopfieldian views acknowledge the importance of single neurons and neural populations.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 8.


“The most charitable interpretation of the Hopfieldian view presents a population + doctrine that populations of cells constructed from individual neurons are the explainers of cognitive phenomena. As a result, the population + doctrine, which is just the Hopfieldian view, can assimilate the activity of keystone cells as the predominant drivers of populations activity in particular contexts.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 9.


“Thus, the characterization of Sherringtonianism and Hopfieldianism as merely a contrast between the neuron and population doctrines is incorrect. Neither view denies that both neural populations and single cells are important and this importance can be reconciled with either theoretical commitment. Consequently, the mere involvement of neurons or populations fails to determine a dominant view, and we find the contrast between single neurons and neural populations to be facile.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 9.


“The Sherringtonian approach may be more effective for older, conserved, or modular structures such as the brainstem or spinal cord. Sherringtonian circuits are computationally dedicated modules that reflect the outcome of selective evolution. Neuron to neuron connections and canalized local circuitry would reflect one outcome of such selection. Newer, flexible or recent structures might require the Hopfieldian approach. Hopfield circuits are more flexible modules that can be used for a range of computations.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 9.


“The Hopfieldian view, by contrast, is more flexible both representationally and computationally – so much so that its computational descriptions subsume those of the Sherringtonian.” Barack, David L. & John W. Krakauer. 2021. “Two views on the cognitive brain.” Nature Reviews Neuroscience. 10.1038/s41583-021-00448-6. [4; page numbering unclear] p. 10.


“In this article I stress that inheritance is not a single discrete package of genes handed over at conception, but a time-distributed developmental process by which diverse developmental resources become available to the next generation. Many extra-genetic inheritance processes (a.k.a. ‘non-genetic inheritance’) are actually best thought of as vital tools for short-term, rapid-response adaptation.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 51.


“Extensive resources, over-and-above genes, are known to be passed from parents to offspring, including components of both egg and sperm, hormones, symbionts, epigenetic marks, small RNAs, antibodies, ecological resources and learned knowledge. Traditionally considered ‘proximate causes’ of development, it is now evident that some of these factors can lead to both short- and long-term inheritance of phenotypes, can be subject to natural selection, and can affect evolutionary dynamics and equilibria.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 52.


“While undoubtedly a simplification, in Lala et al. we suggest that epigenetic inheritance mechanisms fall into three broad categories: The first is DNA methylation, which refers to the addition of a methyl group to one of the DNA nucleotide bases (cytosine) and can block transcription factors from binding to a gene and thereby suppress its expression. The second is histone modification. DNA is usually wound around histones, so chemical modifications of these can affect how tightly the DNA is wound, and in this way up-regulate, down-regulate, or silence transcription. The third is non-coding RNAs, including both small and long non-coding RNAs, which regulate gene expression post-transcriptionally, often by binding to, and thereby silencing, RNA molecules.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 53; reference: Lala, K.N., T. Uller, N. Feiner, M.W. Feldman, S.F. Gilbert et al. 2024. Evolution evolving: The developmental origins of adaptation and biodiversity. Princeton UP.


“In some taxa (e.g., nematode worms, Nematoda) epigenetic inheritance can last over 20 generations but in most it generally peters out in less than 3-5 generations.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 53.


“Recent evidence suggests that pregnant holocaust victims and 9/11 survivors may have transmitted epigenetic marks associated with their trauma to their children. However, there is some evidence that inherited epigenetic effects may be adaptive, with such cases variously known as predictive adaptive responses, adaptive parental effects, or anticipatory parental effects. For instance, human mothers who experienced the Dutch Hunger Winter during WWII may have epigenetically reprogrammed their offspring to upregulate the storage of food as body fat through the methylation of key genes.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 53.


“Inter-generational microbial transmission may be a universal component of animal inheritance....

“Such studies imply the gut microbiome can be a crucial ‘fast-adaptive partner’, enabling microbially acquired adaptive responses to rapid changes in diet, and complementing the slower-acting genetic response to selection in the hosts.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 54.


“... heredity is more than a package of genes and cellular resources handed over at conception like the baton in a relay race: it is a continuous process of developmental reconstruction that spans the entire life cycle. All forms of inheritance collectively guide offspring development by contributing to the production of a phenotype predicted to match the expected environment, where that ‘prediction’ is based on transmitted genes and updates by inherited extra-genetic information.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 57.


“The data require diverse evolutionary scientists to face up to some challenges in how we think about some key concepts. These include natural selection, since genes are not the only form of heritable variation subject to natural selection, and there is now substantial evidence for adaptation occurring through the selection of epigenetic, symbiotic, and cultural variation. Likewise, if evolvability is the ability of a population to respond to selection, then adaptation that occurs through the selection of extragenetic variation needs to be considered. The data even affect how evolution is regarded. As we describe in Evolution Evolving, for a century evolutionary biology has been preoccupied with genes – the most slowly changing aspect of evolutionary adaptation and the most stable component of inheritance. Now it’s time to recognize extra-genetic inheritance as playing a vital and central role in rapid evolutionary adaptation. The selection of epigenetic, symbiotic and cultural variation is not analogous to biological evolution: it is biological evolution.” Lala, Kevin. 2025. “A developmentalist’s view of inheritance.” Acta Ethologica. 28:51-59. 10.1007/s10211-025-00464-0. [5] p. 57; reference: Lala, K.N., T. Uller, N. Feiner, M.W. Feldman, S.F. Gilbert et al. 2024. Evolution evolving: The developmental origins of adaptation and biodiversity. Princeton UP.


“Within developmental neuroscience, intrinsic aspects are generally taken to be those that reliably proceed without clear dependence on sensory input, whereas extrinsic aspects are more open to being shaped by external input relayed through sensory systems.... Although such intuitive typologies may be useful at some level, that utility is limited by two main issues: First, intrinsic aspects of development are sometimes seen as being exclusively under genetic control, which as outlined below, is a misnomer. Second, as suggested by the preceding discussion of brain function, experience cannot be defined simply as extrinsic input to the brain that originates independently of the activity of the organism.” Marshall, Peter J. 2024. “Towards a Biologically Coherent Account of the Brain and How it Develops.” Human Development. 68(5-6):209-220. 10.1159/000540024. [4, page numbering is from a manuscript copy and unsure] p. 211.


“In contrast [to the machine conceptualization of the organism], developmental systems theory (DST) eschews the notion of separable, linearly additive causal influences on development, instead emphasizing the notion of developmental resources over the causal primacy of any one influence on development.” Marshall, Peter J. 2024. “Towards a Biologically Coherent Account of the Brain and How it Develops.” Human Development. 68(5-6):209-220. 10.1159/000540024. [4, page numbering is from a manuscript copy and unsure] p. 214.


“Although authors generally agree on some of its [plasticity-led evolution’s or PLE’s] features–such as the ability to produce different phenotypes in response to different environmental stimuli–depending on the theoretical orientation of the scientists and their disciplinary affiliation, this becomes a property of the genotype, of the organism, or the developmental system....

“The main difference with traditional models–the so-called ‘mutation-led evolution’ (hereafter MLE)–is that in the latter the trigger is found in the genome (and this is usually a mutation) and the environment plays an exclusively selective role. In PLE, on the contrary, the evolutionary process is plasticity wrought by a change in the environment. The environment thus takes on a dual role by changing both developmental and selective mechanisms, insofar as environmental stimuli are essential sources of information for the construction of the phenotype, which, sometimes, can be equated with genes.” Garaffa, Luigi. 2025. “Plasticity-Led (Not First) evolution: A Matter of Causal Relevance.” Biological Theory. 10.1007/s13752-025-00494-8. [3, page numbering unsure] p. 2.


“When we talk about adaptive evolution, the significance of certain causal factors such as natural selection or genetic mutations is generally undisputed. However, there are other factors whose importance for evolutionary explanations is rather dubious and have given rise to heated debates. Phenotypic plasticity falls into this category. One of the approaches that has asserted the significance of phenotypic plasticity in adaptive evolution is the so-called ‘genes-as-followers perspective’ developed by West-Eberhard.... The scheme proposed by West-Eberhard is aligned in many respects with that of PLE proponents, but with a crucial distinction: West-Eberhard’s perspective emphasizes the role of developmental system dynamics in accommodating inputs from both genetic and environmental sources, and their impact on phenotypic variation. In contrast, PLE emphasizs the plastic response of the developmental system only to environmental stimuli.” Garaffa, Luigi. 2025. “Plasticity-Led (Not First) evolution: A Matter of Causal Relevance.” Biological Theory. 10.1007/s13752-025-00494-8. [3, page numbering unsure] p. 3.


“In hard determinism, there are no causes. The universe just inexorably unfolds according to the laws of physics.... In soft determinism, there are causes–some things could be different, depending on how that little bit of randomness plays out–but all the causes are located at the lowest levels. That lowest level is deemed to be the bedrock of reality.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 15.


“And yes, your actions are at any given moment constrained by all those prior causes [evolution, development, learning]. Yet you could just as well say, more positively, that they are informed by prior experience. That is precisely the property that sets life apart from other types of matter: living things literally incorporate their history into their own physical structure to inform future action. For those who would argue this impinges on the freedom of the self to decide at any moment, I counter that it is this very process that enables the self to exist at all. There is no self in a given moment: the self is defined by persistence over time.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 21.


“So, if you want to know what kind of thing you are, you are the kind of thing that can decide. Not just a collection of atoms pushed around by the laws of physics.... You are a new type of thing in the universe–a self, a causal agent.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 22.


“The universe doesn’t have purpose, but life does. Natural selection ensures it. Living organisms are adapted to their environment–retrospectively designed to function in specific ways that further their persistence. Before life emerged, nothing in the universe was for anything....

“And unlike the designed machines and gadgets that surround us in our daily lives, which also have a purpose or at least serve a purpose, living organisms are adapted for the sake of only one thing–their selves. This brings something new to the universe: a frame of reference, a subject.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 42, 43.


“Meaning is often treated in scientific discourse as something mysterious and difficult to quantify. But it rests on a commonplace and uncontroversial fact: some things are physically correlated with other things. Shannon called this ‘relative information,’ where knowing the physical arrangement of one thing tells you about the physical arrangement of something else.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 64.


“If we look at an organism and see that it tends to respond in a certain way to a given signal, we could say that the stimulus is the cause of that behavior. But this misses the larger point. The stimulus may be a trigger, but it is the particular configuration of the organism that causes that signal to cause that behavior. And that configuration is the outcome of eons of natural selection, which has pragmatically wired reasons for doing things into the structure of the living system. Evolution packs causal potential into life: like potential energy, this causal potential can be used to do work in the sense of directing the behavior of the organism.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 67.


“Exercising free will requires an open-ended ability for individuals to learn, to create new goals further and further removed from the ultimate imperatives of survival, to plan over longer timeframes, to simulate the outcomes of possible actions and internally evaluate them before acting, to decouple cognition from action, and ultimately to inspect their own reasons and subject them to metacognitive scrutiny.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 68-9.


“C. elegans, which have the ability to develop knowledge about their environment based on their own experience, highlight an increase in the degree of agency over what we encountered so far. We saw that simple unicellular creatures are biochemically configured to behave in ways that favor their own persistence and to respond in adaptive ways to their environment. Simple multicellular creatures show the same kind of adaptations, preconfigured and coordinated at scale by neural circuitry. These organisms have a repertoire of possible actions and choose between them for reasons.

“But it could be argued that they are natural selection’s reasons, not those of the individual organisms themselves. They come pre-wired, thanks to the life-or-death feedback of natural selection across preceding generations. What we see in C. elegans is a major step beyond that. Individual worms can learn from their own experience and develop their own reasons for choosing one action over another in any given situation. An individual worm is no longer just an instance of an evolutionary lineage–a preprogrammed drone rolling off the factory conveyor belt. It goes out into the world and develops its own agency, through the history of its own actions and its own experiences....

“Finally, the emergence of associative learning and long-lasting memory let individuals transcend their pre-wired instincts and be able to make decisions based on their own reasons. The next step was to give them more to reason about.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 93-4.


“The retina contains dozens of different types of RGCs [retinal ganglion cells], all specialized for parsing different kinds of visual information–high or low resolution, colors, movement, flicker, and so on. In the human retina there are about 1.2 million RGCs but 125 million rods and cones. Each RGC thus integrates information from around 100 photoreceptors.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 107.


“And there [parsing the meaning of incoming visual information] the organism faces a serious challenge. All it receives is a pattern of light of different intensities and wavelengths impinging across the array of photoreceptors in the retina. The job of all those levels of visual processing is to infer what is causing that pattern. But there isn’t one single answer to that question. Any given arrangement of things in the world causes a unique pattern. Yet the converse is not true: many different arrangements of things can cause the same pattern of light. An object could be small, or it could be far away. A visual line could be the continuous edge of one object or be two objects that happen to be aligned. Successive activations in nearby regions could arise from a single object moving or one object disappearing from view and another appearing.

“The organism thus has to solve this ‘inverse problem’ by making inferences about what is causing the detected pattern of light....

“The result is thus not a processed image like a photograph but really a set of beliefs. What is represented by the patterns of neural activity at any level of the hierarchy–that is, what is reported or made available to another part of the system–is not a line or a shape or a face at a particular position in the visual field but the belief that there is a line or a shape or a face at that position. One reason to think of them as beliefs, as opposed to propagated and processed signals that correlate with things in the outside world, is that they can be wrong.

“This is revealed strikingly in people who suffer from hallucinations. But it is also clearly shown by all manner of optical illusions, which have been studied by psychologists and cognitive scientists for well over a century to try and divine the operations that the visual system carries out.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 115-6.


“The imagined future thus also came to crucially inform decision making as much as the remembered past. The organisms that evolved these capabilities no longer just inhabited the here and now.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 122.


“The structure of the hippocampal circuitry is designed to link the assemblies active at one moment with those that are active in the next moment, and the next, and so on. This creates a temporally structured record of what happened during some episode: what is referred to as episodic memory.

“That kind of memory is perfectly structured for drawing inferences about causal relations. If, in a given episode, A happened and then B happened, then maybe A caused B.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 130.


“With the evolution of visual perception, we saw the need for the organism to take its own movements into account. This applies to the causal model of the world too: making sense of all these relations requires modeling the self as a causal agent.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 131.


“... several important points ... First, organisms do not passively wait for external stimuli to respond to. Their brains, when awake, are constantly cycling through possible actions, and this stream of behavior accommodates to new information and the changing environment. Second, this is not a one-way relationship from environment to organism: it is a recursive loop of mutual interaction.... If we ignore these reciprocal effects, we are left studying only half the overall system. Third, the processes of decision making and action selection are just that–processes: they have duration through time. They are not instantaneous transitions from one physical state of the system to the next....

“Finally, the description of the processes involved in action selection risks giving the impression of a mechanism churning away or of a computer running a linear algorithm.... And it is true that some of the operations of these mechanisms can be thought of as computations. However, the idea of an algorithm–a series of steps being completed methodically and sequentially–is not an accurate conception of what is happening. The various subsystems involved are in constant dialogue with each other, each attempting to satisfy its own constraints in the context of the dynamically changing information it receives from all the interconnected areas. Ultimately through these dynamic, distributed, and recursive interactions, the whole system settles into a new state–one that drives the release of one of the set of possible actions under consideration and the inhibition of all the others.

“In a holistic sense, the organism’s neural circuits are not deciding–the organism is deciding. It’s not a machine computing inputs to produce outputs. It’s an integrated self deciding what to do, based on its own reasons.... The process relies on physical mechanisms but it’s not correct to think it can be reduced to those mechanisms. What the system is doing should not be identified with how the system is doing it. Those mechanisms collectively comprise a self, and it’s the self that decides.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 143-4.


“... I introduced two flavors of determinism (‘hard’ and ‘soft’) that present overlapping but distinct challenges to the philosophical idea of free will in humans. To avoid conflating them, I will call them physical predeterminism (the idea that only one possible timeline exists) and causal determinism (the idea that every event is necessarily caused by preceding events–usually seen as the same thing as physical predeterminism but subtly distinct). And I will add a third flavor that we will need to tackle too: biological determinism (the idea that an organism’s apparent choices are really internally necessitated by its own physical configuration: its biochemical state or nervous system wiring).” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 145-6.


“Physicists Lee Smolin and Clelia Verde have proposed that the quantum-to-classical transition does not reflect spatial scale at all but rather the flow of time. In fact, they argue that what we experience as the present is simply the period in which the indefinite becomes definite. In this view, all systems have quantum properties in the future. That is, the properties of the individual particles are probabilistic–they are inherently undefined. It is only when the particles interact that those properties resolve into definite values. What we call ‘the present’ is that period of transition from a future that is indefinite, in which multiple possibilities exist, to a past that can no longer be changed. This process is not instantaneous: it takes time. The present, therefore, has some duration. Rather than eliminating quantum indeterminacy at classical levels, this process constantly introduces it through the random realization of possibilities. A parallel view, articulated b Nicolas Gisin and Flavio del Santo, claims that this future indefiniteness is not restricted to quantum systems but applies to physical parameters at larger scales as well. They argue that the apparent determinacy of Newtonian mechanics rests on a crucial assumption: that the numerical values of the relevant physical parameters are given with infinite precision, all at once. But that assumption of infinite precision comes up against a hard limit–the amount of information that can be physically encoded in any finite amount of physical space. Under strict determinism, the information about all the particles of the universe right now, at this moment, would somehow have to have been present at the moment of the Big Bang. And the same would be true for every other moment of time, requiring an impossibly infinite amount of information.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 159, 161.


“An alternative interpretation [of quantum mechanics] views this relationship temporally, rather than spatially, arguing that indeterminacy is a fundamental feature of both quantum and classical parameters in the future. This indeterminacy is resolved through interactions between fields, particles, or larger objects, with such interactions defining the period we experience as the present. The past is then fixed, while the future remains open, fuzzy, and undetermined.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 160.


“The idea is not that some decisions are determined (driven by necessity) and others are driven by chance. No, the really crucial point is that the introduction of chance undercuts necessity’s monopoly on causation. The low-level physical details and forces are not causally comprehensive: they are not sufficient to determine how a system will evolve from state to state. This opens the door for higher-level features to have some causal influence in determining which way the physical system will evolve. This influence is exerted by establishing contextual constraints: in other words, the way the system is organized can also do some causal work....

“The contemporary philosopher and mathematician George Ellis similarly argues that physical indeterminacy creates causal slack in physical systems, which opens the door for what is known as ‘top-down causation.’” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 163-4.


“Agents can act with causal power in the world because, biologically speaking, they have been paying attention. This is not a free lunch. Like potential energy, living systems act as stores or capacitors of potential causality. The difference is that the content to be stored is information; specifically, causally effective information.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 168.


“The other crucial function of nervous systems, of course, is to allow organisms to learn, to reconfigure their circuitry to reflect past experience and better anticipate future circumstances. If the neurons were already maxed out–if the connection weights were all set to 100 percent strength–then no learning would be possible. By contrast, neurons operating in a responsive range of probability of signal transmission can have that probability modified up or down. Indeed, organisms go to quite a bit of trouble to keep their neurons in the responsive range, renormalizing all synapses during sleep to ensure that a busy day of learning doesn’t overfix the system.

“From this point of view, we can see that the apparent unreliability of neural transmission at the level of (at least some) individual neurons is a feature in the system, not a bug.... Moreover, organisms have developed numerous mechanisms to directly harness the underlying randomness in neural activity. It can be drawn on to resolve an impasse in decision making, to increase exploratory behavior, or to allow novel ideas to be considered when planning the next action. These phenomena illustrate the reality of noisy processes in the nervous system and highlight a surprising but very important fact: organisms can sometimes choose to do something random.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 174-5.


“However, this term [coarse graining] is a little unfortunate, in that it seems to imply only a loss of information in the transformation from fine grained to coarse grained. This is not the case. The details may be lost, but a new type of information is gained in that process, even in transmission from one single neuron to another. The first neuron doesn’t ‘know’ what its firing rate is; it is either currently firing a spike or not. It takes another neuron to monitor the firing rate by integrating spikes over some period of time, creating a new kind of information in the process.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 198.


“All kinds of other things can end up being mapped in this way across brain regions. There are maps of broad families of chemicals in the olfactory system, maps of kinds of actions in the motor cortex, maps of short- and longer-term goals in the premotor and prefrontal cortex, and maps of navigational space and heading (which way an animal is facing or traveling) in the hippocampal system. And there are maps of concepts too–semantic categories that are represented in stereotyped positions and arrangements across individuals.

“For example, in higher areas of the visual system where object identity is extracted and represented, there is a systematic map of different kinds of objects that is remarkably consistent across individuals and even between humans and monkeys.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 206.


“Meaning drives the mechanism. Under causal reductionism, psychological states and cognitive operations can be reduced to being ‘nothing more than’ the activity of neural circuits, which may, in turn, be nothing more than the playing out of physical forces between molecules. The alternative view, which we could call cognitive realism, argues the converse–that neural patterns have causal power in the system solely by virtue of what they mean; that is, by virtue of their status as representing goals, beliefs, intentions, or other elements of cognition (whether conscious or not).” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 213.


“With these general criteria of autonomy and wholeness met, we can look at more detailed properties that together support claims of agent causation versus a reductive, mechanistic, and instantaneous view:

“First, decision making requires that multiple distributed subsystems act in parallel, communicating with each other over some period of time, through a complex web of interlocking, recursive circuits.... In this way, the entire system collectively settles into a new state....

“Second, organisms are endogenously active. They do not passively wait for stimuli to respond to. Even when they are physically still, they are not internally static. When signals come in from the sensory periphery, they are assimilated into the ongoing flux of biochemical and neural activity. For example, when neuroscientists perform brain scans on people using functional magnetic resonance imaging, the signals that are associated with the person doing some particular task are tiny: only about 1 to 2 percent the magnitude of the background bustle of neural activity....

“Third, as we saw in this chapter, the currency of the nervous system is meaning: that’s what causally drives the mechanism. This is not just information in an abstract mathematical sense but information about things, interpreted in the context of stored knowledge, with potential consequences for behavior. The organism is not mechanically driven by stimuli from outside; it is interpreting these signals in its capacity as a self. The organism is meeting the world halfway, as an active partner in a dance that lasts a lifetime.

“Fourth, causation in living systems is extended in time. We cannot build an explanation of what an organism does form an ahistoric description of its neural mechanisms. It is the way it is because of all the interactions that its ancestors had and that it has had with things in its environment. Through feedback from natural selection and through individual learning, organisms come to embody in their own physical structures knowledge about regular causal relations in the world....

“Finally, those reasons inhere at the level of the whole organism.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 216, 217, 218.


“Though it is not often described as such, I claim that agency–the capacity of organisms to act with causal power in the world, for their own reasons–is the defining feature of life itself. It is, moreover, the bedrock on which we can build an understanding of free will in humans.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 218.


“What people are usually after [to accept free will] is some means by which we can really be in charge of our decisions on a moment-by-moment basis and not merely driven by our biology and the history that has shaped it.

“I argue in the final chapters that evolution has provided exactly such a mechanism–or a suite of mechanisms–that grants us that capacity. We are not absolutely free, nor would we want to be–this is not a coherent notion at all, in fact. But we do have the capacity for reflective cognition, which means our subconscious psychology is not always opaque or cryptic to us. We have powers of introspection and imagination and metacognition that let us identify and think about our own beliefs and drives and motivations, examine our own character, and consciously adopt new goals or set new policies that guide our future behavior. We have, in short, the capacity of self-awareness.

“And we have the capacity of self-control. We have, in real time, the means to intentionally adjust our behavior by selecting the objects of our attention and the different options for action that we consider and prioritize.... But it does rely on a recursive hierarchy of neural systems that has been most highly elaborated in humans, giving us a special place in the natural world.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 219.


“These schemes [for clusters of human personality traits] vary in how many major dimensions they identify, with the most popular known as the ‘Big Five.’

“These five dimensions are called Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 230.


“So, yes, we really are all tuned a little differently. And the differences affect our behavioral tendencies in any given situation. But here’s the thing: we are never actually in ‘any given situation’–we’re always in some particular situation.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 235.


“We do not tend to notice or talk about our good habits: all the things we do that are simply useful, efficient automations of routine tasks that leave our cognitive resources free for handling new situations.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 235.


“One way that scientists define a habit is as a behavior that is simply cued by some kind of stimulus or environment or situation.... Studies of the neural basis of this process, in humans and other animals, reveal that it often involves a shift from what is known as model-based reasoning, where individuals use their model of the world to figure out what they should do, to model-free reasoning in which the model is not consulted or used to inform action and, instead, a habitual response occurs, like a reflex.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 236.


“... it could still be argued that the presence of any constraints, right now, regardless of where they came from, invalidates the idea of real free will. This idea hinges on an absolutist notion: we are only truly free if we are completely free from any prior constraints....

“Even if your conscious self were somehow able to choose what to want to do, free from any constraints arising from prior causes or sub-conscious influences, then on what basis would you decide? If you’re not constrained by your own character, or informed by your past experiences, or committed to any long-term goals or policies, then how are you deciding? On a whim? Or based on something?

“Presumably you’d like to be able to decide for some reasons, for your reasons to do whatever you feel like, but why would you feel like anything? Maybe you’d like to exercise some preferences about what you’d like your reasons to be. But then we’re back where we started. Conscious or not, reasons (or preferences) are constraints–that’s how they guide action, making some choices more likely than others. Totally unconstrained action would be totally uninformed action. It’s not clear it would even qualify as action, in fact. An action is something an agent–a self with aims–does. Otherwise it’s just a physical system behaving effectively at random. That doesn’t really sound like you deciding; it’s not obvious where you are in that picture a all.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 244.


“There is no self in a moment. The self is defined by continuity through time. You, right now, in the present, are just the momentary avatar–the representative in the world–of a self that stretches from the past to the future.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 245.


“Continuity is the defining property of life. In a unicellular organism, the whole imposes constraints on the parts: all the interlocking feedback interactions keep all the biochemical processes organized in a certain pattern. The organism is not a pattern of stuff; it is a pattern of interacting processes, and the self is that pattern persisting. The same is true at a higher level–not just physically but also psychologically. What else does it mean to be you other than to think like you and behave like you in some consistent manner through time?” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 245.


“The boundaries between categories can be sharpened by reinforcing one attractor state and inhibiting other ones representing similar concepts, which can be accomplished through the kind of neural dynamics we discussed in chapter nine....

“When faced with some problem, we have the ability to see the bigger picture by taking into account a wider context and a longer time horizon. This means we can avoid getting stuck in local optima–the quickest, easiest solution to a local problem–and instead optimize for global parameters.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. pp. 253, 254.


“If a population of neurons has a number of possible attractor states that it can stably be in, each of which means different things, then another population of neurons that monitors the state of the first set can draw an inference about the certainty attached to the signal. If population A is very strongly driven into a given state, then over some short period of time it will mostly be signaling that one thing. But if it’s being less strongly driven–if the incoming signals are more ambiguous–then it might oscillate between several possible states. The degree of any such vacillation can be measured by population B, if it can sample the activity of population A over some time period. Maybe it’s signaling ‘X’ 70 percent of the time and ‘Y’ 30 percent, or maybe it’s 90-10 or 50-50. Importantly, the first population can’t ‘know’ its own level of certainty at any moment–it requires a second population to monitor it and infer that paramenter.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 256.


“As we evolved into the ultra-social, ultra-cooperative, obligately cultural species we are, other minds became, by far, the most important things in our environments.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 266.


“I purposively did not start with a preconceived notion of what properties our will must have to qualify as ‘free,’ for this purpose or any other. Instead, I aimed to naturalize the underpinning concept of agency, with its core elements of purpose, meaning, and value, so as to arrive at an understanding of the properties, scope, and limitations of human decision making.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 278.


“Selfhood is defined by continuity through time–by maintaining a certain dynamic pattern of processes in the face of the thermodynamic pressure to take on any of the other, almost infinite sets of disordered arrangements those processes could adopt. Selfhood thus entails constraint. It is only constraint. The freedom to be you involves constraining the elements that make you up from becoming not you.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 279.


“The question of whether we have free will does not have a yes-or-no, all-or-none answer. Instead, we have degrees of freedom–an idea that is reasonably well captured, in my view, by a more commonsense understanding of the (still useful) notion of free will. That understanding entails, first, the ability to make choices–that we really can choose what to do. Our actions are not simply determined by outside forces because we’re causally set apart from the rest of the universe to at least some degree. And, just as importantly, we are not driven by our own parts. Rather, we, holisticaly–our selves–are in charge.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 279.


“A long-standing argument against holistic or top-down causation is that it constitutes a situation of circular causation–where the whole is supposed both to cause the organization of the parts and to be caused by that organization at the same time. Taking time into account reveals the true pattern of spiral causation, where interactions up and down the hierarchy are not occurring instantaneously but are spread over time.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 288.


“Living beings do not cause themselves in an instant, but they do cause themselves through time. That’s what being alive entails–continuing to cause yourself.” Mitchell, Kevin, J. 2023. Free Agents: How Evolution Gave Us Free Will. Princeton UP. p. 288.


“This paper wishes to introduce a formalization of processes, namely the reaction networks used in what has been called Chemical Organization Theory (COT). In reaction networks and COT, the relation between states and dynamics is turned upside down. The processes are primary, in the form of ‘reactions’, which are the most fundamental elements of a reaction system. States only appear in a second stage, as the changing concentrations of the ‘molecules’ that the reactions are processing into other molecules. The molecules therefore are not static objects, but merely raw materials that are constantly being produced, consumed, and recreated by the reactions. In that sense, reaction networks form perhaps the first formalization of a process ontology that is both fundamental and practical.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. [Updated from bootleg in 2023 Quotz] p. 2.


“The basis of a COT model is a reaction network. It consists of two types of entities, which we will call resources (‘molecules’, ‘molecular species’, or ‘species’ in the traditional COT formulation) and reactions. A resource is an abstract representation of a specific kind of substance, entity, or, most generally, measurable phenomenon. Examples of resources are particular types of chemical substances, elementary particles, biological species, economic goods, human agents, messages, words, ideas, or decisions. All the resources in the model are assumed to be available in some shared container or workspace, which in COT is called the ‘reaction vessel’. This joint presence allows any resource to interact directly or indirectly with any other resource. Reactions denote elementary processes that create or destroy resources. They typically produce combinations of new resources out of combinations of existing resources. Yet, the simplest reactions just create or destroy a single resource.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. [Updated from bootleg in 2023 Quotz] p. 3.


“Formally, we will define a reaction network as the 2-tuple <M, R), where M = {a, b, c, ...} is the set of resources, and R ⊆ P(M) x P(M) is the set of reactions, where P(M) denotes the power set (i.e. the set of all subsets) of M. Each reaction r ∈ R maps a particular subset X of M onto another subset Y of M:

r: X -> Y: {x1, x2,...∣xi ∈ M} →{y1 , y2,... ∣yj ∈ M}

“Note that the sets X and Y can be empty. We will call X the input set and Y the output set of r, and denote them respectively In(r) and Out(r). We will call the elements of In(r) the reactants of r, and the elements of Out(r) its products.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 3.


“The combined system <M, R> forms a network because the resources in M are linked to each other by the reactions in R that transform the ones into the others. But this is not a traditional network (i.e. a directed graph), in which a link connects a single element (node’, ‘vertex’) x to a single element y. A reaction connects a set X of elements to a set Y of elements. In mathematics, a network with this property is called a directed bipartite graph, or a directed hypergraph.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. [Updated from bootleg in 2023 Quotz] p. 3.


“... logical inference is a special type of reaction, namely one in which no ‘resources’ ever get consumed: inferences can only add true propositions to our knowledge, they cannot remove any. This is why logic is inherently static: nothing really changes by making logical inferences; at most we become aware of additional statements that were already true implicitly, but had not been proven yet.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. pp. 4-5.


“The most important new concept introduced by COT is an organization. This denotes a reaction system that is fundamentally self-sustaining: the resources it consumes are also the resources it produces, and vice-versa. This means that although the system is intrinsically dynamic or process-based, constantly creating or destroying its own components, the complete set of components (resources) remains invariant, because what disappears in one reaction is recreated by another one, while no qualitatively new components are added.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 6.


“Consider a subnetwork <M’, R> of a larger reaction network <M, R>, i.e. M’ ⊆ M. The formal definition of an organization is derived from three characteristics that such a reaction network <M’, R> can have:

• Closure: this means that nothing new is generated: the only resources produced by the reactions are those that were already in the starting set M’: ∀ r ∈ R such that In(r) ⊆ M’, the requirement holds that Out(r) ⊆ M’.
• Semi-self-maintenance: this is the complementary condition that nothing existing is removed; each resource consumed by some reaction is produced again by some other reaction working on the same starting set: ∀ x ∈ M’ for which ∃ r ∈ R such that x ∈ In(r) ⊆ M’, ∃ r’ ∈ R such that In(r’) ⊆ M’, and x ∈ Out(r’).
• Self-maintenance: this is a stronger form of the semi-self-maintenance condition, which states that each consumed resource x ∈ M’ is not only produced by some other reaction in <M’, R>, but that the amount produced is at least as large as the amount consumed.

“The determination of self-maintenance is more complex than the other two conditions, because it requires the introduction of a quantitative dynamics in the reaction network, which specifies the rate at which resources are consumed and produced by the different reactions.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 6.


“The requirement for self-maintenance is that this rate [rate of production for a resource] is non-negative for all resources, i.e. all resources either increase or are conserved. The reaction network fulfils this condition if there exists a flux vector (i.e. list of reaction rates) for which this requirement holds. Note that if the constraints determined by the (qualitative) reaction network allow such self-maintaining flux vectors to exist, then it seems likely that the (quantitative) system will converge to the corresponding regime of self-maintenance. The reason is that resources that are consumed more than they are produced (no self-maintenance) will decrease in concentration up to the point that the reactions consuming them slow down enough so that production (which is normally not affected by the concentration of the products, only by the concentration of the resources consumed) compensates for consumption.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 7.


“We are now able to define the crucial concept of organization: a subset of resources and reactions <M’, R> within a larger reaction network is an organization when it is closed and self maintaining. This basically means that while the reactions in R are processing the resources in M’, they leave the set M’ invariant: no new resources are added (closure) and no existing resources are removed (self-maintenance)....

“Being an organization may seem a rather uninteresting property: nothing really changes. Most theories, models and formalisms are based on invariant elements, so what is novel here? The essential contrast with classical modeling frameworks is that we started by assuming that everything changes: all resources are in constant flux, being consumed by some reactions, produced by others, but by default processed into something different. The concept of organization establishes that stability can arise even within such ceaseless flux of transformations.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 7.


“Note that some resources (such as bacteria in the last reaction) appear in both the input and output of a given reaction. That means that they are neither removed nor added by that reaction. Yet, they are necessary for the reaction to happen. In chemistry, such resources are called catalysts: they enable a reaction, but are not themselves affected by it. In our more general interpretation, we may call them agents: they act on the other resources in the reactions, processing them into something else.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 8.


“By adding a particular reaction, we may create a ‘source’ or a ‘sink’ for a particular resource, either injecting it into a system in which it was previously absent (thus interrupting closure), or removing it from the system faster than it can be produced (thus interrupting self-maintenance).” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 9.


“An arbitrary subset of a reaction network will in general not be an organization: its reactions working on its resources will produce additional resources (non-closure). These additional resources may react with some already present resources producing even further new resources. Thus, every addition may activate reactions that produce further additions. However, this process of growth of the resource base must come to an end when there are no further resources that can be produced by reactions working on the already present reactions. At that stage, all produced resources are already in the present set, and closure is reached. Thus, closure can be seen as an attractor of the dynamics defined by resource addition: it is the end point of the evolution, where further evolution stops.

“Let us now apply the same reasoning for self-maintenance, starting from the previously reached closed set. Some of the resources present in that set will be consumed by the reactions, but not produced, or at least not produced in sufficient amounts to replace the amounts consumed. These resources will therefore disappear from the closed set. Note that this does not affect the closure, because loss of resources cannot add new resources. Without these resources, some of the reactions producing other resources will no longer be able to run. Therefore, the resources they otherwise produce will no longer be replaced if they are consumed by some other reaction. If no other reactions continue producing these resources, they too will disappear from the resource set, possibly triggering the disappearance of even further resources that depend on them for their production. Thus resources disappear one-by-one from the set. However, this process too must come to an end, when the remaining resources do not depend for their production on resources that have been removed, but only on resources that are still being produced in sufficient amounts. Thus, self-maintenance too can be seen as an attractor of the dynamics defined by resource removal.

“The process of resource addition ending in closure followed by resource removal ending in self-maintenance produces an invariant set of resources and reactions. This unchanging reaction network is by definition an organization.

“The scenario for the spontaneous emergence of an organization illustrates the general principle of self-organization: any dynamic system will eventually end up in an attractor (originally called ‘equilibrium’ by Ashby), i.e. an invariant regime of activity defined as a subset of the system’s state space that the system can enter but not leave. In the present, qualitative formulation of COT, such an attractor is defined as a subset of resources that is self-sustaining and therefore invariant.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 9; reference: Ashby, W.R. 1962. “Principles of the self-organizing system.” In: H. von Foerster & G.W. Zopf (eds). Principles of Self-Organization. pp. 255-278. Pergamon Press.


“In the qualitative version of COT, a disturbance can be represented as the removal of a resource that the organization relies upon (e.g., cows), or as the introduction of a new resource (e.g., mice) that reacts with some of the existing resources (e.g., grain), thus interfering with the network of reactions that defines the organization....

“Ths simplest method of control [for a disturbance] is buffering: maintaining a large enough reserve of resources so that temporary reductions in availability have little effect....

“The next method is negative feedback: organizing the network of reactions in such a way that deviations from the desired concentration of resources are automatically counteracted after each cycle of consumption and production....

“The third basic control method is feedforward: neutralizing the disturbance before it has had the chance to perturb the functioning of the system. This can be achieved by reactions that consume the disturbing resource before it could have interfered with other, vital resources. The tricky part here is that these neutralizing reactions will only be enabled when a disturbance is present for them to react with....

“An example of such a collection of neutralizers [for responding in feedforward manner] are the genes of an organism that are activated via a particular molecular pathway whenever the cell encounters a particular disturbance. Once activated, these genes produce enzymes catalyzing reactions that neutralize the disturbance.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. pp. 11, 12.


“We have argued that arbitrary networks of reactions will self-organize to produce sustainable organizations, for the simple reason that organizations are attractors of their dynamics. It is less obvious that these organizations would also be resilient. However, evolutionary reasoning shows that resilient outcomes are more likely in the long run than fragile ones.

“First, any dynamical process starts from some point in the state space of the system, while eventually settling down in some attractor region within that space. Attractors are surrounded by basins of attraction.... The larger the basin, the smaller the probability that a disturbance pushing the system out of its attractor would also push it out of the basin, and therefore the more resilient the organization corresponding to the attractor.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 12.


“Without going into the necessary mathematical details of the construction, we will here argue that such spatial and hierarchical differentiation [e.g. boundaries, vessels, etc.] can be introduced into COT models without essential changes in the formalism.

“First, as we already noted, the concept of agent is easily reinterpreted in COT as a catalyst–i.e. a resource a that is necessary to enable a reaction, but that is not itself affected by the reaction it triggers: a + b + c -> a + d. This can be read as ‘agent a processes b + c into d’. Since an agent can catalyze several independent reactions (e.g. a + f -> a + g + h), it will be characterized by a list of ‘condition-action rules’, of the form a: b + c -> d, f -> g + h, ...” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 14.


“To define superagents, we may note that complex organizations often contain suborganizations: subsets of their resource set that are able to autonomously self-sustain while exchanging some of these resources with other processes or suborganizations within the larger organization. These exchanged resources can be categorized as either input, In(S), or output, Out(S), of the suborganization S. This allows us to summarize the activity of S by the following ‘higher-order’ reaction:

“S + In(S) -> S + Out(S)

“Suppose that In(S) = {a, b} and Out(S) = {c, d, e}, then we can write this as a more conventional condition-action rule:

“S: a + b -> c + d + e

“The fact that S is itself constituted of a network of resources and reactions does not really make any difference when seen from the outside. S behaves like a ‘black box’ which processes a given input (a + b) into a specific output (c + d + e). If S is sufficiently resilient, it can maintain itself even when the input changes, producing a correspondingly changed output of ‘waste products’. This means that S behaves like a higher-order agent, capable of executing a range of condition-action rules, while itself remaining invariant. The larger organization of which S is a subset may itself be embedded in a network of reactions, thus defining an agent of an even higher order. While we still need to investigate this construction mathematically, this appears to open the door to the modeling of the dynamical hierarchies and metasystem transitions that characterize the multilevel self-organization that we see in the evolution of life and society.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 14.


“A general advantage of COT is that you can freely mix resources of very different types, such as organisms, chemicals, economic goods, and even human decisions. This makes it eminently suitable for modeling the truly complex social-technological-economical-ecological-physical systems that surround us, such as cities, businesses, regions, or our planetary society. This is the objective of the new approach of global systems science.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. p. 16.


“... a highly evolved organization is likely to exhibit a variety of regulatory mechanisms characteristic of a cybernetic or autopoietic system. Such a system acts like a goal-directed agent that aims to sustain its essential organization while suppressing any disturbances that may push it away from this goal. That means that it exhibits not just the most basic features of life, but of cognition, intelligence, and intentionality. Like all living systems, the implicit goal or intention of an organization is to maintain and grow. To achieve this, it needs to produce the right actions for the right conditions (e.g. produce the right resource to neutralize a particular disturbance, or to exploit a particular input). This means that it implicitly follows a system of ‘condition-action rules’ that play the role of the organization’s ‘knowledge’ on how to act in its environment. The capability of ‘computing’ the right combination of action(s) to solve a given problem constitutes the organization’s ‘intelligence’....

“Because this abstract conceptualization is independent of any specific substrate–such as a brain–it is applicable to systems that exhibit intelligent behavior but that are otherwise very different from the individual human beings that we tend to see as the sole possessors of minds. Examples are the intelligence exhibited by insect societies, plants, bacterial colonies, human organizations, the self-regulating planetary ecosystem–i.e. ‘Gaia’–, and the Internet in its function as a ‘Global Brain’.” Heylighen, Francis, Shima Beigi & Tomas Veloz. 2024. “Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems.” Systems. 12:111. 10.3390/systems12040111. pp. 16-17.


“...multicellular systems, from biofilms to metazoa, have faced several problems in order to achieve a viable integration between their cellular components. Among the main ones, are the trade-off between cell differentiation and avoidance of conflict, the control and coordination of cells, the availability of nutrients, the access to signal molecules and the possibility of intercellular communication, modularity, structural cohesiveness, to mention the main ones.

“To explain how living systems found solutions to these problems, different theoretical approaches emphasize different aspects as the core of multicellularity: self-organization, the capability to interpret positional information, gene regulation, cell-to-cell communication, and its role in cell differentiation, division of labor between reproductive and vegetative functions, genetic homogeneity, low conflict, metabolic integration, increase in the energy available and the development of larger genomes, among others.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 2.


“It is our contention that the increase in size which characterizes multicellular organisms, and which enables cell differentiation and division of labor, goes hand in hand with and directly depends for its viability on the capability to organize the intercellular space.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 2.


“Yet this is not the only way to look at the problem [evolutionary explanations of the origin of multicellularity]. While not denying the importance and role of evolutionary considerations for the study of the origins and the histories of the lineages of multicellular systems, another possible research avenue is to investigate the distinctive features of their physiologies. This alternative approach implies looking at how these systems are organized and how their organization is necessary for their persistence.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 3.


“When considering the problem from this perspective [how multicellular physiology works rather than its evolution], several types of features have been proposed as necessary for multicellularity, including genetic homogeneity and unicellular bottlenecks, low conflict, metabolic integration, genetic control, patterns of self-organization, etc. In particular, two closely interdependent characteristics have been suggested as distinctive of multicellularity and crucial for the functioning, maintenance, and viability of multicellular systems: cellular differentiation and increase in size with respect to unicellular systems.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 5.


“Cellular differentiation is a distinctively multicellular feature. It might seem trivial to say, but unicellular systems can only produce different phenotypes and play distinct functions in time. Multicellular systems, from biofilms to metazoa, can instead exhibit several differentiated phenotypes at the same time. Such a capability is an essential requirement for functional integration. Through cell differentiation, multicellular systems become in principle capable to harbor components playing different functional tasks, and hence to realize division of labor under certain conditions.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 5.


“In sum, focusing either on the increase in size or on signal-induced cell differentiation, or even on both factors together, cannot explain why multicellular systems are not limited to just small balls or thin layers of cells, but instead give rise to complex, differentiated and integrated structures. In our view, something more fundamental is missing to understand the reason why the size and number of cells can increase in such a way to take advantage of cell differentiation and allow cells to coordinate and actually carry out activities with different functional roles.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 6.


“A crucial distinction can be made between those structural constraints which statically and passively reduce the degrees of freedom of the processes they canalize, and those dynamic control constraints that actively select between the degrees of freedom available.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 7.


“Not all the cells can proliferate and not at any time. Therefore, the system activates the division of certain cells in specific moments in time and inhibits it in others. Moreover, depending on the state of the system, the capability of motility is also inhibited in most cells. When those constraints that act on proliferation, motility, mobility, etc. fail, or their properties are modified, these changes may give rise to different forms of multicellular organization, more often incompatible with the original one, such as in cancer, and contribute to the development of several human diseases, such as osteoarthritis, fibrosis, etc.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 8.


“While providing stable anchorage, and exhibiting specific features in different tissues, ECM structures also carry out differential constraining activity that functionally modulates the state of cells. They are dynamical constraints because, at different physiological time-scales, they can change their physical state, density, composition, 3D shape, or the state of activation of their proteins, in relation to the state of the system or of a specific tissue. For example, mechanical forces and molecular interactions can alter the functional domains of proteins embedded in the matrix; building and dissolving the matrix also selectively modifies its control capabilities in time. In addition, enzymes can act as regulatory switches that modulate the control capabilities of the ECM by creating and modifying collagen cross-links.

“In turn, depending on their (activation) state, ECM structures can constrain in different ways the behavior of cells by acting upon specific membrane receptors, by inducing changes in cells shapes, or by modulating the activity of signaling molecules and morphogens. These activities are functional insofar as they contribute to the overall maintenance of the system.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] pp. 8-9.


“Changes in the stiffness of the matrix also control cell differentiation as well as migration, apoptosis, and proliferation.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 9.


“The functional features of the intercellular space include: the control of cell fate and behavior; the enablement of metabolic capabilities by providing access to nutrients (e.g., through vascularization); physical properties such as resilience to physical stress and structural cohesiveness; the constitution of basement membrane for anchoring epithelial or endothelial cells, tendons, bones, etc.; spatial differentiation and modularity with distinct areas characterized by different boundary conditions for cells, and the realization of specialized areas and tissues; the creation of permeable or semipermeable barriers and interfaces by contributing to structure the epithelium, or directly, like in the kidney; and finally, the organization of mobility and communication at medium and long range (beyond cell-to-cell signaling).” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 9.


“Unlike in unicellular organisms, motility is inhibited in most cells of multicellular systems.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 10.


“... multicellular systems achieve integration by organizing space at longer ranges, by controlling the movement of some cells and of those nutrients, signals, control molecules, etc. that are necessary for the coordinated activity of the components in different areas of the system. Long-range control upon movement and communication within the system is achieved in at least three different ways: (1) by making components mobile in a fluid through vascularization; (2) by means of cells, such as the immune ones, that retain the capability of motility and move in the blood or through the ECM in tissues; and (3) through signal transmission architectures realized by networks of neurons.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 11.


“Immune cells can move through blood, but in most cases they reside in tissues, where they are highly mobile within the ECM network that fills the space between tissues cells. There, these primed and memory cells, called ‘T resident memory cells’, provide for a primary system of immune surveillance at the level of tissues and organism’s barriers. Through their mobility among the cells that constitute the tissue, they can exert a localized and specific control. By delivering highly specific signals to cells within tissues, they play important fine-grained coordinating functions, such as, among others, tissue repair, the regulation of fat cell metabolism to adapt to prolonged exposure to environmental cold, and communication with the nervous system in the guts.... The movement of immune cells in tissues is afforded by the porosity of the molecular network that makes up different types of ECM, depending on the orientation and density of the fibers. It is made possible also by the ability of immune cells to modify their shape, which in turn is limited by the nuclear size and shape and by its intrinsic ability to deform as well.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 12.


“We provided a theoretical framework to understand the role of spatial organization in multicellular systems, based on the role (1) of ECM structures as control mechanisms that organize the system at short (together with cell-to-cell interactions) and medium ranges and (2) of vascularization, immune cells, and neural cells, which control movement and communication at longer ranges. The central idea is that the intercellular space is internally differentiated and functionally organized by these dynamic extracellular (ECM) or supracellular (endothelium, epithelium with their BMs [basement membranes]) structures that play an active role as control mechanisms.” Bich, Leonardo, Thomas Pradeu & Jean-Francois Moreau. 2019. “Understanding Multicellularity: The Functional Organization of the Intercellular Space.” Frontiers in Physiology. 10(1170): 1-17. 10.3389/fphys.2019.01170. [4] p. 13.


“Complexity refers to interesting behavior produced by the interactions of simple parts. Emergence refers to simpler higher order behavior that arises from underlying complexity. On the one hand, we have complexity from simplicity. And on the other hand, we have simplicity from complexity.” Page, Scott. 2011. Diversity and Complexity. Princeton UP. p. 26.


Wolfram considers complexity to be a matter of kind, a property. He classifies systems as producing one of four types of outcomes that can roughly be characterized as: fixed points, simple structures/periodic orbits, randomness, or complexity. In this conceptualization, complexity lies between simple structures and randomness.” Page, Scott. 2011. Diversity and Complexity. Princeton UP. pp. 26-7; reference: Wolfram, S. 2002. A New Kind of Science. Wolfram Media.


“Chaos is not randomness. Chaos refers to extreme sensitivity to initial conditions.” Page, Scott. 2011. Diversity and Complexity. Princeton UP. p. 32.


“The soma constitutes cell lineages created to keep the germ line alive and reproducing. While all cells are ‘born’ and die, the line of germ cells is potentially immortal, and somatic lineages always die when the organism itself dies – but we do note that the picture gets rather quickly complicated in organisms with clonal abilities.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. “Introduction.” pp. 1-19. Cambridge UP. p. 2.


“Weismann’s germ-soma theory was profoundly influential and continues to inspire researchers to this day. However, his theory predicts only that senescence will occur in all organisms with a strict germ/soma separation and so cannot account for senescence observed in unicellular life, plants, fungi, some animals such as corals, and many microbes.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. “Introduction.” pp. 1-19. Cambridge UP. p. 3.


“In many cases, we know now that a lack of senescence is likely a real phenomenon in some groups rather than simply a case of inadequate or insufficient data. It was in the 1990s that Caleb Finch gave serious consideration to organisms that exhibit ‘negligible senescence’ and experience no, or only very small, increases in mortality rate with age.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. “Introduction.” pp. 1-19. The Evolution of Senescence in the Tree of Life. Cambridge UP. p. 5; reference: Finch, Caleb. 1990. Longevity, Senescence, and the Genome. U of Chicago Press.


“In purely verbal terms, the disposable soma concept may be understood according to the following sequence: (1) it is important for the organism to invest in sufficient maintenance that the body does not fall apart too soon; (2) however, most organisms in natural (wild) environments die young from extrinsic hazards, and there is little to be gained from investing in better maintenance than is required to keep the body in reasonably sound condition through the typical survival period experienced in the wild; and (3) therefore, under pressure of natural selection to make optimal use of resources, it was a higher evolutionary priority to invest in growth and reproduction than in maintaining a body well enough to last in good condition indefinitely, when the potential utility of such indefinite survival is extremely unlikely to be realised.

“The significance of the disposable soma theory is that it explains not only why ageing occurs but also how it is caused, the primary mechanisms of ageing being predicted to involve the accumulation of molecular and cellular defects.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 2: “The Disposable Soma Theory Origins and Evolution.” by Thomas B.L. Kirkwood. pp. 23-39. Cambridge UP. p. 26.


“In general, it is now recognised that ageing most likely involves multiple kinds of molecular damage and multiple mechanisms driving its accumulation. This has been advanced through the development of ‘network’ models that demonstrate the importance of interactions among various mechanisms. Nevertheless, the core recognition that holding the accumulation of damage in check is metabolically expensive is widely accepted.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 2: “The Disposable Soma Theory Origins and Evolution.” by Thomas B.L. Kirkwood. pp. 23-39. Cambridge UP. p. 28.


“These two population genetic mechanisms, mutation accumulation [for genes that are detrimental in later life] and antagonistic pleiotropy [genes that have positive effects in early life before reproduction but that have negative effects in later life], thought to underlie the evolution of senescence, are not mutually exclusive; research has focused on distinguishing the relative importance of both mechanisms, and each has some support. The end result is that the expression of genes with late-acting mildly deleterious effects is expected in nearly every tissue, causing a near-universal and coordinated senescence.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 9: “The Evolution of Senescence in Nature.” by Andrew I. Furness & David N. Reznick. pp. 175-197. Cambridge UP. p. 176.


“Perhaps the most widely cited prediction, and one that has largely become synonymous with the evolutionary theory of senescence, is that high levels of (extrinsic) mortality are expected to result in the evolution of higher rates of intrinsic mortality due to senescence. In an environment with high extrinsic mortality, the probability of reaching old age is reduced relative to that in a low-mortality environment.... In contrast, in an environment with low extrinsic mortality, there is a high probability of reaching old age, favouring a more balanced investment in reproduction and somatic maintenance and resulting in delayed senescencce. We hereafter refer to this as the ‘Williams prediction’.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 9: “The Evolution of Senescence in Nature.” by Andrew I. Furness & David N. Reznick. pp. 175-197. Cambridge UP. p. 176.


“Although dozens of proximate or mechanistic theories of senescence exist, there are only three dominant evolutionary theories: mutation accumulation, antagonistic pleiotropy and disposable soma.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 20: “Life History Trade-Offs Modulate the Speed of Senescence.” by Salguero-Gomez, Roberto & Owen R. Jones. pp. 403-421. Cambridge UP. p. 405.


“We have quantified the speed of senescence using a metric of adult life expectancy across 622 studies from 571 species of animals and plants. We found that the speed of senescence is a rather labile trait across the Tree of Life, a statement that is supported by [the?] fact that the phylogenetic signal is fairly high, particularly in animals. This labile evolution has resulted in 48.2 per cent of the included species having an average mature life span of less than thirty years, with the remaining species extending this figure well beyond that age. The statement is further bolstered by the 3.7 per cent of species achieving truly exceptional adult life spans, living well over 150 years after they become reproductive.” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 20: “Life History Trade-Offs Modulate the Speed of Senescence.” by Salguero-Gomez, Roberto & Owen R. Jones. pp. 403-421. Cambridge UP. p. 417.


“Nevertheless, despite the long and interesting journey ahead [in the study of ageing], it is pleasing to find that just two main axes are sufficient to summarise almost 80 per cent of life history variation. These axes boil down to (1) how fast or slow species live and (2) how long species remain reproductive and when they start reproducing. It is also gratifying to show that both of these continua of variation robustly predict the speed of senescence: species that live slow, start reproducing early and frequently, achieve long mature life spans, which here we argue is the demographic output of either negligible or perhaps even negative senescence rates....” Shefferson, Richard P., Owen R. Jones & Roberto Salguero-Gomez. 2017. The Evolution of Senescence in the Tree of Life. Ch. 20: “Life History Trade-Offs Modulate the Speed of Senescence.” by Salguero-Gomez, Roberto & Owen R. Jones. pp. 403-421. Cambridge UP. pp. 418-419.


“A widely accepted distinction contrasts symmetric asexual reproduction, such as binary fission, where the parent’s body is divided equally between the two offspring individuals, and asymmetric asexual reproduction, such as budding, where the parent persists as a distinct individual across the reproductive act while a minor portion of its body becomes its offspring. In the symmetric binary fission of many protists, the two cells that are thus obtained are considered sisters, descendants of an individual which, by dividing, has ceased to exist. But in the budding of a cell of the common bread yeast, the larger cell is called the mother cell, while the smaller cell which detaches from it is called the daughter cell. This unequal treatment might seem rationally unsound... However, this distinction might be justified, at least in certain cases, by the different behaviour of the products of reproduction with respect to senescence. The two Euglena daughter cells, like two sisters, have the same life expectancy, but the yeast mother cell generates an individual with a longer life expectancy than her own current value, exactly as it should be for a mother’s offspring.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. [Also cited in 2023 QUOTES] Cambridge UP. pp. 15-6.


“The characterization of a life cycle rests on the possibility of distinguishing the reproductive events, which imply the transition to a new generation, from the processes of development, which are instead transformations of the same individual.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. [Also cited in 2023 QUOTES] Cambridge UP. p. 23.


“The reader will certainly have noted that, as in the case of reproduction, a definition of development cannot be given without an explicit concept of the individual ....” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. [Also cited in 2023 QUOTES] Cambridge UP. p. 25.


“... senescence (or biological ageing) is a cumulative process of change, at different levels of body organization, which progressively corrupts metabolism and body structures, producing a deterioration of the qualities of the organism that eventually leads to its death.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. [Also cited in 2023 QUOTES] Cambridge UP. p. 35.


“The property of sexual reproduction seems to be an attribute also of sex in the broad sense. Ciliates reproduce only asexually, in many species by binary fission, but commonly practise a form of sex called conjugation. Here, two individuals (conjugants) unite temporarily, exchange genetic material, and then separate again. The result of this exchange is a pair of independent individuals (ex-conjugants) genetically identical to each other, but genetically different from both conjugants. In most ciliates, the clone that originates from an ex-conjugant after separating from its partner shows a form of senescence, consisting of a limit to the number of cell divisions in the propagation of the clone. This number varies from species to species, but also between strains of the same species. In Tetrahymena this limit varies between 40 and 1500 divisions. Moreover, the clone goes through different maturation stages that in a multicellular organism we would not hesitate to describe as developmental phases. During an initial period of ‘sexual immaturity’ of the clone (measured in number of divisions since the last conjugation) individuals can only multiply asexually, without being able to conjugate. Then follows a period of ‘sexual maturity’ during which they will be able to conjugate.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. [Also cited in 2023 QUOTES] Cambridge UP. pp. 37-8.


“In some species, however, a further contribution to the variety and complexity of cycles is provided by the possibility, at certain stages of the cycle, of taking one of two or more alternative options for reproduction or development. The ‘choice’ generally depends on the contingent state of the organism and/or the occurrence of specific environmental conditions. This is a form of phenotypic plasticity, which could be called life-cycle plasticity, through which developmental processes and/or the mode of reproduction can first diverge and then converge again in a subsequent stage, which can thus be reached through alternative paths within the same cycle.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 73.


“In spiders of the genus Tidarren, the male self-amputates one of the two copulatory appendages before using the other to inseminate the female. In T. argo, from Yemen, after mating, the females pull out this remaining pedipalp, so that the male can mate only once (traumatic semelparity). Similarly, in some land slugs of the genera Limax, Ariolimax and Deroceras, apophallation was often observed, namely the amputation of the penis–by the owner or by its partner – at the end of mating.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 76.


“According to some microbiologists, when the production of a single spore is accompanied by the destruction of the mother cell, sporulation should not be interpreted as a reproductive process but rather as a form of differentiation.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 87.


“Fission and sporulation are often found in the same bacterial species, and the switch from one process to the other is induced by changing environmental conditions. When these are favourable, the bacterium undergoes fission; when they become adverse, it turns instead to sporulation.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 88.


“The term spore is used with many, more or less overlapping, meanings in the biology of unicellular and multicellular prokaryotes and eukaryotes. What is common to most of these spores is the fact that they are reproductive cells, of a sexual or asexual origin, which can develop into a new organism without merging with another cell, in this respect behaving unlike gametes. Moreover, the spore is often a form of quiescence and resistance, often in relation to a dispersal phase of the life cycle.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 92.


“The problem of the origin and maintenance of sexual reproduction is considered by many as the ‘main problem of evolutionary biology’, often labelled as the ‘paradox of sex’.

“Sexual reproduction is widespread in all major eukaryotic groups, but it seems to present an insurmountable disadvantage compared to asexual reproduction. With the same reproductive investment (number of eggs), females that reproduce asexually can have twice as many second-generation descendants as females that reproduce sexually, simply because they do not waste resources generating males, which do not produce offspring by themselves. This is the so-called ‘twofold cost of sex’, but more correctly it should be called the ‘cost of males’, because it only applies in the case where sexual reproduction is not isogamous.... Furthermore, with regard to the genetics of hereditary transmission, sexual reproduction (anisogamous or not) can break apart favourable gene combinations that had been stabilized by selection in previous generations, or create deleterious or non-viable combinations of genes (e.g. due to genetic incompatibility).

“Given these heavy costs of sex, it is assumed that sexual reproduction must provide some selective advantage, to an extent that at least compensates for these disadvantages. Many hypotheses have been formulated, generally based on the idea that despite the deficit in terms of number of descendants (low fecundity fitness) sexual reproduction can lead to an improvement in the quality of offspring (high viability fitness) in sexual populations.

Most of these hypotheses are variants of four main ideas: (i) sex facilitates adaptation to new environments by combining favourable genetic variants from different genomes (Fisher-Muller model); (ii) sex confers advantages to the host in coevolution with its parasites, through the negative frequency-dependent selection imposed by the latter (Red Queen model); (iii) sex maintains adaptation by removing deleterious mutations more effectively (deterministic mutational models); (iv) sex releases beneficial mutations from association with deleterious alleles in the genomes where they appear (mutational load models)..... Different types of advantages could obviously operate in a synergistic way. More recent theoretical work suggests that occasional or conditional sex, involving facultative switching between sexual and asexual reproduction, is the optimal reproductive strategy. Therefore, the true ‘paradox of sex’ could turn out to be the prevalence of obligate sex.

“This enigma, ‘why sex?’ (or the ‘paradox of sex’), is countered by the opposite problem, ‘how to manage without sex?’, a problem posed by so-called ‘ancient asexual scandals’. If the prevalence of sexual reproduction shows that it must necessarily have advantages over asexual reproduction, either those thus far hypothesized or others, how is it possible that there are groups of organisms that have exclusively reproduced asexually for millions of years?” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. pp. 107-8.


“We refer to an individual’s sex condition as its state with respect to sexual function, either male or female, but also both male and female (hermaphrodite) or neither male nor female (sexually indeterminate). Thus there are two sexes, but four sex conditions.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 112.


“Hermaphroditism is estimated to occur in 5-6% of animal species (and almost one-third of non-insect species), with over 70% of animal phyla containing at least one hermaphrodite species.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 125.


“Within the vertebrates, it is striking that no bird or mammal species practises parthenogenesis regularly. In the case of mammals, it is possible that they cannot abandon amphigonic reproduction because of genomic imprinting: in the course of gametogenesis some genes are modified in such a way that they will be able to function only if transmitted paternally, others only if transmitted maternally, and therefore normal development is possible only if the individual possesses both paternal and maternal genes.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. p. 175.


“According to the traditional description of this phenomenon [geographical parthenogenesis], some plant and animal species include both amphigonic and parthenogenetic populations, the latter often polyploid.... It is logically impossible, in fact, to apply the biological species concept to organisms with uniparental reproduction, so it is perhaps better to say that some amphigonic species are accompanied by populations derived from them, which practise only thelytokous parthenogenesis.... Parthenogenetic populations usually occupy marginal areas of the species’ range, subject to difficult or even extreme environmental conditions, and are unusually quick to colonize new areas.

“A typical example is the presence of parthenogenetic populations, almost always polyploid, of weevils... in Alpine areas and in the northernmost regions of Europe, which have been free of ice for only a few thousand years, or even less. The greater capacity for colonization demonstrated by these populations can be partly attributed to the short-term advantage of uniparental reproduction (i.e. a single individual can found a new population); in part, however, it seems to be due to their polyploid condition.” Fusco, Giuseppe & Alessandro Minelli. 2019. The Biology of Reproduction. Cambridge UP. pp. 180-1.


“With different formulations, a complex life cycle has been described as one that includes abrupt ontogenetic changes in an individual’s morphology, physiology, or behaviour, usually associated with a change in habitat, thus passing through two or more distinct ecological and morphological phases for each complete generation. These definitions do not explicitly include the complexity related to the possibility of multiple generations within the same cycle, but other definitions take this aspect into account, specifying that the two or more discrete phases we can identify in a complex life cycle can be either phases in the development of an individual or distinct generations in a multiple-generation cycle.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 1.


“... a life cycle includes development as a part of it, but can be composed of multiple developmental and reproductive phases.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 2.


“There are different ways in which a life cycle, either monogenerational or multigenerational, can present developmental complexity.

“A first kind of life-cycle developmental complexity is a function of the number and magnitude of changes an individual organism undergoes throughout its development. This linear developmental complexity is widespread across the tree of life. Just think of the contrast between embryonic and postembryonic development in many multicellular organisms. But very different developmental stages can succeed each other also along post-embryonic life. In many animals, including numerous marine invertebrates and most holometabolous insects, the adult form is markedly distinct from juvenile (larval) form(s), to the point that the passage between these segments of life is generally qualified as a metamorphosis....

“In a second kind of life-cycle developmental complexity, at certain stages of a monogenerational cycle, alternative options can be taken: developmental pathways can first diverge and then converge again in a subsequent stage, so that the latter can be reached through alternative paths within the same cycle....

“A third type of developmental complexity is found in cycles with reproductive (rather than developmental) options, that can actually entail more individual developments, when, for instance, asexual reproduction is facultative and development has a very different start than from a fertilized egg. An example of this reproduction-dependent developmental complexity is the contrast between development starting from a fertilised egg and development starting from a bud in a hydra polyp.

“A fourth type of developmental complexity is offered by multigenerational life cycles. This sequential developmental complexity can be found in many cycles with alternations of generations, haploid and diploid as in most plants, but also sexual and asexual as in many cnidarians, or unicellular and multicellular as in slime moulds.

“Combinations of these four kinds of complexity are not only possible, but widespread.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 2.


“Cycles with parallel developmental complexity bring to light at least two general questions. One is the contrast between the cycle of Caenorhabditis, where it is the developing juvenile that senses the environmental conditions that eventually may take to the alternative developmental route through the resting dauer stage, and the cycle of Streblospio, where alternative developmental pathways are partially under maternal control, through the parental resource allocation in the egg. This contrast, rather than suggesting a further splitting in the classification of life cycles, matches with a more general phenomenon in development, where the boundary between the developmental processes under the control of the developing individual and those under the control of the mother, either (epi)genetically or physiologically, can be set at different places, with variation both within species and among closely related species.... A second question is the difficulty of tracing a neat boundary with other phenomena of multiple development. One grey zone is at the boundary with sequential developmental complexity, as exemplified by the cycle of Strongyloides, where the switch between alternative developmental pathways takes also the value of a switch between a monogenerational and a multigenerational life cycle.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 5.


“Cycles with reproductive options are a wide class of phenomena, including both monogenerational and multigenerational cycles. Reproductive options occur whenever a given reproductive modality is facultative or optional, rather than obligate or constitutive. Parthenogenesis is facultative in many molluscs, annelids and arthropods, and also in some vertebrates, including the Komodo dragon. Self-fertilization is facultative in various hermaphrodite animals, including some pulmonate gastropods, while self-pollination is facultative in a number of flowering plants, including various members of the legume, orchid and aster families. Likewise, asexual reproduction is facultative in many organisms that usually reproduce sexually.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 5.


“Not surprisingly, fission in metazoans is generally associated with high regenerative capacities, and the relationship between reproduction and regeneration is a key aspect of the life cycles with reproduction-dependent developmental complexity.

“Many annelids with high regenerative abilities practice asexual reproduction. An evolutionary connection between regeneration and asexual reproduction is suggested by the extensive similarities between the developmental mechanisms underlying these two processes.... Fission and regeneration, although very similar in many respects, present nonetheless important differences in the extent and timing of tissue remodelling, as well as gene expression. Thus, although regeneration and asexual reproduction appear to be evolutionarily related, they do not define equivalent developmental trajectories.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 6.


“In cubozoan cnidarians, the polyp disappears when literally transforming into a medusa. Should this count as a reproductive or a developmental event? On the opposite side, in the metamorphosis of many marine invertebrates, most of the larval body is discarded and the young derives from a small number of founding (set-aside) cells. In the sea star Luidia sarsii the larva can even continue to swim for months after the juvenile that originated from it has detached. Should this count as a developmental or a reproductive event? The matter is generally resolved by taxon-specific tradition, but this should not obscure the connections among these only apparently completely separate types of cycle.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 11.


“Multigenerational cycles of some multicellular organisms are characterized by a phase of aggregation among the individuals that are generated, usually indicated as a colony. When the colony presents a species-specific form and/or a certain level of integration of the single individuals and/or their divergent specialization, the solitary individual and the colony as a whole can be regarded as two different organizational forms of the same organism. In these cases, the development of the solitary individual is a different kind of development with respect to that of a colony, although some biologists would not call the latter process ‘development.’” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 12.


“Adopting a wide concept of multicellularity, Lamza identified 45 independent multicellular lineages in eukaryotes. These can be grouped into different types, depending on the origin of the multicellular aggregate, e.g., by the division of a single founding cell (clonal multicellularity) or by the gathering of multiple separate cells (aggregative multicellularity), and on the structure of the aggregate, from septate multinucleated thalli to pseudoplasmodial forms, to bodies made of multiple fully compartmentalised cells.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 14; reference: Lamza, L. “Diversity of ‘simple’ multicellular eukaryotes: 45 independent cases and six types of multicellularity.” Biol. Rev. 2188-2209. 10.1111.brv.13001.2023.


“In evolutionary biology, there is a growing interest in the evolution of multicellularity, but the connections between the emergence of multicellularity and the evolution of developmentally complex life cycles are still to be explored.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 16.


“Life cycle evolution is a challenging subject of study, but most of the relevant literature is taxonomically restricted and a general theoretical treatment is still lacking.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 16.


“Many kinds of evolutionary changes are modifications of specific features of the structure of the life cycle, such as its articulation into one or more organizational forms, or the specific mode of reproduction of one of these to the next.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 16.


“Another point is that the complexity of the biological cycle and the morphological complexity of the organism are largely independent. Developmental complexity has been frequently increased, without any obvious consequence for the morphological complexity of the preexisting stages, adult included, by addition of a new intercalary stage, such as the pupa of holometabolous insects, and novel first larval stages, such as the triungulin of blister beetles and other hypermetabolous insects. On the opposite, morphological simplification is not necessarily coupled with decreasing developmental complexity, as witness the Myxozoa, now recognised as morphologically highly simplified forms of Cnidaria, which nevertheless retain considerable life-cycle complexity.” Fusco, Giuseppe & Alessandro Minelli. 2025. “Multiple developmental pathways in organisms with developmentally complex life cycles.” Frontiers in Cell and Developmental Biology. 13:1585073. 10.3389/fcell.2025.1585073. [5] p. 16.


“The distinction between mere happenings versus doing is something that matters a lot to us. Whenever you are dealing with some item (an object, a situation, or an occurrence)–whether by interacting with it, trying to explain it, or appraising it–you first need to determine where that item falls in the basic distinction between mere happenings and doings. Are you dealing with something that is just happening, or has no capacity to do anything, or is just suffering the effects of someone else’s doing? Or are you dealing with something that is a doing, or has the capacity to do things and possibly actively exercising this capacity? The very character and nature of your interactions, explanations, and appraisals–the category to which they belong–is affected by the preliminary classification of their objects into the two basic categories, mere happenings versus doings.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. pp. 1-2.


“According to this picture, agency is the capacity to create or produce, to bring about something new, such as the initiation of an action. A down-to-earth example of this creative power is the ‘at will’ raising of one’s arm from a position of rest, which is prompted by nothing other than one’s choice or decision to do so. A similar example is the case of the arbitrary selection between two or more open paths, especially in the case in which they appear to be equally desirable (think about Buridan’s ass scenario, for instance).

“This picture emphasizes the role of the agent as the source or origin of action, where the action is added as something new to the world. This is why I call this approach ‘agency as creation’ (rather than agency as the mere power of initiation or selection). I also suspect that, for some proponents of this picture, the ideal or model of agential power might be something like a divine ‘fiat’–a divine ex-nihilo creative act. This is not to say that raising one’s arm or selecting from among open paths is without constraints. But within those restrictions, for this picture, agency operates unfettered, hence its ‘discretionary,’ ‘at will,’ or ‘arbitrary’ character.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 9.


“According to the second picture [after agency as creation, above], agency is ultimately a matter of self-constitution or self-maintenance. In this picture, the paradigmatic example of the exercise of agency is the conduct of an organism, which is ultimately directed at the organism’s self-maintenance, that is, at securing its continuous survival in response to the ultimate existential threat: that of dissolution and death. For this picture, agency is ultimately the same as the capacity of life: agents are first of all organisms (self-constituting and self-maintaining entities), and different kinds of agency reflect different kinds of life-form.

“The sense of life in this picture need not be restricted to the ‘biological,’ to material organisms with a metabolism. In principle, it seems possible to extend the idea of self-constitution and self-maintenance to rational life, to the life of a rational subject as a rational subject, where the existential threat arises within the rational order rather than within the causal one. Inconsistency and incoherence might be to rational life what material disintegration is to physical life....

“In agency-as-creation, the creative power is primarily manifested in each individual exercise of agency, as directed at bringing into existence the particular object of that individual act of creation. In agency-as-self-constitution, agency still has a creative aspect, but what is ultimately (and constantly) brought into existence is the agent itself. The agency of self-constitution is the agency of continuous self-(re)creation.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 10.


“Agency as self-constitution seems especially apt at accounting for the agential character of the operations of simpler forms of life. The concern is that it might become much less plausible when applied to the distinctive features of full-blooded agency.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 11.


“According to a third picture [after the first two above], agency is fundamentally the psychological capacity to bring about bodily movements that intelligibly fit with the agent’s desires, cares, concerns, or commitments. A straightforward illustration of this picture is found in what is known as the ‘standard story about action’. According to this story, an action is a bodily movement cause (in the right kind of way) and rationalized (that is, made intelligible) by the agent’s desire for a certain end and her belief that moving he body in that particular way will bring about that end.

“There is something intuitively appealing about the standard story, since it seems to conform to ordinary folk-psychological explanations of action, in terms of the so-called belief/desire psychology.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. pp. 11-12.


“Unlike the pictures of agency-as-creation and agency-as-self-constitution, this picture [psychological] makes the presence of a mind–of a psychic economy–central to the nature of agency; it does so by articulating the internal structure of the psychological structure and its contribution to bringing about genuine exercises of agency. As I remarked earlier, this articulation appears to be a problem for agency-as-creation, especially if that picture insists on the sui generis character of the agential powers. The agency-as-creation picture is at risk of locating agency outside of the natural causal order. A commitment to a naturalistic explanation appears to be a major motivation behind the agency-as-psychological-causation model, hence its insistence that both the internal operation and the external outputs of the psychology be accounted for in terms of the generic bond of ordinary causation. Psychological causation is ordinary causation by elements of one’s psychology, not some kind of supernatural, mysterious, or spooky power.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 12.


“Agency-as-creation is not necessarily guilty of invoking some kind of magic, but it can lend itself to such invocation. For, unlike agency-as-psychological-causation, it does not start from the very idea that agency is a capacity to be explained in terms of the naturalistic operation of some underlying causal structure.

“Notice that this is not a concern with agency-as-self-constitution. A self-maintaining-entity is one with an internal structure and organization that is in principle naturalistically explainable.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 12.


“The psychological and rational dimensions of agency, instead, are only a secondary feature of agency-as-self-constitution, given that self-maintenance is, first of all, an ontological rather than a psychological or rational property. By starting with life rather than with mind, agency-as-self-constitution might end up being too generous in the attribution of agency to simpler kinds of organisms while struggling to account for the distinctive rational dimension of full-blooded agency at the other end of the spectrum.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 13.


“The risk is that the psychological-causation picture might either explain the agent away or surreptitiously and uninformatively assume some homuncular unity within the internal working of the psychic economy.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 13.


“According to this last general picture [after the previous three above] of agency, agency is primarily the capacity to respond to reasons. Agency is first of all exercised in making up our minds on the basis of normative or rational considerations about how we ought to make up our minds....

“It is useful to compare this picture to agency-as-creation. In agency-as-creation, agency is ultimately a matter of making a difference within the causal fabric of the world. This kind of difference can be modeled in terms of changes in the physical world, such as selecting which path to take at a junction or setting into motion an inert body. In agency-as-reason-responsiveness, instead, the change is first of all within a normative rather than a physical space. In agency-as-reason-responsiveness, the agent takes on a new shape because of the new status acquired by some of one’s own attitudes (paradigmatically, one’s judgments) in response to one’s sensitivity to normative considerations.

“Crucial to both pictures is the idea of the agent s the direct source of one’s own agential conduct. But the two pictures drastically differ in the character of the proposed source. This difference can be illustrated by the distinction between ‘authorship’ and ‘authority.’ According to agency-as-creation, the agent is the source of agential conduct. It is so because the agent is the author–the creator or originator of this conduct. This authorship, which retains a voluntary character, is ultimately accounted for in causal terms.

“By contrast, in agency-as-rational-responsiveness, the agent is a source in the sense of being the authority that endorses, judges, or avows something. Being the agent is, first of all, a matter of putting a stamp of approval rather than of directing one’s conduct into some physical direction instead of another.” Ferrero, Luca. 2022. “An Introduction to the Philosophy of Agency.” Ferrero, Luca (ed). The Routledge Handbook of Philosophy of Agency. pp. 1-18. Routledge. p. 14.


“In summary, the brain interacts with its environmental niche through organs of the body. Due to the coevolution of the nervous system and periphery, there is extensive matching between their properties. We hypothesize that inputs from sensors alone are not sufficient for the brain to learn about the organisms’s ecosystem and guide its future actions. Instead, the brain’s actuators–skeletal, autonomic, and endocrine systems together–are essential for attributing significance and meaning to inputs impinging on the organism.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 194.


“The primacy of action and internal senses view is supported by phylogenetics. Early invertebrates were inconspicuous suspension feeders, capturing and ingesting floating particles by essentially running into them. Example organisms are jellyfish, brittle stars, many annelid worms, and other cnidarians. When food is abundant there is no need for sensors to detect them; direct interception and diffusional deposition are sufficient....

“A main reason why the body plays an undeservedly low role in the studies of cognition is that in our everyday experience we do not need to move, walk, or even adjust the sensors to think, recall, plan, imagine, or feel....

“An alternative, evolutionary, hypothesis is that cognition is an exaptation and expansion of the circuits and algorithms serving bodily functions. As organisms continued to evolve more complicated bodies, more precise control and coordination of internal bodily functions were also required. Early neuronal circuits developed a symbiosis with the body to support the body’s fundamental functions, such as ion equilibrium homeostasis, energy management, metabolism, respiration, eating, drinking, excretion, temperature regulation, movement, sensing and responsiveness, reproduction, and sleep. Although many of these survival mechanisms exist even in organisms lacking a nervous system, neuronal circuits interacting with body functions perfected them, enhancing the organism’s prosperity....

We propose that early neural circuits (e.g., the primordial hippocampus) allowed for the effective prediction of internal states through flexible action sequences and the exploration of body space to construct action maps. These prediction circuits allowed for a more efficient exploration of the organism’s niche. As we discuss below, spatial navigation is not simply a series of responses to environmental cues but an internally organized neuronal sequence operation that can be matched to external landmarks. In turn, when the internally organized neuronal sequences disengage from the body actuators, the ensuing fictive or virtual navigation computed by these same circuits can be equated with ideas of memory, planning, and imagination. A feature of this hypothesized exaptation is that even the most complex cognitive and emotional operations keep their dependence on the action repertoire of the organism. Thinking thus may be conceptualized as time-deferred action in the body-disengaged brain. The utility of thought can be evaluated only if the content of thought is acted out sometime in the future. In summary, cognition is prospection, inducing current of time-deferred actions to acquire a desired goal. Below, we discuss these seemingly disjunctive, yet likely related, brain operations and entertain the possibility that brain-body partnerships evolved initially for internal regulation of complex bodies and later gave rise to cognitive processes.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. pp. 195-6.


“If we consider the simple swim-or-rest algorithm described above, an organism can get quite far in its niche with essentially two action states and no external sensory inputs. In animals with a nervous system, this bistable algorithm has been mechanistically–and bidirectionally–coupled to actions of the body throughout evolutionary history and can be observed when taking physiological measurements from essentially any body part. Brain states in vertebrates also fall into dichotomous categories and correspond roughly to what early behavioral research referred to as preparative and consummatory (or terminal) classes.... They are also referred to as voluntary and nonvoluntary or conscious and nonconscious brain states.... Consummatory behaviors include feeding and drinking, resting and its extreme form, sleep. Thus, all behaviors can be considered as part of a sequence of action-rest transitions.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 196.


“The consummatory brain state is an obvious form of disengagement when interaction with the environment is reduced. Brain-body interactions persist but are qualitatively different. Spike activity of neurons in many structures continues, albeit in a different format. During such states, sharp-wave ripple (SPW-R) events are the dominant activity form of the hippocampus. During SPW-Rs, a large fraction of neurons fire together in a highly synchronized manner.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 196.


“Taken together, the hippocampal system appears to have privileged access to stress and metabolic states of the body and ability to bias them.

“The high dimensionality of this internal sensor function is well illustrated by the high fraction of neuronal and glial receptors influenced by circulating substances. While many veins of neuroscience research conceptualize an average neuron integrating at most a dozen types of signals (i.e., GABA, glutamate, and canonical neuromodulators), a typical hippocampal neuron possesses receptors and channels that allow it to integrate a minimum of 60 unique signals (i.e., metabolitess, hormones, temperature). These nonsynaptic neuronal and glial receptors are activated by the many substances produced by the immune system, peripheral organs, and microbiota-gut-brain signaling axis.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 202.


“In summary, the hippocampal system may have undergone two exaptation processes during evolution. Primordial neural circuits that initially evolved to coordinate sequential events in the body (allostasis) were co-opted to anticipate and incorporate the bodily outcomes in physical exploration of the organism’s niche. Second, these same circuits were again co-opted for an internalized processing of sequences of experience (episodic memory). Thus, the hippocampus may perform a singular algorithm that relates its sequential, relational, content-agnostic mechanisms to actions performed by the body or to an internalized version of action sequences. The disengaged mode of self-organized neuronal activity provides access to a virtual world of vicarious or imagined experience and constitutes a gateway to a variety of cognitive processes. Memory is a transmission mechanism gleaned from past experience to guide current and future actions rather than a storage of symbols of world events and facts. Thus, navigation through either a physical space or alternatively a landscape that exists only in the imagination (i.e., mental time travel and planning ahead for an action) may be accomplished through identical neural mechanisms. These complex computational mechanisms may have evolved from the need to predict future metabolic needs of the body.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 204.


“Throughout evolution, the brain’s constant partner is the body it serves. A critical task for the brain is to coordinate the numerous body functions. These brain functions, originally evolved to regulate and predict metabolic and motor processes in the body, have undergone various levels of exaptation, expanding the same circuit computations to perform environment-disengaged activity in the service of cognition. Because of these exaptation steps, even the most complex operations keep their dependence on the action repertoire of the organism. The unified goal of both simple and complex neuronal circuits is to predict the consequences of their outputs. At the highest level, cognition is prospection, inducing immediate or deferred actions to acquire desired goals. While the metaphor of embodied cognition has been extensively discussed over decades within cognitive science, methods and conceptual thinking in neuroscience have matured only recently to take a fresh look at the embodied mind or, more appropriately, the embodied brain.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. pp. 204-5.


“The functioning of an organism depends on its ability to maintain stable homeostatic states, such as body temperature and blood sugar levels, within narrowly regulated ranges in the face of constant perturbations from the environment. Similarly to the whole organism, maintaining a physiological self-organized dynamic of brain circuits is essential for its physiological operation. Maintenance of this dynamic (also known as spontaneous activity or internal state) can also be considered a homeostatic process, which is achieved through cooperation with the body.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 205.


“Maintenance of the internal dynamic is a costly operation, as illustrated by the high energy budget of self-organized network activities. More than half of emitted spikes by forebrain neurons serve to maintain homeostatic network dynamics, and most of the remaining, temporally coordinated, spiking across neurons also contributes to internally generated oscillations and assisting homeostatic body functions. Only a small fraction of neurons respond to external stimuli or control body actuators. Thus, sensing and associating environmental stimuli mobilize only a small fraction of neurons at any given time. This responding mode is energetically cheap and secondary compared to sustaining the perpetual internal operations.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 205.


“Our review illustrated several homeostatic loops within the brain and between brain and body. Even for a seemingly unitary function, for example, glucose regulation, multiple loops act cooperatively. A main issue that remains to be resolved is the interactions among the many loops. It is unlikely that separate homeostatic loops work in isolation. The alternative is that we are dealing with a tangled web of loops; thus, understanding any single homeostatic regulatory loop makes sense only when viewed in the context of related regulation mechanisms. For example, energy, temperature, orthostatic blood pressure, food-seeking motor activity, sleep, and memory appear distinct and are typically investigated in different laboratories, yet changing feedback strengths in one loop may affect several other loops. Multiple substances of the body may converge on the same brain effector mechanisms. Conversely, the same substance/input may differentially activate brain circuits, depending on the affordances available for the organism at different occasions in the environment. Recognizing the interdependence of the many brain-body interactions and, possibly, their hierarchical or nested relationships is a key step in disentangling their specific contributions in such multivariate loops.” Buzsaki, Gyorgy & David Tingley. 2023. “Cognition from the Body-Brain Partnership: Exaptation of Memory.” Annual Review of Neuroscience. 46:191-210. 10.1146/annurev-neuro-101222-110632. p. 205.


“A more ecologically balanced point of view would examine the protoecological cycles and subsequent chemical systems that must have developed and flourished while objects resembling organisms appeared.” Morowitz, Harold J. 1992. Beginnings of Cellular Life. Yale UP. p. 54.


“Roughly speaking, an attracting set for a dynamical system is a closed subset A of its phase space such that for ‘many’ choices of initial point the system will evolve towards A.” Milnor, John W. 2006. “Attractor”. Scholarpedia. 1(11):1815.


“Definition. A closed subset A ⊂ M will be called an attractor if it satisfies two conditions:

“(1) the realm of attraction ς(A), consisting of all points x∈M for which ω(x) ⊂ A, must have strictly positive measure; and
“(2) there is no strictly smaller closed set A’ ⊂ A so that ς(A’) coincide with ς(A) up to a set of measure zero.

“The first condition says that there is some positive possibility that a randomly chosen point will be attracted to A, and the second says that every part of A plays an essential role.

“Note. In the literature, the set ς(A) is usually called the ‘basin of attraction’ if it is an open set, and the ‘stable manifold’ if it is a lower dimensional smooth manifold.” Milnor, John. 1985. “On the Concept of Attractor.” Communications in Mathematical Physics. 99:177-195. pp. 179-180.


“Keeping on with the attempt to characterize types of broken symmetry which occur in living things, I find that at least one further phenomenon seems to be identifiable and either universal or remarkably common, namely, ordering (regularity or periodicity) in the time dimension. A number of theories of life processes have appeared in which regular pulsing in time plays an important role: theories of development, of growth and growth limitation, and of the memory. Temporal regularity is very commonly observed in living objects. It plays at least two kinds of roles. First, most methods of extracting energy from the environment in order to set up a continuing, quasi-stable process involve time-periodic machines, such as oscillators and generators, and the processes of life work in the same way. Second, temporal regularity is a means of handling information, similar to information-bearing spatial regularity [like DNA where differences appear in “information-bearing crystallinity”]. Human spoken language is an example, and it is noteworthy that all computing machines use temporal pulsing. A possible third role is suggested in some of the theories mentioned above: the use of phase relationships of temporal pulses to handle information and control the growth and development of cells and organisms.” Anderson, P.W. 1972. “More is Different.” Science. 177(4047):393-6. pp. 395-6.


“... we have yet to recover from that [arrogance] of some molecular biologists, who seem determined to try to reduce everything about the human organism to ‘only’ chemistry, from the common cold and all mental disease to the religious instinct. Surely there are more levels of organization between human ethology and DNA than there are between DNA and quantum electrodynamics, and each level can require a whole new conceptual structure.” Anderson, P.W. 1972. “More is Different.” Science. 177(4047):393-6. p. 396.


“Temporal constraints are at the heart of algorithms and protocols. Constraints themselves can concatenate: the activation of one constraint can become conditional upon the occurrence of an earlier one, for example. The general logic can be characterized as follows: given that X occurred, Y becomes necessary, impossible, or more or less likely. Given that X and Y have occurred in sequence, Z becomes overwhelmingly likely. Thinking of suffixes like -TION when playing hangman helps. Given -TIO, N become overwhelmingly likely.” Juarrero, Alicia. 2023. Context Changes Everything: How Constraints Create Coherence. MIT Press. p. 43.


“In the past years, the amount of research on active matter has grown extremely rapidly, a fact that is reflected in particular by the existence of more than 1000 reviews on this topic.” Te Vrugt, Michael & Raphael Wittkowski. 2025. “Metareview: a survey of active matter reviews.” The European Physical Journal E. 48:12. 10.1140/epje/s10189-024-00466-z. p. 1.


“There are many biological examples for microswimmers (bacteria, algae, sperm, ...), but there also exist many artificial variants.” Te Vrugt, Michael & Raphael Wittkowski. 2025. “Metareview: a survey of active matter reviews.” The European Physical Journal E. 48:12. 10.1140/epje/s10189-024-00466-z. p. 7.


“From a physical point of view, the cytoskeleton can be viewed as an active gel, which is a viscoelastic material consisting of polar filaments that is in a nonequilibrium state. Thereby, the study of the cytoskeleton links cell biology with polymer and active matter science.” Te Vrugt, Michael & Raphael Wittkowski. 2025. “Metareview: a survey of active matter reviews.” The European Physical Journal E. 48:12. 10.1140/epje/s10189-024-00466-z. p. 8.


“A molecular motor is a molecular machine in which the change of position of the components exerts an influence on a system. There are many biological examples for this, in particular motor proteins such as kinesin, dynein, and myosin. These move along cellular filaments in order to perform certain biological functions.” Te Vrugt, Michael & Raphael Wittkowski. 2025. “Metareview: a survey of active matter reviews.” The European Physical Journal E. 48:12. 10.1140/epje/s10189-024-00466-z. p. 9.


“... a molecule might undergo conformational changes as a bound molecule of adenosine triphosphate (ATP) loses a phosphate group through hydrolysis and the replacement of adenosine diphosphate (ADP) by ATP restores the original conformation. In presence of an excess of ATP, this will lead to a cycling of the molecule between the two conformations.” Fang, Xiaona, Karsten Kruse, Ting Lu & Jin Wang. 2019. “Nonequilibrium physics in biology.” Rev. Mod. Phys. 91:045004. 10.1103/revmodphys.91.045004. [Unpublished manuscript] p. 3.


“The fields of condensed matter physics and materials science study the physical properties that emerge when objects (e.g., atoms, molecules, grains of sand, or soap bubbles) are placed in sufficiently close proximity, such that interactions between them cannot be ignored. Interatomic or intermolecular interactions give rise to emergent properties that are not seen in isolated species.... These emergent properties, such as conductivity, elasticity, and viscosity, enable us to predict the behavior of a collection of objects in these condensed phases.” Gardel, Margaret L. 2012. “Living matter–nexus of physics and biology in the 21st century.” Molecular Biology of the Cell. Vol. 23. 10.1091/mbc.E12-05-0353. p. 4166.


“Just consider how complicated physical materials would be if we did not have the appropriate parameters to describe the macroscopic responses and had instead became obsessed about knowing the details of all the interactions between underlying atoms and molecules?” Gardel, Margaret L. 2012. “Living matter–nexus of physics and biology in the 21st century.” Molecular Biology of the Cell. Vol. 23. 10.1091/mbc.E12-05-0353. p. 4166.


“Out of the proteins building the physical structure of the cell, actin is arguably the most important one. A small protein of only 42 kDa molecular weight, actin appeared very early in evolution and afterwards did not change its structure much, so that many other proteins could evolve around it. Its main feature is that monomeric actin (globular or G-actin) can readily assemble into polar filaments (filamentous or F-actin), which have two biochemically and structurally distinct ends. These filaments in turn can form different superstructures in cells, including branched networks, cross-linked meshworks, cross-linked bundles, and contractile bundles. While actin is essential in all kingdoms of life, it is most prominent in animal cells, where the actin cytoskeleton is the primary determinant of cell shape, mechanics, division, and migration.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] pp.1-2.


“Actin filaments are controlled in cells by more than one hundred proteins directly binding to them (actin-binding proteins, or ABPs)”.... Actin and also many of the ABPs require the energy source ATP for their proper function. Therefore, actin-based materials have to be considered as being active. In particular, each actin monomer has a binding site for ATP, and an actin filament grows mainly at its plus end (also known as the barbed end) by binding ATP-actin. After hydrolysis by the actin in the filament, the minus end (also known as the pointed end), is characterized by a predominance of ADP-actin, and this marks it for disassembly. The combination of association at the barbed end and dissociation at the pointed end leads to the concept of living or treadmilling polymers, which when anchored to its environment can actively move through space and push against obstacles while keeping its length fixed.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] p. 2.


“Another important example of actin-associated and ATP-driven activity is the action of myosin II molecular motors, which bind to actin filaments of opposing polarity and slide them relative to each other to achieve contraction.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] p. 2.


“The proteins that have evolved around actin include not only those that directly bind to actin, but also a large range of signaling molecules that affect several of these processes at once, most importantly the small GTPases from the Rho family. This is similar to the control of gene expression, where different genes are switched on together by one operon.... Rho not only activates formins that recruit new actin monomers to the barbed ends, but it also suppresses their disassembly at the pointed ends, assembles and activates myosin II minifilaments that contract the resulting actin bundles, and inactivates myosin phosphatase.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] p. 3.


“By growing with their barbed ends against obstacles, actin filaments can convert chemical energy into protrusion forces. This is essential for animal cells that have to push against the membrane during spreading and migration, which they do with a dense network of actin filaments called the lamellipodium. Pushing forces created by branching actin networks are also used to engulf foreign objects during phagocytosis and to push budding vesicles inside cells during endocytosis and during cytoplasmic streaming. They are further exploited by certain parasitic bacteria and viruses that nucleate actin comet tails from their surfaces to push themselves forward in the cytoplasm of their host cells.... For this system to work, it is essential that new monomers can be added at the barbed end even in close proximity to the obstacle. The main mechanism for this is thermal fluctuations, in particular of the plasma membrane away from the growing actin gels, as described by the different variants of the Brownian ratchet model.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] p. 5.


“As individual myosin II motors remain bound to actin filaments only for a small fraction of their ATP-hydrolysis cycle, they are incapable of generating appreciable mechanical forces on F-actin as single molecules. Force generation therefore involves myosin motor assembly into bipolar filament structures, called myosin minifilaments, which in nonmuscle animal cells are composed of around 30 myosin molecules. As myosin minifilaments translocate along actin filaments, they generate stresses via antiparallel sliding of actin filaments. In cells, these stresses are contractile and drive shape changes at the scales of organelles, cells, and tissues for executing diverse physiological functions including cell migration and cell division as well as tissue regeneration and morphogenesis.” Banerjee, Shiladitya, Margaret L. Gardel & Ulrich S. Schwarz. 2020. “The Actin Cytoskeleton as an Active Adaptive Material.” Annu Rev Condens Matter Phys. 11(1): 421-439. 10.1146/annurev-conmatphys-031218-013231. [author manuscript] [3] p. 6.


“Today, all eukaryotic cells are known to contain a cytoskeleton, comprising a network of dynamic filamentous protein polymers that collaborate with a diverse set of binding proteins and molecular motors to form nature’s most remarkable active material. Even bacteria, thought to have no cytoskeleton at the turn of the millennium, are now known to contain a diverse set of proteins capable of forming structural filaments.” Fletcher, Daniel A. & Phillip L. Geissler. 2009. “Active Biological Materials.” Annu Rev Phys Chem. 60:469-486. 10.1146/annurev.physchem.040808.090304. [author manuscript] [3] p. 2.


“We may know which proteins are required for a process and how they are arranged in a cell, but we do not know how those proteins became arranged into that particular configuration nor how that configuration gives rise to the physical behavior under study. It thus remains unclear how the cytoskeleton drives complicated movements such as those involved in endocytosis, phagocytosis, and crawling motility. The challenge now lies not in identifying the molecular components or their individual function, but rather in integrating the parts and their biochemical, mechanical, and energetic behaviors into a comprehensive understanding of cell movements and shape changes.” Fletcher, Daniel A. & Phillip L. Geissler. 2009. “Active Biological Materials.” Annu Rev Phys Chem. 60:469-486. 10.1146/annurev.physchem.040808.090304. [author manuscript] [3] p. 2.


“Some of the most important features of biological structures, such as protrusive actin networks and contractile stress fibers, involve transitions between distinct structural states.” Fletcher, Daniel A. & Phillip L. Geissler. 2009. “Active Biological Materials.” Annu Rev Phys Chem. 60:469-486. 10.1146/annurev.physchem.040808.090304. [author manuscript] [3] p. 13.


“The cell has long been considered as a viscoelastic material. When subjected to high-frequency forces or deformations over a relatively short timescale, the cytoplasm behaves as an elastic solid; under low-frequency or relatively slow loadings, the cytoplasm instead relaxes and thus behaves as viscous fluid. It is known that cell viscoelastic behavior has wide implications in a variety of physiological and pathological processes such as cell migration, embryonic development, and cancer invasion.” Li, Yiwei, Wenhui Tang & Ming Guo. 2021. “The cell as matter: Connecting molecular biology to cellular functions.” Matter. 4:1863-1891. 10.1016/j.matt.2021.03.013. [3] p. 1864.


“The cytoskeleton of mammalian cells is composed of three major biopolymer networks, forming an interpenetrating network. Both filamentous actin (F-actin) and microtubules are dynamic networks that are constantly undergoing reorganization and repolymerization. Disrupting F-actin or microtubules in mammalian cells leads to cell softening. In contrast, cytoskeletal intermediate filaments have a much slower turnover process and thus have been considered as a major structural component maintaining cell mechanical integrity.” Li, Yiwei, Wenhui Tang & Ming Guo. 2021. “The cell as matter: Connecting molecular biology to cellular functions.” Matter. 4:1863-1891. 10.1016/j.matt.2021.03.013. [3] p. 1865.


“Meanwhile, biophysical studies reveal that cell mechanics are also regulated by their extracellular mechanical cues, including shear force, stretch, and compression. These previous works suggest that regulations of cellular material properties and biochemistry are in parallel, in response to the mechanical cues in the microenvironment. In this section, we review the emerging concepts from a material perspective that bridge the gap between cell mechanics regulation and mechanotransduction. Instead of searching for particular receptors or sensors upstream on the cell membrane, we discuss the physical properties of the cell interior as a regulator altering the equilibrium and rate of intracellular biochemistry on the molecular level. This provides us a new perspective from which to understand those biological consequences of mechanical cues that lack identified upstream receptors/sensors, and to construct regulatory loops (both forward and backward) between cellular mechanical/physical properties and cellular signaling for developing multicellular tissue systems.” Li, Yiwei, Wenhui Tang & Ming Guo. 2021. “The cell as matter: Connecting molecular biology to cellular functions.” Matter. 4:1863-1891. 10.1016/j.matt.2021.03.013. [3] p. 1867.


“Over the last few decades, molecular crowding has been confirmed to be a critical factor affecting both the rate and equilibrium of biochemistry, in both in vitro tube reactions and synthetic cell-free systems. More recently, molecular crowding of the cellular interior has been shown to regulate cell mechanics.... Furthermore, recent studies demonstrate that cell mechanics and intracellular molecular crowding can be tuned by a variety of physical cues, such as stretch, compression, osmotic pressure, confinement, substrate stiffness, and cell spreading.” Li, Yiwei, Wenhui Tang & Ming Guo. 2021. “The cell as matter: Connecting molecular biology to cellular functions.” Matter. 4:1863-1891. 10.1016/j.matt.2021.03.013. [3] p. 1868.


“At present, most questions about how things work in biological systems are answered by experimental exploration. The situation in physics is very different, where theory and experiment are more equal partners.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] p. 1.


“I hope to convince you that theory has had important successes, shaping how we think about life today, and that this is true despite a widespread impression to the contrary. Turning from the past to the present and future, I will argue this is an auspicious time: theory is having a real impact on experiment, related theoretical ideas are emerging in very different biological contexts, and we can see hints of ideas that have the power to unify and deepen our understanding of diverse phenomena.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] p. 1.


“Thus it is essential to point out that theory already has made contributions, and big ones at that. Many of the foundational papers in what we now call molecular biology were unambiguously theoretical papers, and the example of Rayleigh [who around 1900 predicted that animal hearing could perceive phase differences in sound reception using the length difference between two ears to be able to perceive sound direction, which was later confirmed] points to a theoretical tradition that reaches much farther back into the history of interactions between physics and biology. But these examples also have problems.

“First, in the case of Watson and Crick [who used the rules of chemical bonding to conjecture how a long molecule could have pairs in many permutations allowing equal energy information to arise], it appears that all the theorizing was in words and not in equations, and so what’s written in these papers doesn’t look like theory in the sense that we use the term in physics....

“Second, this was theorizing in which the relevant principles were at the level of molecular structure. This is a level at which, I think, nobody would doubt that physical principles are relevant for biology. But it isn’t clear how you would ever get from that level up to the level that concerns many of us today, the level of ‘systems,’ whether we mean systems inside one cell, in a developing embryo, in a network of neurons in the brain, or in a group of organisms behaving cooperatively....

“Finally, there is a question about the connection between theory and experiment. By the time of Rayleigh’s work, there was a well established tradition of trying to make quantitative connections between our perceptions and the properties of the physical signals at the input to our sense organs; this subject of ‘psychophysics’ would grow and deepen throughout the twentieth century. The fundamental prediction made by Watson and Crick was about the structure of a molecule, and the decades following their work would see the emergence of X-ray diffraction experiments with atomic resolution, even in large biological structures. Thus, in both our examples, the theory pointed toward experiments that could be done quantitatively....” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] pp. 3-4.


“To summarize, the classical examples are inspiring, but the challenge for theory in our time is (at least) three fold. First, we have to identify principles that organize our thinking at a systems level. Second, we have to express these principles in mathematical terms. Third, if we expect our mathematical theories to make quantitative predictions, we have to push our experimentalist friends to expand the range of life’s phenomena that are accessible to correspondingly quantitative measurements.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] p. 4.


“By varying the copy numbers of just two types of channel [in neurons], we can produce cells that are silent, cells that fire single, isolated action spikes like the ticks of a clock, cells that generate bursts with two or three spikes per burst, and more. Along one direction we can see transitions through three qualitatively distinct behaviors when the number of copies of one channel is changed by just 10-20%.... This means that our problem in fitting models can be identified with the cell’s problem in controlling it’s [sic] own behavior: how does a cell manage to sit in the middle of one functional region, and not wander off into other regions?

“What Abbott and colleagues proposed was that cells set the number of channels by monitoring what the cell as a whole is doing. For example, a cell could monitor it’s internal calcium concentration. When the voltage across the membrane changes, as during an action potential, calcium channels open and close, calcium flows in, and this provides a monitor of electrical activity. The calcium concentration is known to feed into many bio-chemical pathways inside the cell, and we can imagine that some of these could regulate either the expression of the channels or their insertion into the membrane. Mechanisms of this type allow cells to stabilize the very different behaviors seen in Fig 2 [controlling numbers of 2 types of voltage channels shows regions of distinct firing patterns], essentially because the map of calcium concentration vs channel copy numbers neatly overlays the map of spiking rhythms....

“These ideas were quickly confirmed.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] pp. 5-6; reference: Goldman, M.S., J. Golowasch, E. Marder & L.F. Abbott. 2001. “Global structure, robustness, and modulation of neuronal models.” J. Neurosci. 21:5229-5238.


“Our most complete theories of the natural world certainly have parameters, but there is a sense that if we are focused too much on these parameters then we are doing something wrong. If parameters proliferate, we take this as a sign that we are missing some additional level of unification that could relate these many parameters to one another; if our qualitative explanation of phenomena hinges on precise quantitative adjustment of parameters, then we search for the hidden dynamics that could make this apparent fine tuning happen more naturally. Some of the greatest triumphs of modern theoretical physics are nearly free from parameters–the BCS theory of superconductivity, the renormalization group theory of critical phenomena, the theory of the fractional quantum Hall effect, and more. Importantly, these examples refer not to a rarefied world of interactions among small numbers of elementary particles, but rather to the properties of real, macroscopic materials, with all their chemical complexities.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] p. 6.


“How can we reconcile the parameter aversion of theoretical physicists with the explosion of parameters that arise in a realistic approach to biological systems? Much of what our community is doing, I think, can be understood as a reaction to this problem. There are several approaches. [Note, below in text: “One possibility, surely, is that the multitude of parameters is a fact of life, and somehow irreducible, in which case we need to give up on our search for a physicist’s understanding. I’ll discard this is too pessimistic.”] First, it might be that the parameters are just a distraction, and that the meaningful functional behaviors of biological systems emerge as generic or ‘robust’ properties of our models, independent of precise parameter settings. A second, approximately opposite view is that the forces of evolution have been strong enough to select particular, non-generic parameter values, thus giving the appearance of fine tuning; if we can identify the selection principle, we then have a path to building a theory without free parameters. Finally, we might hope that parameter independence emerges in biological systems much as it does for inanimate materials, with something like the renormalization group telling us that macroscopic behaviors which matter for the organism can be independent of (highly parameterized) microscopic details.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] p. 6.


“Despite these many examples [of theory being used to advance understanding in biology], there is a persistent notion that biology has developed without significant theoretical input. This is reinforced by what amounts to revisionist history in the teaching of biology. If biology is presented to undergraduate students as the science they can do even if they don’t like math, then when it comes time to teach them about the foundations of molecular and cellular neuroscience, one simply cannot write down the Hodgkin-Huxley equations and expect the students to understand what is going on.... The message, I think, is that mathematical analysis–not to speak of theory–is merely technical.... If the community insists that what is ‘biologically relevant’ must always be translated into words, then the search for mathematical description can never be central to the practice of biology. In a dissent from cheerful interdisciplinarity, I believe it is essential that the physics community provide a home for the theoretical physics of biological systems.” Bialek, William. 2018. “Perspectives on theory at the interface of physics and biology.” Reports on Progress in Physics. 81:012601. [Submitted manuscript] [5] pp. 14-15.


“TCM [traditional Chinese medicine] can be considered as an ancient and classical paradigm of systems biology. In TCM, diagnosis and medication are based on ‘Syndrome’ (‘ZHENG’ in Chinese Mandarin), which can be regarded as a profile of symptom combination, or clinical phenotypes, such as Cold or Hot Syndrome, and ‘Hot medication curing Cold Syndrome’ is a standard therapeutic guide line.” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 613.


“‘The myriad things have their backs to the Yin and face the Yang. Through the interaction of the Yin and Yang, a new harmony is created’, said Laozi in his great work ‘Dao De Jing’ (Taoism), a philosopher more than 2000 years ago. In TCM, disease is regarded as aberrancy from the balanced state of body and various pathogenic factors, including both endogenetic and exogenetic, are categorized as Cold, Hot or other typical patterns.” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 614.


“This methodology [systems biology] still needs improving as the systematic interaction of different molecules at the micro level is not equivalent to the function of a cell or an organ, no mentioning the overall action of body at the macro level, although current systems biology did enlarge our vision of understanding complex biological systems as well as complex diseases.” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 615.


“The neuro-endocrine-immune (NEI) network is a paradigmatic system in Western medicine.” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 615.


“These results [gene expression correlations between TCM diagnoses and identified Western diseases] not only validate our proposal that NEI system bridges transcriptomic information at the micro level and TCM phenotypic information at the macro level, but also reveal that the abnormal communication between NEI Cold and Hot gene groups leads to TCM Cold Syndrome in all probability....” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 616.


“Complex diseases originate from intertwined body-environment interactions and present as miscellaneous phenotypes.” Ma, Tao, Conge Tan, Hui Zhang, Miqu Wang, Weijun Ding & Shao Li. 2010. “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.” Molecular BioSystems. 6:613-619. [3] p. 618.


“In recent years, systems biology research on TCM [traditional Chinese medicine] syndromes has gradually become the focus of TCM research, including syndrome differentiation and functional research using systems biology methodologies such as proteomics, transcriptomics, and metabolomics.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2939.


“TCM syndrome (also called Zheng) is a temporary state that can be assessed by inspection, auscultation, olfaction, interrogation, and palpation. Inspecting the tongue, especially the patterns on the tongue’s surface is the common task in TCM to get information about the viscera. Auscultation includes listening to the sound and pitch of the voice, respiration, cough, and hiccups. Olfaction refers to smelling any abnormal odor that the patient may have from the breath, perspiration, urine, and other sources. Interrogation includes asking the patient about the onset and change of the disease. Palpation refers to pulse taking and touching different parts of the body.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2940.


“The eight Principles describe the four pairs of fundamental qualities of a disease: Yin/Yang, exterior/interior, cold/heat, and deficiency/excess.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2940.


“The Qi-blood circulation theory is another basic TCM theory. Qi is often described as refined nutritious substance that maintains life activities. In addition, Qi refers to functions of organs, such as lung-Qi and liver-Qi. Disease may occur when the flow of Qi-blood is disturbed by pathogens.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2940.


“Thus, the patients can be classified clinically into different TCM syndromes based on the condition of exterior/interior, cold/heat, deficiency/excess, Yin/Yang of the disease, and the condition of Qi-blood. The aim of treatment in TCM is to suppress the cause of the disease and restore the balance between Yin-Yang and Qi-blood.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2940.


“The specific composition, expression level, and correlation between different proteins are closely related to the TCM syndrome. Therefore, it is significant to analyze the dynamic changes of proteomic components and expression levels by proteomics techniques for the study of TCM syndrome classificaiton.” Jiang, Ting-Ting & Ji-Cheng Li. 2019. “Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.” Annat Rec. 306:2939-2944. 10.1002/ar.24354. [3] p. 2941.


“But is it reasonable to believe that intergeneration genetic changes alone can code for an ability to balance so many competing considerations? Or do we conclude that perhaps the genes need a little help? If so, we must presume that genes code less for specific responses than for plasticity. But this begs the question: by what mechanism is plasticity utilized to do the ‘right thing”? This concern was voiced by Waddington as far back as the 1950s. Waddington also expressed doubts that specifically directed genetic instructions were compatable [sic] with the fact that a particular genotype might correspond to several phenotypes or that ‘phenotypically almost identical (individuals) could contain wildly different genotypes.’ Finally, he noted that no adaptive-genetic mechanism had been proposed to account simultaneously both for plasticity and the rigidity of canalization. These concerns remain with us today.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 718.


“In this paper we argue that the nature of living organisms, as complex, acquisitive systems, leads inevitably to appropriate organismal responses to the external world quite independently of genetic changes arising from natural selection. For reasons that will become clear, we refer to this assertion as the ‘attractor hypothesis.’” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 718.


“Consider a minimal subsystem, that is, one that is no longer reasonably subdivisible, e.g., the glycolytic cycle. Such a system, despite its least complex status, comprises a huge number of constituent parts (substrates, enzymes, etc.) It is characterized also by a vast number of feedback loops and an input of energy, some of which is utilized to do work, the rest of which is dissipated. The dynamic of any such subsystem, providing its energy supply is not cut off, can be described as a trajectory in its corresponding phase space, a path that moves about until it encounters a region of that space where feedback processes capture it. Once it enters such a ‘domain of attraction’ it remains there (unless shaken out by external forces), and henceforth follows a path referred to as an ‘attractor.’” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 718.


“But if an organism lives, its dynamic must, by definition, have reached some nonstatic attractor.

“Complex systems (such as organisms) can display many attractors, and as complexity rises, the number of alternative attractors generally increases. The glycolytic cycle, for example, exhibits several attractors. And by the time the myriad loosely connected subprocesses in a whole organism are jointly considered, the number of alternative attractors must be enormous.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 718.


“Note first that biological systems are distinguished from the complex systems studied by chemists and physicists in an important way. The latter are forced systems, maintained by an externally imposed input of energy and/or nutrients. Biological systems are not forced; the environment does not shove food into the mouths of its denizens. Rather organisms are acquisitive systems, garnering resources to satisfy their own needs. Passive diffusion may, for some nutrients in some circumstances fulfill input and catabolic (energy) needs. But it is difficult to see how organisms utilizing this means only could compete successfully with others exhibiting effectively autocatalytic input and catabolic mechanisms.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 719.


“It is the juxtaposition of positive (acquisitive) and negative feedback control that defines attractors and thus leads to the formation of homeostasis in biological organisms.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 719.


“The term ‘adapt’ has evolutionary overtones. Nevertheless, for lack of a better word, we shall henceforth use it, generically, to mean ‘appropriately’ respond. Adaptation, in this sense, therefore, occurs repeatedly in response to environmental changes over short periods of time within a generation. We can now restate the ‘attractor hypothesis’ in a slightly different way. Adaptation can occur via movement among attractors, as an inevitable consequence of organisms’ complexity and acquisitive nature, quite independently of genomic change. As opposed to adaptation in the evolutionary sense, it refers specifically to the balancing of intake and production to demand, and to the energy efficiency with which such balancing occurs.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 720.


“As noted above, intermediate levels of stress, enough to jolt some trajectories from their domains of attraction, but not enough to produce disruptive cascades, should enhance the kinetic ‘search’ of the phase space. A little stress may actually lead to more rapid and more complete adaptation. Supporting this contention is a sizeable literature. The enhanced response, known as hormesis, is well documented for a wide array of chemicals.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 720.


“To exemplify attractors with differing time rates, and also the interaction among hierarchical levels of attractors, consider a human’s response to lowered oxygen availability (a trip to higher altitude, for example). Define several sub(phase)spaces, one of which describes the domains for breathing, heart rates, and activity level. The positions of attractors in this subspace depend on environmental milieu as determined not only by air pressure outside the body but also blood volume, hemoglobin count, active status of hemoglobin-affecting genes and chest girth (among others). The most rapid response to increased altitude is a kinetic movement within this phase space toward an attractor describing increased breathing and heart rates and decreased activity. We refer to such rapid response attractors as ‘superficial.’ A second sub(phase)space describes the domains of blood volume and hemoglobin levels. The position of attractors in this subspace are influenced by breathing and heart rate, air pressure, gene status, and chest girth. As response occurs in subspace 1, and continuing after an appropriate (energetically adaptive) attractor is reached in that space, movement occurs also toward a new attractor in the second space, an attractor characterized by increased blood volume and hematocrit. Similarly, at a still slower pace, alterations in gene expression for hemoglobin type are moving the organism toward a new attractor in a third subspace. Finally, over generations, still another subspace attractor (for increased chest girth) is favored by natural selection. Slowly reached attractors we refer to as ‘deep.’ As noted, all these subspaces define the domains of processes at least loosely interconnected to each other. Thus, acquiring the first, superficial attractor somewhat alleviates pressure for moving toward the second, and so on. As a result, with a single or infrequent exposures to an environmental change, only the first, superficial adaptation might occur... or perhaps the first and second, but not deeper adaptations. Similarly, as the deeper processes reach more efficient attractors, there is less impetus for rapid response. Thus physical conditioning lessens the heart rate response of exercise, and repeated stimuli lead to habituation.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 721.


“A cascade in genetic expression is useful to the organism in at least two ways. First, it provides new attractors from which the organism can choose. Shoposhnikov showed that the explosive increase in phenotypic variability in aphids following transplantation to unsuitable plant hosts was followed by canalization of adaptive new phenotypes at the expense of old ones, which gradually disappeared. Second, by disrupting chemical structures and repair mechanisms, stress also may bring about an increase in the (random) mutation rate.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 722; reference: Shoposhnikov, G. 1965. Enthomol. Rev. 44:3-25.


“By virtue of nongenetic adaptation, the production of increased diversity under stress, and the possible prescreening of mutants, natural selection need not choose among randomly generated phenotypes. The attractor hypothesis provides, instead, a ready set of preadapted alternatives (for efficient balancing of supply and demand if not for fitness, per se).” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 723.


“The evolution of plasticity becomes a nonproblem in light of the attractor hypothesis. Adaptive plasticity is an unavoidable consequence of biological complexity.

“Waddington argued that the neo-Darwinian paradigm could not account simultaneously both for plasticity and canalization. The attractor hypothesis provides a solution to Waddington’s dilemma. Processes inhabiting attractors, as noted above, resist change and, when faced with sufficient pressure, jump attractors. Those on deep attractors react only slowly, those on superficial attractors more readily. Thus canalization can be explained as the resistance to jumping deep attractors, and plasticity the ability to hop among superficial ones.” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] p. 723.


“Despite this linkage [adaptive responses that are tied to selected genes that promote fitness so as not to justify attractors when changes in an organism are because of the Natural Selection of genes], several considerations permit tests of the attractor hypothesis.

“ (1) There are dynamical correlates of the hypothesis (characteristic cyclicities, saltatory shifts among different cyclicities, hysteretic responses, differential prominence of autocatalytic processes in slow versus rapid responses) that have no apparent rationale under natural selection theory. Consider that alternative physiological states exhibit characteristic signatures in the form of oscillatory frequencies and patterns. Can physiological states be changed (can we shift among attractors) by driving these signatures? The attractor hypothesis says yes, and the answer would seem outside the realm of prediction by natural selection....

“If an organism experiences continuing change in its environment, does it, as the hypothesis predicts, eventually display a saltatory change in some aspect of its phenotype? Does the change exhibit hysteresis, and is the switch accompanied by an decrease in energy dissipated per energy taken in?

“We argued that autocatalysis can be expected to evolve more often with respect to superficial than deep processes. Is this so? For example, does food intake stimulate further ingestion (to a point) and are dietary preferences, by contrast, conservative? Does fear feed on itself while learned avoidance patterns resist change? Are rapid physiological responses more likely to be self-inducing relative to slow responses?

“ (2) Under the attractor hypothesis, hormesis arises when stress jolts an organism from one attractor into a more energetically efficient attractor. This suggests that organisms ‘stuck’ in in appropriate behaviors may be cured b applying limited stress. Animals, including humans, exhibit a wide array of ‘bad habits’ that can be acquired or lost within a generation and so do not qualify as unavoidable side effects of genetically selected traits. These range from addictions to anorexia to inappropriate motor habits in dance, other athletics, and piano playing. It is highly unlikely that the former two contribute to Darwinian fitness, and the latter is probably irrelevant to fitness. In the contexts in which they developed do these behaviors represent energy-efficient alternatives? Would a little bit of stress hasten a correction of these habits? The attractor hypothesis would answer both questions affirmatively....

“ (3) Adaptation by attractor search is related to energy efficiency, adaptation by natural selection to life-time reproductive output. Thus we can look for examples of homeostatic response that satisfy one and not the other.

“ (4) Natural selection-based responses to strictly novel stimuli will not necessarily be adaptive. Scharloo wrote
adaptive phenotypic reactions only occur for variation found within the natural environment of the species.

“Not so if the attractor hypothesis is correct. Perhaps we could look at the Darwinian fitness and energetics of organismal responses to novel stimuli (such as hypergravity).

“ (5) Under the attractor hypothesis, the tightness of the genotype-phenotype link can be expected to loosen when genetic change comes via induced mutations rather than selection. Similarly, the penetrance of genes should drop when genetic background is altered. Do these predictions match observations?

“A change in environment, or an enforced change in cytoplasmic chemistry (perhaps via drug delivery), coming as it would without corresponding change in the genome, should disrupt the genotype-phenotype linkage. Does heritability of specific traits decline under stress?” Emlen, John M., D. Carl Freeman, April Mills & John H. Graham. 1998. “How organisms do the right thing: The attractor hypothesis.” Chaos. 8(3):717-726. [6] pp. 723-4.


“Calcite microbialites are complex assemblages of organisms occupying a mineral superstructure that they build, analogous to corals and stromatolites. We suggest that microbialites are a colonial intermediate between the exclusively prokaryotic colonial precursors of the stromatolites and the multicellular organismic aggregates that gave rise to coral reefs.” Schulze-Makuch, Dirk, Bernard Laval & Louis N. Irwin. 2012. “The Rise of Complexity: Pavilion Lake Microbialites Suggest a Pathway toward Macroorganismic Communities.” Hypotheses in the Life Sciences. 2(2):55-59. [2] p. 55.


“Compared with other major transitions in evolution that occurred just once (for example, the origin of eukaryotes), multicellularity has evolved repeatedly.... Here we use experimental evolution to directly examine the first steps in this transition [to multicellularity] using the unicellular alga Chlamydomonas reinhardtii. This species is uniquely suited to such an investigation, as it has never had a multicellular ancestor and is closely related to the volvocine algae, a clade in which the historical order of multicellular adaptations has been inferred.” Ratcliff, William C., Matthew D. Herron, Kathryn Howell, Jennifer T. Pentz, Frank Rosenzweig & Michael Travisano. 2013. “Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii.” Nature Communications. 4:2742. 10.1038/ncomms3742. [3] p. 2.


“A hallmark feature of complex multicellullarity is a two-stage life cycle in which multicellular individuals develop from a single cell.” Ratcliff, William C., Matthew D. Herron, Kathryn Howell, Jennifer T. Pentz, Frank Rosenzweig & Michael Travisano. 2013. “Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii.” Nature Communications. 4:2742. 10.1038/ncomms3742. [3] p. 2.


“To select for cluster formation, the first step in the transition to multicellularity, 10 populations were subjected to strong selection for rapid settling through liquid medium. Briefly, we performed the selection by centrifuging 1ml of each population at 100g for 5s and then transferring only the bottom 100 μl to fresh medium.” Ratcliff, William C., Matthew D. Herron, Kathryn Howell, Jennifer T. Pentz, Frank Rosenzweig & Michael Travisano. 2013. “Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii.” Nature Communications. 4:2742. 10.1038/ncomms3742. [3] p. 2.


“Using experimental evolution, we find that simple algal multicellularity can arise in as little as 219 days in a species that has never had a multicellular ancestor....

“Despite strong selection, substantial evolutionary responses to selection occurred in just 1 of 10 populations under settling selection within 219 days.” Ratcliff, William C., Matthew D. Herron, Kathryn Howell, Jennifer T. Pentz, Frank Rosenzweig & Michael Travisano. 2013. “Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii.” Nature Communications. 4:2742. 10.1038/ncomms3742. [3] p. 5.


“Even though multicellularity has evolved dozens of times in the last 3.5 billion years, this transition is still rare (occurring, most recently, ~200 MYA in the brown and the volvocine algae). Although it is possible that a control population could evolve multicellularity in a few months, this would be an unexpected outcome given that hundreds of independent lineages in the genus Chlamydomonas (including C. reinhardtii) have failed to do so over hundreds of millions of years. More broadly, our finding that simple multicellularity can evolve in less than a year in both Chlamydomonas and Sacchromyces suggests that genetic barriers (for example, few mutational paths to multicellularity) may be less restrictive than ecological barriers, namely a lack of persistent selective advantages for cellular clusters.” Ratcliff, William C., Matthew D. Herron, Kathryn Howell, Jennifer T. Pentz, Frank Rosenzweig & Michael Travisano. 2013. “Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii.” Nature Communications. 4:2742. 10.1038/ncomms3742. [3] p. 5.


“Regulation provides a clear example of a biological selective process that operates at the level of the dynamics of individual organisms, and that generates a distinctive form of biological teleology.” Gonzalez de Prado, Javier & Cristian Saborido. 2025. “Biological Purposes Beyond Natural Selection: Self-Regulation as a Source of Teleology.” Erkenntnis. 90:217-236. 10.1007/s10670-023-00695-2. [3] p. 218.


“Given the prominence of Darwinian evolution in biological research, it is understandable that biological selection tends to be identified with natural selection, and therefore with differential reproduction. However, biological selection can take place via other forms of reinforcement. For instance, Garson describes neural selection as a case of biological selection involving differential retention without differential reproduction.” Gonzalez de Prado, Javier & Cristian Saborido. 2025. “Biological Purposes Beyond Natural Selection: Self-Regulation as a Source of Teleology.” Erkenntnis. 90:217-236. 10.1007/s10670-023-00695-2. [3] p. 221; reference: Garson, J. 2019. What biological functions are and why they matter. Cambridge UP.


“Our proposal is that biological regulation constitutes a selective process....

Once it is granted that biological regulation is a selective process, it follows from selected-effects theories that regulation introduces teleological standards.” Gonzalez de Prado, Javier & Cristian Saborido. 2025. “Biological Purposes Beyond Natural Selection: Self-Regulation as a Source of Teleology.” Erkenntnis. 90:217-236. 10.1007/s10670-023-00695-2. [3] pp. 229, 230.


“For biomolecules and complexes in the early stage of prebiotic evolution, their persistence was not coupled with (cellular) metabolism or replication: the coupling of persistence, metabolism, and replication was a product of pre-Darwinian evolution.” Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.” Biological Theory. 15:175-9. 10.1007/s13752-020-00359-2. [5] p. 176.


“Natural selection can operate without replication or even metabolism (at least not cellular metabolism), as long as different molecules, complexes, and vesicles have different persistence rate within a system.” Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.” Biological Theory. 15:175-9. 10.1007/s13752-020-00359-2. [5] p. 176.


“Four major non-Darwinian selection mechanisms, which most likely had appeared in the following order, had worked together in the process leading to FUCAs.

“(a) The first pre-Darwinian selection mechanism is mostly chemical. It operates upon molecules and selects not only their chemical properties as monomers but also their capacities for forming polymers and complexes. Here, the key yardsticks of ‘fitness’ include steady supply from abiotic synthesis (i.e., availability), kinetic and thermochemical stability or persistence, solubility, polymerization, and stereochemical ‘mutualism’ for forming larger complexes.

“(b) The second pre-Darwinian selection mechanism is both chemical and physical. It selects the different capacities of different bioorganic molecules and complexes to interact with each other, and in turn, whether their interactions confer new (or emergent) life-facilitating properties, structural and functional. Among the various possible interactions, two were perhaps central: (1) alpha-helix forming peptides, perhaps (poly-)nucleotides too, that can not only interact with and stabilize vesicles but also make vesicles selectively permeable; and (2) peptides and RNAs that can not only interact with each other but also lead to new or enhanced properties (e.g., more efficient and reliable) via their interactions.

“(c) The third pre-Darwinian selection mechanism selects the different capacities of different vesicles (1) to absorb biomolecules and components via simple absorption and breaking-and-re-encapsulation and (2) to engulf (or acquire) via proto-endocytosis and to merge (or fuse) via proto-endosymbiosis or similar processes. Vesicles with superior capacities in both absorption and merger-acquisition will enjoy advantages over those with less effective capacities, in terms of persistence, variation, and evolvability. For both processes, a wet-and-dry cycle might have played a key role. Notably, absorption, acquisition, and fusion entail extensive ‘horizontal biomolecule transfer’ (HBMT) rather than merely horizontal gene transfer (HGT): HMBT thus subsumes HGT. Only with HBMT could pre-Darwinian evolution draw from ‘global inventions’. HBMT was therefore the more pivotal and pervasive process than HGT, at least in the pre-Darwinian epoch....

“(d) The fourth pre-Darwinian selection mechanism operates upon vesicles that now approach protocells. among those now fairly stable vesicles, those that can (1) absorb, acquire-engulf via proto-endocytosis, and fuse-merge via proto-endosymbiosis, or processes similar to them, (2) produce primitive metabolism and replication, and (3) grow, divide, and stabilize will hold critical selection advantage over those that cannot. Here, the key yardstick of ‘fitness’ was persistence, absorption, growth, and division, first without and then with primitive metabolism and genetic replication.

“The central point is that FUCAs most likely did not come to exist via de novo evolution within individual protocells: this will imply that every FUCA had to evolve almost entirely independently and such a possibility would have been miracle.” Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.” Biological Theory. 15:175-9. 10.1007/s13752-020-00359-2. [5] pp. 176-7.


“Certainly, FUCAs did not come to exist via HGT alone: HBMT had to come first before HGT came into play. In fact, only through HBMT rather than HGT, at least not HGT alone, could the evolution of FUCAs be drawing useful ingredients or components from ‘global invention.’ It was only through HBMT that is underpinned by absorption, engulfing/acquisition, and merger/fusion rather than HGT alone that FUCAs came to possess both a proto-machinery of survival and a proto-machinery of replication within the same protocell....

“During the pre-Darwinian epoch that led to LUCA and long before eukaryogenesis, this mechanism of HBMT via absorption, acquisition, and fusion or processes similar to them, had thus been a far more powerful and foundational force of variation and selection than even Lynn Margulis and many of her supporters had appreciated.” Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.” Biological Theory. 15:175-9. 10.1007/s13752-020-00359-2. [5] p. 177.


“Once FUCAs came to possess both a proto-machinery of survival and a proto-machinery of replication, survival and replication began to coevolve with each other, within a vesicle....

“For this phase, a tight coupling of survival and replication might not hold any selective advantage. Indeed, the opposite might have been true: being more promiscuous means more flexibility and provides a protocell with significant advantage for survival. It is due to this key dynamics rather than HGT alone that FUCAs did not have a genealogical history, but only a physical-chemical one.

“Within the original population of FUCAs, each FUCA protocell competed against each other. After a period during which survival and replication coevolved with each other, some of the FUCAs eventually became protocells in which survival and replication are more tightly coupled and smoothly regulated. Protocells with a tighter coupling and smoother regulation of division and replication would come to enjoy an enormous advantage over those protocells without, and these protocells eventually became the LUCA.

“Along the way, many genetic elements were selected out from FUCAs and LUCA, and those genetic elements that were left out became the first batch of genetic parasites or mobile genetic elements (MGEs), and the inevitable arms race between hosts and genetic parasites was on.” Tang, Shiping. 2020. “Pre-Darwinian Evolution Before LUCA.” Biological Theory. 15:175-9. 10.1007/s13752-020-00359-2. [5] p. 177


(See also CITE_2022 for this article)
“Also, by using FUCAs in plural wheras LUCA in singular, I convey the message that FUCAs had been a commune of different (proto-) cellular lineages whereas LUCA was more likely a single cell that came to produce all the organisms on this planet.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] pp. 428-9.


“During this stage of coevolution [of RNA and amino acids and peptides], precision in RNA replication (and proto-translation) is not necessarily an advantage. Rather, during this stage of coevolution, the key was to make more RNAs and peptides without too much precision so that the structural diversity and hence the functional diversity of RNAs and peptides could increase more rapidly.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] p. 433.


“Thus, only in FUCAs did the narrower HGT replace the broader HBMT [horizontal biomolecule transfer] as the more critical force in driving evolution, although HBMT continued to operate, most dramatically in eukaryogenesis. Moreover, only in FUCAs did HGT gradually become more harmful.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] p. 434.


“The universality of the standard genetic code is best explained by the coming of amino acid/peptide-RNA interaction (and then the coevolution of amino acid/peptide-RNA interaction and the proto-translation system) very early on, long before the coming of DNA replication and DNA to RNA transcription. The possibility that the standard genetic code came to exist via initial chemical mutualism between amino acid/peptide with RNA and then the coevolution of peptide/protein and RNA is now generally accepted.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] p. 436.


“The fact that quite a few paths (e.g., pH, concentration, wet-and-dry, hot-and-cool, or even redox) can propel this process of vesicular recombination and that different vesicles containing different biomolecules have different capacities of growth (via absorption and in-taking) and division under different conditions strongly suggests that such pathways might have been powerful forces of variation and selection in the evolution of FUCAs.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] p. 438.


“The notion that FUCAs came together through HBMT based on proto-endosymbiosis and proto-endocytosis that bring together different vesicles containing different components suggests a heterotrophic origin of FUCAs and LUCA. Autotrophic life was only achieved after a long period off heterotrophic evolution.” Tang, Shiping. 2021. "The Origin(s) of Cells(s): Pre-Darwinian Evolution from FUCAs to LUCA." Journal of Molecular Evolution. 89:427-447. doi: 10.1007/s00239-021-10014-4. [7] p. 441.


“Taking modularity in the evolution of biological systems as the starting point, we contend that the coordination of the various pathways constitutes a key task that organisms have evolved to cope with modularity so that organisms can survive and reproduce under stress.” Tang, Shiping. 2024. “Coordination of Pathways in Metazonas: An Integrated Framework.” Preprints.org 10.20944/preprints202405.1996.vi. Not peer-reviewed. [3] pp. 1-2.


“The evolution of simple and then complex multicellularity, including metazoans, has been a major transition. A hallmark of this transition has been the transition from temporal differentiation (i.e., a life cycle) to spatiotemporal differentiation and integration of cell types, tissues, and organs. More complex development and differentiation in metazoans then led to the next landmark breakthrough: the spatial separation of soma and germline....

“In simple animals (e.g., planaria, sponges, Hydra), soma and germline are not so clearly differentiated: both cell types can be generated from similar or identical stem-cell like populations. In contrast, soma and germline are clearly differentiated both temporally and spatially in complex animals. By relieving soma cells of the duty of reproduction, soma cells can differentiate into all kinds of tissues and organs that fulfill many diverse functions, and thus allow organisms to adapt to variuos new niches.

“Protecting reproduction (i.e., the germline) under stress, however, remains a central task. In fact, sex itself might have originated as a stress response, often induced by damages. This holds true even in bacteria: fundamentally, sex in bacteria is a repairing system....

“In other words, life has evolved from a state of little or no conflict between survival and reproduction in unicellular organisms to a state of serious conflict between survival and reproduction in simple multicellular organisms but more so in complex metazoans. This results in a critical tradeoff: even though survivability came before replicability, organisms now can, and often need to, sacrifice some soma for the germ when under stress. In short, some soma becomes disposable to reproduction.” Tang, Shiping. 2024. “Coordination of Pathways in Metazonas: An Integrated Framework.” Preprints.org 10.20944/preprints202405.1996.vi. Not peer-reviewed. [3] p. 8.


“We have outlined a new framework for understanding the coordination network of the numerous pathways in metazoans, a topic that has received inadequate attention. We highlight five key points.

“First, an organism’s structure and function are underpinned by numerous biological pathways. For survival and reproduction, these pathways must be finely controlled and regulated. Control and regulation, however, are insufficient: these pathways must also be coordinated.

“Second and quite remarkably, evolution has produced only a few master coordination hubs. Moreover, evolution has produced essentially two broader means for coordination: (direct and indirect) cross-talk and (intra-cellular and extra-cellular) communication.

“Third, RB, p53, PTEN, TOR are master coordinating hubs, and they also have extensive cross-talks and communications among themselves. This fact alone explains why they have been so critical in regulating so many key biological functions, from cell cycle control to homeostasis, metabolism, development, PCD [programmed cell death], reproduction, cancer and aging.

“Fourth, because these coordination hubs have evolved in different time, their roles have been stratified or stacked upon even though they also have extension cross-talk and communication among themselves: this is the most efficient and effective way for building a coordination network.

“Finally, consistent with the gist of evolution theory, much of the innovation was driven by evolutionary pressure to survive and reproduce under stress. As a result, all the pathways and their coordination hubs bear the imprints of coping with stresses, ranging from genetic to nutritional.” Tang, Shiping. 2024. “Coordination of Pathways in Metazonas: An Integrated Framework.” Preprints.org 10.20944/preprints202405.1996.vi. Not peer-reviewed. [3] p. 15.


“Compared to membrane-encapsulated protocells, routes to multi-compartmentalization can be more facile for coacervates, with the possibility to generate several layers of substructures....

“Between coacervates and lipidic protocells exists a considerable range of membranous and membrane-like structures that can form an interface. Membrane material of such interfacial assemblies can consist of inorganic nanoparticles, proteins, amphiphilic block copolymers, or mixtures of bio-macromolecules and polyelectrolytes. They are established by self-assembly of phase separation.” Gozen, Irep, Elif Senem Koksal, Inga Poldsalu, Lin Xue, Karolina Spustova, Esteban Pedrueza-Villalmanzo, Ruslan Ryskulov, Fanda Meng & Aldo Jesorka. 2022. “Protocells: Milestones and Recent Advances.” Small. 10.1002/smll.202106624. [4] p. 10.


“Spontaneous formation of membranes from amphiphile solutions is a concentration-dependent process, in which a significant critical aggregate concentration (cac) must be reached. The concentrations of simple bioamphiphiles, synthesized under prebiotic conditions, would probably be too low to lead to self-assembly, therefore discovery of autonomous upconcentration mechanisms is crucial for identifying suitable origin of cellular life conditions.” Gozen, Irep, Elif Senem Koksal, Inga Poldsalu, Lin Xue, Karolina Spustova, Esteban Pedrueza-Villalmanzo, Ruslan Ryskulov, Fanda Meng & Aldo Jesorka. 2022. “Protocells: Milestones and Recent Advances.” Small. 10.1002/smll.202106624. [4] p. 11.


“The ultimate purpose of a model protocell is to reveal a viable pathway from abiotic matter to life, which is only possible if integration of chemical, and eventually biochemical functionality can be achieved to a point where the minimal criteria are fulfilled. A satisfactory model system, from which a mechanism of the transition to life can be derived, is still not available. Its construction requires, above all, that any protocell-internalized chemical processes and the supramolecular enveloping container are compatible, and can coexist. Under the assumption that a defined set of reactive chemical precursors and conditions existed in the prebiotic world, only a limited subset of all possible chemical and physicochemical interactions would have been compatible with, and could therefore have been actively involved in, protocell development, and eventual transformation to life. Higgs formulated criteria for chemical reaction networks to be valid contributors to growth and division of protocells. These conditions strongly involve the physical and materials features of protocells, for example, their distinct ability to retain reactants at high concentrations, establish concentration and reaction rate gradients, and exclude unsuitable components.” Gozen, Irep, Elif Senem Koksal, Inga Poldsalu, Lin Xue, Karolina Spustova, Esteban Pedrueza-Villalmanzo, Ruslan Ryskulov, Fanda Meng & Aldo Jesorka. 2022. “Protocells: Milestones and Recent Advances.” Small. 10.1002/smll.202106624. [4] pp. 17-19. Reference: Higgs, P.G. 2021. Life. 11:966.


“However, it has been repeatedly pointed out that a key limitation for a breakthrough might be the difficulty to approach the challenges in a concerted, interdisciplinary manner. It is reasonable to forecast that inter- and transdisciplinary communication efforts will increase, driven by need and opportunity alike.” Gozen, Irep, Elif Senem Koksal, Inga Poldsalu, Lin Xue, Karolina Spustova, Esteban Pedrueza-Villalmanzo, Ruslan Ryskulov, Fanda Meng & Aldo Jesorka. 2022. “Protocells: Milestones and Recent Advances.” Small. 10.1002/smll.202106624. [4] p. 26.


“Stephane Leduc realized at the beginning of the last century that chemistry and biology alone will not be sufficient to answer the questions associated with the spontaneous generation of life. We now have reason to suspect that none of the individual sciences alone can actually achieve this. However, a smart combination may have a chance.” Gozen, Irep, Elif Senem Koksal, Inga Poldsalu, Lin Xue, Karolina Spustova, Esteban Pedrueza-Villalmanzo, Ruslan Ryskulov, Fanda Meng & Aldo Jesorka. 2022. “Protocells: Milestones and Recent Advances.” Small. 10.1002/smll.202106624. [4] p. 26.


“Von Neumann’s work on self-reproducing automata shows us that, in a universe whose physical laws did not allow for computation, it would be impossible for life to evolve.” Aguera y Arcas, Blaise. 2024. What is Life? Antikythera. p. 57.


“Our artificial life experiments demonstrate that, when computation is possible, it will be a ‘dynamical attractor,’ since replicating entities are more dynamically stable than non-replicating ones; and, as von Neumann showed, replicators are inherently computational.” Aguera y Arcas, Blaise. 2024. What is Life? Antikythera. p. 87.


“In a way, symbiosis is the very essence of functionality. When we talk about a kidney’s function only making sense in context, we mean that is in symbiosis with other functions–like those of the liver (breaking ammonia down into urea), the heart (pumping blood), and so on.” Each of these functions is purposive precisely because its inputs are the outputs of others, its outputs are inputs to others, and thus they form a network of dynamically stable cycles.” Aguera y Arcas, Blaise. 2024. What is Life? Antikythera. p. 98.


“We also know that if a virus finds itself inside an organism whose physiology is too different from that of its original host, it can’t gain purchase–a lucky thing for you, if you’ve ever swallowed a mouthful of sea-water, which likely contained about a billion virus particles! (Most would have targeted single-celled marine life.) The greatest danger seems to come from viruses adapted to a different but closely related species. Perhaps, when it kills, such a virus is doing its job: wiping out rivals who have invaded the original host’s territory.

“Viruses could, in other words, work like an out-of-body immune system. Within our bodies, our immune systems seek out and destroy cells that are recognized as ‘not-us.’ Outside our bodies, ‘our’ viruses could be similarly seeking out and destroying whole animals who are recognized as ‘not-us.’” Aguera y Arcas, Blaise. 2024. What is Life? Antikythera. pp. 109-110.


“How lucky I am to have something that makes saying goodbye so hard.” Winnie the Pooh (seen on sign near Vineyard Haven ferry landing)


“In colloquial contexts, ‘agency’ is a cognitive or psychological term with connotations of freedom, self-determination, and rational control.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 143.


“In this understanding [a recent understanding of biological agency], agency is an organism’s capacity for goal-directed activity and self-determination that is not explained by underlying mechanisms or by natural selection. We refer to this as simply as ‘biological agency.’ Biological agency is supposed to be manifested in a diverse set of more specific features, including niche construction, robustness, plasticity, open-ended evolvability, downward causation, internal causal control, and even the origin of evolutionary novelties. Many of these phenomena are those foregrounded by advocates of the Extended Evolutionary Synthesis (EES), and much of the literature on biological agency can be understood as an extension of that controversial framework.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 143.


“The central claims of this paper....

5. Rejection of molecular reductionism or determinism does not necessitate a commitment to the idea of biological agency. Researchers need not embrace the agency perspective in order to acknowledge the importance of multi-level complexity, emergence, and downward causation.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 144.


“We show that the central claims of the agency perspective are unintepretable except as promoting a cryptically cognitive or psychological perspective on biological systems, despite assurances to the contrary. In effect, this perspective represents an effort to reinstitute the use of untenable psychological or folk-biological ideas in experimental research on non-human organisms. Because this perspective does not generate testable predictions, it is empirically unproductive. We conclude that biological agency is an empty concept without a research program.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 144.


“The agency perspective is rooted in the intuition that goal-directedness or purposive organization and behaviour must be an inherent attribute of an organism, not just a result of natural selection in previous generations. To justify this idea, one would have to show how it sometimes leads to different attributed ‘goals’ than those that arise from natural selection, and presumably, how it leads to different predictions and explanations.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 144.


“Biological agency is not adequately motivated empirically. It is not clear how it could become so, because its theoretical basis depends on flawed reasoning: (1) the misunderstanding or neglect of natural selection as an explanation for goal-directedness; (2) the promotion of teleological explanations in science and misunderstanding of causal-mechanistic explanation as inherently reductionistic; and (3) confusion about self-determination and whole-organism causation. Once these problems are recognized, it becomes evident that the phenomena that agency is being invoked to explain can be explained in terms of complex multiscale feedback mechanisms evolving under natural selection.” DiFrisco, James & Richard Gawne. 2025. “Biological agency: a concept without a research program.” Journal of Evolutionary Biology. 38:143-156. 10.1093/jeb/voae153. [3] p. 152.


“Any form of life is expected to be an embodied and differentiated structure that performs healing, self-repair and error correction. Such processes reduce the system’s entropy by mapping a large set of ‘incorrect’ (damaged) states to a much smaller set of ‘correct’ (viable) states. Entropy reduction also comes by growth: the synthesis of organized biological machinery from simpler, disconnected components–for example, as done by the ribosome during the synthesis of proteins–involves a large reduction in entropy.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] p. 3.


“In biology, a collection of energy intermediates drives many biotically essential transitions that otherwise would not occur spontaneously. These intermediates include, first and foremost, the phosphate-bearing cofactors (ATP and the other nucleoside triphosphates, and others) that can drive dehydrating reactions by phosphoryl group transfers, a variety of electron-transfer cofactors (such as NAD, NADP and a variety of others) and membranes that act as capacitors for the exchange of protons. Energy intermediates remove the need for internal processes to be in direct contact with environmental sources of free energy, thus achieving a kind of thermodynamic autonomy.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] p. 4.


“Formally, it [von Neumann’s Universal Constructor (UC)] was defined in terms of a ‘machine’ that is implemented using operations on a lattice. The machine includes the following four primary components: the Constructor, the Instructions, the Duplicator and the Controller. The Constructor (A) builds the new machine out of components from the surrounding environment. The Instructions (I) contain information on how A will operate and effectively define an input tape (as in Turing machines). The Duplicator (B) reads the instructions and duplicates them. Finally, the Controller (C) regulates the whole process, which has to unfold in a given sequence. As defined, the tape plays two markedly different roles. First, the information on the tape provides instructions to be interpreted and allows the construction of a machine. On the other hand, the information on the tape is also treated as uninterpreted data, which must be copied and attached to the new machine.

“Von Neumann’s insight went a crucial step beyond Schrodinger’s conceptualization of information by showing that a self-replicating agent must contain a sufficient description of itself. As happened with our previous case study, the components of von Neumann’s construction mirror those of self-replication found in cellular biology. Although the biological reality is significantly more complex and multifaceted, we find close similarities between the Duplicator and the information storage mechanism in cells (DNA, perhaps RNA in early protocells), the Controller’s role in interpreting and executing instructions resembling cellular control mechanisms, the Constructor’s function in manufacturing new components akin to cellular machinery (as executed by RNA polymerase and the ribosome ... and the Instructions reflecting the genetic information directing cellular self-replication. These striking similarities suggest a fundamental logic determining the critical components required for a self-replicating system.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] pp. 8-9.


“The search for other formal systems able to self-replicate, usually defined on a two-dimensional lattice, has shown simpler examples with a much smaller number of parts than those proposed initially by von Neumann. However, a rather crucial problem exists when mapping the original cellular automaton approach to the UC into the real world: all these systems share a high brittleness. Due to the deterministic spatially dependent nature of the rules required to implement replication, even a slight error (or mutation) typically destroys the whole pattern. Initial conditions must also be fixed in some predetermined way; otherwise, the system will not follow adequate paths towards reliable copying.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] p. 9.


“... to create a group [such as cells to form a multicellular], individual units must come together within a finite physical domain and, importantly, deal with the emergence of cheaters. In this context, two generic classes of MC [multicellularity] can be defined. In the first, MC develops from a single cell Co that generates a clonal assembly through cell division, ... whereas in the second, there is an aggregation of individual cells from a set.... These examples illustrate the following two dynamical processes that can generate MC groups: (i) stay together (ST) when, as new units are generated, they keep in close connection with the rest, and (ii) come together (CT), which occurs when the units move towards each other.... These models reveal that ST can favour the division of labour, while CT allows the exploitation of a combination of units with different properties. Both can be found at every level of biological construction, and their dynamical features define constraints to the possible.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] p. 10.


“Indeed, beyond ferromagnetism, the two-dimensional Ising model [matrix of equal energy states with some stochasticity and light influences between contiguous elements so that at lower temperatures the states rapidly align] and its extensions have been used in many contexts within complex systems. This includes its equivalence to Eigen’s quasispecies model, thus allowing mapping the error threshold as a phase transition, cell membrane response, multicellular assemblies, spatiotemporal changes in rainforests, universal models of complexity or large-scale functional brain dynamics....

“Several well-known examples of evolutionary innovations seem to be associated with a symmetry-breaking event. These include, for example, the transition from ‘pre-volution’ to evolution, natural selection, the universality of intermediate metabolism as well as the origin of chirality as a mechanism to favour one of the two possible solutions through an amplification phenomenon: the final choice would be a historical accident.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] pp. 18-19.


“... deep constraints also limit the possible kinds of evolutionary laws ruling the biosphere. These would include (i) an inevitable requirement for autocatalysis as a mechanism for population amplification, (ii) the emergence of molecular heterogeneity as a pre-condition for population dynamics, and (iii) the phase transition to evolution from a non-Darwinian to Darwinian biosphere once some given interaction thresholds are achieved.” Sole, Ricard, Christopher P. Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith & David Wolpert. 2024. “Fundamental constraints to the logic of living systems.” Interface Focus. 10.1098/rsfs.2024.0010. [6] p. 19.


“List of cellular design patterns

“Creational Structural Behavioral
Template Input/output Adaptation
Assembly line Collector/broadcaster Periodic
Passive assembly Common currency Proportional output
Active assembly Chain Hyperbolic output
Pores and pumps Parallel paths Switching
Transformation One-way cycle Direction maker
Annotation Insulator
Fold-change/ratiometric”
Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 2.


“Design patterns are closely related to motifs, mechanisms, and modules, but have the distinction that they are explicitly solutions to problems.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 2.


“We define creational patterns [column 1 in chart above] as the solutions that cells use to create the physical objects that they are built from.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 2.


“Template
“Problem. Cells need a diverse set of macromolecules (i.e., DNA, RNA and proteins) that are built from prespecified designs, and are heritable and evolvable.

“Solution. Biosynthesis using a master copy of the macromolecule sequence, which is then faithfully copied using a relatively small set of enzymes. Kinetic proofreading steps, which consume energy, are necessary for improving copying fidelity over the best that could be achieved in a copying system that does not consume energy.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 2.


“Assembly line
“Problem. Cells require molecules that perform specific physical or chemical functions that are beyond the capabilities of nucleic acids and proteins. These molecules include lipids, polysaccharides, polyamines, protein cofactors, metabolities, and many small molecules.
“Solution. Biosynthesis using an assembly line of enzymes, each of which performs a specific chemical reaction. These assembly lines can have incoming branches, outgoing branches, or cycles, as needed for managing chemical fluxes.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 2.


“Transformation
“Problem. All cellular components need to be either disassembled or transformed into new components at some point.

“Solution. Proteins that degrade or transform cellular components. These proteins require tight regulation to ensure that they only degrade or transform the correct components.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 4.


“Collector/broadcaster
“Problem. Many separate cell functions need to be regulated simultaneously in a consistent manner.

“Solution. Information flow periodically converges at central nodes that then provide consistent information to multiple downstream targets.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 4.


“Parallel paths
“Problem. The chain pattern can be too limiting if it includes steps that are infeasible or have inadequate sensitivity in particular situations.

“Solution. Multiple parallel paths that complement each other.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 6.


“Metabolism that was completely reliant on one or more one-way cycles would be unstable because it would not be able to recover from perturbations that lowered metabolite concentrations to extremely low levels. To address this, all metabolic one-way cycles appear to include alternative synthesis pathways. In the citric acid cycle, for example, note that oxoacetate can also be synthesized separately from the cycle.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 7.


“Direction maker
“Problem. While individual biochemical reactions are always reversible, they typically need to operate in a specific direction for cells to perform essential functions.

“Solution. Reactions can be made effectively irreversible if they either have a large free energy decrease or if they rely on a reactant that is kept at high concentration and produce a product that is kept at low concentration.”

“Intracellular reactions range from being sufficiently reversible that they are essentially always at equilibrium to being almost completely irreversible.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 12.


“Insulator
“Problem. Cellular reaction networks are highly interconnected, yet need to reduce crosstalk between networks with separate functions and to maintain evolvability.

“Solution. Networks that are modular, including boundaries that insulate subnetworks from each other.” Andrews, Steven S., H. Steven Wiley & Herbert M. Sauro. 2023. “Design patterns of biological cells.” BioEssays. 10.1002/bies.202300188. [6] p. 12.


“From the perspective of the RNA world, at some point RNA must have gained the ability to instruct and catalyse the synthesis of, initially, just small peptides. This initiated the transition from a pure RNA world into an RNA-peptide world. In this RNA-peptide world, both molecular species could have co-evolved to gain increasing ‘translation’ and ‘replication’ efficiency.” Mueller, Felix, Luis Escobar, Felix Xu, Ewa Wegrzyn, Milda Nainyte, Tynchtyk Amatov, Chun-Yin Chan, Alexander Pichler & Thomas Carell. 2022. “A prebiotically plausible scenario of an RNA-peptide world.” Nature. 605:279-284. 10.1038/s41586-022-04676-3. [3] p. 279.


“These experiments [using small RNA molecules in non-canonical forms in conjunction with small peptides to examine a potential RNA-peptide world that could have led to increasingly complex self-replicating RNA molecules] suggest the possibility of generating highly complex RNA-peptide chimeras with just a small number of reaction steps.” Mueller, Felix, Luis Escobar, Felix Xu, Ewa Wegrzyn, Milda Nainyte, Tynchtyk Amatov, Chun-Yin Chan, Alexander Pichler & Thomas Carell. 2022. “A prebiotically plausible scenario of an RNA-peptide world.” Nature. 605:279-284. 10.1038/s41586-022-04676-3. [3] p. 281.


“It is difficult to imagine how an RNA world with complex RNA molecules could have emerged without the help of proteins and it is hard to envision how such an RNA world transitions into the modern dualistic RNA and protein world, in which RNA predominantly encodes information whereas proteins are the key catalysts of life.

“We found that non-canonical vestige nucleosides, which are key components of contemporary RNAs, are able to equip RNA with the ability to self-decorate with peptides. This creates chimeric structures, in which both chemical entities can co-evolve in a covalently connected form, generating gradually more and more sophisticated and complex RNA-peptide structures.” Mueller, Felix, Luis Escobar, Felix Xu, Ewa Wegrzyn, Milda Nainyte, Tynchtyk Amatov, Chun-Yin Chan, Alexander Pichler & Thomas Carell. 2022. “A prebiotically plausible scenario of an RNA-peptide world.” Nature. 605:279-284. 10.1038/s41586-022-04676-3. [3] p. 283.


“In this section, we briefly discuss three of these criticisms delineating the insufficiency of homeostasis and introduce the various concepts that these critics have advanced. Each of the criticisms addresses the notion of setpoint, the idea that organisms maintain variables at specific values by negative feedback.

“The first group of critics argue that setpoints need not be fixed....

“The examples so far involve organisms changing the setpoint for negative feedback processes in response to perceived conditions. A second group of critics focuses on how organisms can anticipate conditions that have not yet arisen and adjust the setpoint for negative feedback accordingly....

“The first two challenges to homeostasis conceived as negative feedback to a setpoint focus on how setpoints are changeable either in response to changing conditions or in anticipation of such changes. The third group of critics challenges the very appeal to setpoints. In engineered systems, there is often a physical component that embodies the setpont. Typically, it is a component, as in a thermostat, that can be acted on and thus reset. In invoking the language of setpoint in biology, researchers have often assumed that there is likewise a components in the organism that can be set and, in response to anticipatory processes such as circadian rhythms, reset. The failure to find a component that functions as a setpoint would be expected to function leads some to reject the notion.” Bechtel, William & Leonardo Bich. 2025. “Rediscovering Bernard and Cannon: Restoring the Broader Vision of Homeostasis Eclipsed by the Cyberneticists.” Philosophy of Science. 92:584-605. 10.1017/psa.2024.72. [3] pp. 588, 589, 590.


“‘The coordinated physiological reactions which maintain most of the steady states in the body are so complex, and are so peculiar to the living organism, that it has been suggested that a specific designation for these states be employed–homeostasis.’

“He [Cannon] goes on to explain that he employed ‘homeo’ as an abbreviation for homoio, the Greek word for similar, not ‘homo,’ to make explicit that what was maintained was not the same state but only a similar one that ‘admits some variation.’ While he uses the word stasis in the term, he constantly refers to conditions. From this discussion, it seems clear that in introducing homeostasis he was not limiting it to negative feedback maintaining a setpoint.” Bechtel, William & Leonardo Bich. 2025. “Rediscovering Bernard and Cannon: Restoring the Broader Vision of Homeostasis Eclipsed by the Cyberneticists.” Philosophy of Science. 92:584-605. 10.1017/psa.2024.72. [3] p. 597; subquote and reference: Cannon, Walter Bradford. 1929. “Organization for Physiological Homeostasis.” Physiological Reviews. 9(3):399-431. [p. 400 for subquote]


“In this concluding section we return to the three arguments raised against the narrow negative-feedback account and discuss in turn how Bernard’s and Cannon’s understanding can be extended to incorporate the regulatory activities the critics have pointed to . The key is that for both [Claude] Bernard and Cannon the focus was on how (at least higher) organisms regulate their internal conditions so that they can carry out the activities they need to perform to continue their existence. That is, the maintenance of conditions suitable for the continued activities of the organism, not the means for doing so, is the focus.” Bechtel, William & Leonardo Bich. 2025. “Rediscovering Bernard and Cannon: Restoring the Broader Vision of Homeostasis Eclipsed by the Cyberneticists.” Philosophy of Science. 92:584-605. 10.1017/psa.2024.72. [3] p. 599.


“American voters, not just Republicans, want two somewhat contradictory things. They want the opportunity to get rich, but they also want insurance against falling into penury. In economic terms, there’s a trade-off: lower risks typically mean less potential for reward, while providing a safety net for those who don’t prosper comes at the expense of those who do. It’s the wealth generated by risky entrepreneurship that makes welfare states possible, while the more the risk-averse mentality spreads, the less money there is to support middle-class entitlements or programs for the poor. A smart politician who supports economic freedom knows they must provide the public with economic reassurances, too – while one whose priorities are nationalist or even ‘democratic socialist’ should be wise enough not to kill the capitalist goose that lays the golden eggs. New York’s future may depend on whether Mamdani understands that.” McCarthy, Daniel. 2025. “The New Right’s New Deal.” The Spectator World. September 15. p. 23.


“The Chinese model [of life], which is historically pre-scientific yet strongly compact and structured, views human beings as a dynamic, unstable organism constantly looking for balance and who is influenced by internal and external factors–primarily psychic factors that, in ancient Eastern medical philosophy, have their roots in the body, even in individual organs.” Bottaccioli, Francesco & Anna Giulia Bottaccioli. 2024. “The suggestions of ancient Chinese philosophy and medicine for contemporary scientific research, and integrative care.” Brain Behavior and Immunity Integrative. 5:100024. 10.1016/j.bbii.2023.100024. [3] p. 1.


“... natural selection is a manifestation of a more general persistence principle, whose temporal consequences we propose to name ‘stability-based sorting (SBS).... Natural selection is a specific form of SBS–sorting based on dynamic stability. It requires some form of heredity and is based on competition for the largest difference between the speed of generating its own copies and their expiration.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 29.


“However, natural selection is probably a manifestation of a more general law that affects all material and immaterial entities in the universe, does not require replication and inheritance, and is usually called survival of the stable, according to the remark in the first chapter of Dawkins’ book Selfish Gene. At first, it sounds like a tautology: Changeable entities change, whereas stable or rapidly emerging entities accumulate and predominate in the system. Indeed, the claim that the most stable (or persistent) entity lasts the longest time is undoubtedly an axiom and this ‘law’ thus seems utterly trivial, at least in a simple model. However, in the real world, coexisting entities interact in a complex manner and the consequent evolution of systems of interacting entities with variable and context-dependent persistence is all but simple (while still characteristic of the perpetual search for states of higher stability).” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 30.


“Researchers that touched it [the above principle of stability] from various angles during their investigations called it e.g. natural selection in the non-living world (Van Valen, 1989), survival in the existential game (Rappaport, 1999; Slobodkin and Rapoport, 1974), contraction (Slotine and Lohmiller, 2001), Persistence Through Time of a lineage (Bouchard, 2008; Bouchard, 2011), thermodynamic stability (Pross, 2003, 2004, 2012; Wagner and Pross, 2011), the selection of long-lasting structures (Shcherbakov, 2012), sorting on the basis of stability or sorting for stability (Flegr, 2010, 2013), natural selection through survival alone (Doolittle, 2014), viability selection or selection on persistence (Bourrat, 2014), persistence principle (Pascal and Pross, 2014, 2015, 2016), ultrastability (Bardeen and Cerpa, 2015) eventually differential persistence or persistence selection (Doolittle, 2017).” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 30.


“All forms of selection, including species selection, require selected entities to originate in reproduction or copying (and thus have an ancestor-descendant relationship) and exhibit at least some degree of inheritance of ancestor qualities. SBS, on the other hand, does not require any of this. It takes place in all systems with history, i.e., evolution in the broad sense.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 30.


“In most systems, SBS acquires solely the form of competition among entities for the highest static stability, i.e., lowest probability of expiration or transformation of individual entities or their traits into something else. In a particular class of systems–those in which new entities originate from parental entities and inherit their traits–SBS becomes predominantly the competition for the highest dynamic stability....

“Sorting based on dynamic stability (i.e. selection) and sorting based on static stability differ in the nature of what is sorted–entity itself versus the information how to create its copies.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 31.


“...in the case of terrestrial life, the selected information, which was originally coded directly in the replicating sequence of nucleotides, emancipated to some degree from its material basis. Replicators evolved interactors–bodies–that interpret the information embedded in the sequence of nucleotides in various context-dependent ways. These interactors started new rounds of competition on higher levels, so that the meaning or interpretation of genetic information and the DNA-body complex became the subject of selection rather than the nucleotide sequence itself.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] pp. 31-2.


“Nevertheless, the general SBS-mediated tendency of the system to develop towards higher stability via the accumulation of contextually stable elements affects it all the time, on all levels. The later the system is observed, the more long-term stability supporting entities and processes it accumulates and thus remains in stable states for longer periods. This agrees with the observed decrease in extinction and speciation rates and accumulation of long-lived genera in the terrestrial biosphere during the Phanerozoic.... Another consequence of SBS is that it is more probable that any such system (Earth, certain exoplanet etc.) will be met in a long-term stable state than in an ephemeral unstable one.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 37.


“All complex novelties in biological evolution originate from the joint influence of two kinds of SBS in the broad sense, the force that drive the system towards dynamic stability and the force that drive [sic] the system towards static stability.” Toman, Jan & Jaroslav Flegr. 2017. “Stability-based sorting: The forgotten process behind (not only) biological evolution.” Journal of Theoretical Biology. 435:29-41. 10.1016/j.jtbi.2017.09.004. [3] p. 38.


“Pivotal physiological processes are now studied by scientists everywhere in terms of the movements they make possible: the metastasis of cancerous cells, the migration of axons, the movement of neutrophils to the site of injury, the migration of cells during ontogenesis, intracellular transport, the walk of motor proteins in the cytoplasm.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 20.


“The processes that constitute the organism, keep it alive, and renew it again and again are always movements, but they are produced by particular situations, in specific ways, in diverse micromilieus, each with its own constraints. Never static, the physiology of the organism is what motion makes of it, new at every moment. If, as Nicholas Rescher put it, events have little or no ‘fixed nature in themselves,’ neither do their effects. Observing a movement event, and thus the workings of the body, will always be only a snapshot, a momentary impression in every sense. It is momentary for the viewer who participates in it; it is a momentary excerpt from the flow of motion, a momentary instantiation within a specific experimental setting, and a momentary instantiation within a specific experimental setting, and a momentary use of the artifices available to snatch movement out of ephemerality.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 22; reference: Rescher, Nicholas. 2000. Process Philosophy: A Survey of Basic Issues. U of Pittsburgh Press. p. 12.


“Aristotle defines motion as ‘the fulfilment of what exists potentially, in so far as it exists potentially... of what is alterable qua alterable’. Motion in this definition has a starting state and an ending state; more precisely, it is a tension between potentiality (dunamis) and actuality (energeia, entelecheia). The two terms that Aristotle uses to denote actuality, energeia and entelecheia, both of which he introduced into ancient Greek himself, are applied interchangeably in his work, but highlight the contradiction contained in the concept of actuality: energeia means–in Joe Sachs’s translation–‘being-at-work,’ entelecheia, ‘being-at-an-end.’ Actuality means both ‘to be at work’ and ‘to act for an end’ and thus is a movement between the possible and the actual, which are two elements of one and the same motion. It is, but equally is not yet; it is directed to a future. Being real, being actualized, therefore means a vital being-in-the-world and a being-on-the-way-to-completion, which is always an active being, directed toward self-maintenance or one’s role as part of a greater whole.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. pp. 30-1.


“Motion, recall, inheres in all natural things, and natural things are epitomized by living beings. ‘Life,’ as Aristotle explains in De anima, has ‘more than one sense, and provided that any one alone of these is found in a thing we say that thing is living. Living, that is, may mean thinking or perception or local movement and rest, or movement in the sense of nutrition, decay and growth. Hence we think of plants also as living’ (De anima 2.2.413a22-25. In this expansive sense of motion, as every kind of change, all the properties of life are changes in quantity, quality, substance, or place, and thus movements.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 31.


“Such mechanist thinking [Descartes’ notion of living organisms as machines] depends upon a peculiar logic by which the machine, though built in imitation of a living model, does not copy that living model, but itself comes to model the natural phenomenon. The machine is not the copy of a body according to which it has been built; on the contrary, the copy is the blueprint for the body that it originally copied.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 38.


“Harvey also follows Aristotle in defining motion as change in quality, change in quantity, and change in spatial location, driven by the avoidance of pain and the quest for pleasure.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 44.


“If we assume that the living world is an event, new at every instant and embedded in an intricate braid of movements, changing relationships, and potential futures, then not only is it in constant flux, but no disciplinary approach, whether in the natural sciences, the humanities, or the social sciences, can continue to stake an exclusive claim to explain it.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 176.


“Cytoplasmic streaming, mitosis, intracellular transport–more and more processes were now [following work on microtubules of the cytoskeleton in the 1970s and 1980s] identified as motion phenomena....” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 180.


“This [the difficulty of comparing two movements including through new video techniques at high magnification] raises two questions: How can we make movements comparable with each other, and upon what basis can we compare processes generated in a representation with processes taking place in nature? Above and beyond those questions, Graeper’s work indicates a different scale of comparability. Graeper understood that even if a common goal exists, the ways of moving toward it are manifold. Motion is a trajectory in its own right, so it is not possible to infer from the product of a process the motion that brought it about.” Wellmann, Janina. 2024. Biological Motion: A History of Life. NY: Zone Books. p. 204; reference: Graeper, Ludwig. From early 20th century. Bio in: Buch der Docenten der Medicinischen Facultaet zu Jena. 2004. Wiederanders, Bernd & Susanne Zimmermann (eds). Golmsdorf: Jenzig.


“Self-organization is an extension of self-assembly, but employing several new chemical principles. In contrast to self-assembly, self-organization gives ‘structures under a wider set of condition; the rules tend to be more general and the structures more variable’. Self-organizing systems are characterized by reaching a steady state, where there is continuous energy consumption and gain and loss of material.. In discussing examples of self-organization, we will focus on two of the most archetypal and unusual biological properties: (1) the capacity for unitary organization, also called polarization; and (2) the capacity to generate nearly regular biological structure when size and composition of components are altered, also called regulation.” Kirschner, Marc, John Gerhart & Tim Mitchison. 2000. “Molecular ‘Vitalism’”. Cell. 100:79-88. January 7. [4] p. 80; subquote: Kirschner, M. & T. Mitchison. 1986. “Beyond self-assembly: from microtubules to morphogenesis.” Cell. 45:329-342.


“Listeria monocytogenes, an intracellular pathogenic bacterium, hijacks the natural actin nucleation machinery of the cell and propels itself through the cytoplasm by triggering assembly of a polarized ‘comet tail’ of actin. A single secreted protein of the bacterium, Act A, is sufficient to induce actin assembly.” Kirschner, Marc, John Gerhart & Tim Mitchison. 2000. “Molecular ‘Vitalism’”. Cell. 100:79-88. January 7. [4] p. 80.


“Two types of protein-protein interactions are thought to drive spindle assembly: tubulin-tubulin interactions and tubulin-motor protein interactions. Assembly driven by motors means that initially random microtubules can slide actively past each other to achieve correct positions. Such motor driven sorting is thought to be a major force in spindle assembly. Tubulin-tubulin interactions in the microtubule lattice are coupled to GTP hydrolysis, which powers rapid microtubule turnover by drastically increasing the off-rate. This process, termed dynamic instability, accelerates the rate at which microtubules probe cellular space, and destabilizes incorrect organizations relative to the correct ones. Motor-dependent assembly interactions and dynamic instability collaborate to make incorrect or partial assemblies dissipate energy faster than the correct one, and together provide a thermodynamic drive to the assembly of functionally correct structures.

“This picture of self-organization to a thermodynamic minimum at steady state is likely applicable to many, perhaps all, cellular assemblies.” Kirschner, Marc, John Gerhart & Tim Mitchison. 2000. “Molecular ‘Vitalism’”. Cell. 100:79-88. January 7. [4] p. 82.


“The two main filamentous structures found in the cytoskeleton are actin and microtubules. Both filaments are inherently polar; thus, molecular motors move towards a specific filament end. Myosin motors move along actin filaments, whereas kinesin and dynein motors walk along microtubules. Each category of motors is classified into further sub-categories that depend on their evolutionary history. Motors can be either processive or non-processive. Processive motors can take hundreds of successive steps before dissociating from a filamentous track, whereas non-processive motors take only a single step along a filament before unbinding.” Needleman, Daniel & Zvonimir Dogic. 2017. “Active matter at the interface between materials science and cell biology.” Nature Reviews Materials. 2:17048. 10.1038/natrevmats.2017.48. [3] p. 3.


“Actin polymerization into filaments can produce significant mechanical force. Protrusive motility in biological systems can be initiated by local catalysis of actin polymerization, for example at the leading edge of lamellipodia and filopodia in crawling (amoeboid) cells. Persistent directional cell locomotion requires an asymmetric distribution of growing actin filaments, a classic example of cell polarity. Similar to polymerization at the leading edge of amoeboid cells, local polymerization of actin filaments is also catalysed at the surface of some intracellular bacterial pathogens, including Listeria monocytogenes, Shigella flexneri and spotted-fever group Rickettsia. Host-cell actin polymerization pushes these bacteria through the cytoplasm, enabling efficient intracellular and intercellular spread. For L. monocytogenes, actin polymerization is induced by the virulence factor ActA, a bacterial surface protein. ActA interacts with host-cell proteins to catalyse local actin filament nucleation and elongation, but ActA does not remain physically attached to the filaments. As with amoeboid actin-based motility, persistent directional motion in the bacterial systems requires an asymmetric distribution of growing actin filaments. Shortly after entering the host-cell cytoplasm, L. monocytogenes becomes surrounded by a symmetric ‘cloud’ of host-cell actin filaments. Movement is initiated when the symmetric cloud is rearranged to form an asymmetric ‘tail’. The ActA protein is distributed in a polarized fashion on the bacterial surface, and its distribution dictates which pole will form the tail.

“Here, we explore how actin asymmetry arises and is maintained, starting from a symmetric filament distribution. We have studied the process of symmetry-breaking in an in vitro system, in which the polarized bacterium is replaced with a spherical polystyrene bead that has no structural asymmetry, and have compared experimental measurements with the predictions of a new theoretical model that is based on the known dynamic properties of actin polymerization. We find that the presence of the bead effectively couples the polymerization of different filament tips, such that filaments on the same side of the bead cooperate with one another, while filaments on opposite sides of the bead inhibit each other’s growth. This arrangement allows for small stochastic fluctuations to be amplified under certain conditions, so that symmetry-breaking can readily occur for the system as a whole. The coupling between polymerization dynamics and mechanical force in a system of interacting actin filaments can explain the actin cytoskeleton’s remarkable ability to act as a self-organizing system capable of spontaneously generating unidirectional motion.” Van Oudenaarden, Alexander & Julie A. Theriot. 1999. “Cooperative symmetry-breaking by actin polymerization in a model for cell motility.” Nature Cell Biology. 1:493-499. December. [3] pp. 493-4.


“The spontaneous symmetry-breaking that we observe in the model [of multiple growing actin filaments ‘pushing’ a plastic bead] indicates that the actin filaments in the cloud may behave cooperatively, such that the addition or loss of a subunit on one actin filament influences the likelihood of addition or loss on other filaments.... ... we observe that filaments that are almost parallel are anti-correlated. If, at a given time, one filament tip is closer to the bead than its average position, it is significantly likely that the other filament tip will be further from the bead than its average position. The filaments therefore operate cooperatively in a hand-over-hand fashion: addition of a subunit to one filament tip pushes the bead forward, generating a gap which enables efficient addition of a subunit to the other filament tip....

“We have found that two properties of actin filaments are particularly critical for efficient symmetry-breaking in a system in which forces are generated by protein polymerization.

“First, it is necessary that the polymerization reaction be readily reversible, that is, that the subunit off-rate at a growing filament tip be moderately high. Intuitively it is straightforward to understand why bead movement will be inefficient in the two limiting cases, where Poff is close to 0 or close to 1. If the subunit off-rate is small (Poff <0.05) symmetry-breaking cannot occur, because the filaments all are constantly growing and cage the bead inside a symmetric cloud. In this regime, the bead performs a random walk with a step size that is much smaller than the actin-subunit size Δ. Conversely, if Poff is large (Poff > 0.8), the filaments will quickly depolymerize away from the surface of the bead, and the bead will then undergo a diffusive random walk whose step size is determined by the diffusion coefficient of the bead. The surprising emergent behaviour of symmetry-breaking and superdiffusive movement appears only for intermediate values of Poff, most efficiently when Poff ≈0.2–0.4. In this range, some filaments will grow while others shrink. Because addition of a subunit to one filament will increase the probability of addition of a subunit to nearby filaments, the simultaneous growth of near neighbours is positively reinforced.” Van Oudenaarden, Alexander & Julie A. Theriot. 1999. “Cooperative symmetry-breaking by actin polymerization in a model for cell motility.” Nature Cell Biology. 1:493-499. December. [3] p. 497.


“Symmetries have a conservative nature because they are transformations that can be inverted. For instance, in our example [planet moving around a sun where planet speeds up when closer to sun] and thanks to the system’s symmetry invariance, when the distance to the Sun increases, the velocity of the planet decreases, so the mechanical energy always remains the same. More technically, symmetries by time-translation are associated with energy-conservation.... Strictly speaking, such a compositional, hierarchical, and mediated relation between states and structure cannot be treated causally, because the space-time path of the elements coexists with the space-time path of the structure. The structure will not exist before the states and vice versa.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. p. 2.


“The concept of a closed system in physics is simply a strong idealization, nothing but the expression of the fact that macroscopic thermodynamics cannot be fully recovered by Statistical Mechanics without the use of infinite idealizations because phase transitions, a widespread and basic physical phenomenon, can occur only in open thermodynamic systems, in either a weak (energy exchanges) or a strong (matter and energy exchanges) use of this term.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 3.


“For an infinite idealized system of particles occupying an infinite volume, the partition function Z characterizsing its physical structure can harbor singularities. Yet, as pointed out by Callender, phase transitions occur in finite systems in nature: ‘Phase transitions – as understood by statistical mechanics – can only occur in infinite systems, yet the phenomena that we are trying to explain clearly occur in finite systems’.

“Such physical systems are, in effect, incomplete because the characterization of the states of the system does not simply depend on the system’s internal physical structure, and singularities that are not directly analytically solved can appear in it. These physical systems also depend on boundary conditions: a flow equation characterizing the relation between the systems and their boundary conditions can be drawn, but it exhibits singularities at critical points, and such systems will not conserve their initial structures.

“In other words, these systems exhibit symmetry breaking (SB) at the critical point of phase transition. Unlike the classical systems in physics, SB cannot be explained by an inherent relationship between appropriate symmetry groups and conservation principles. Instead, at the critical point, SB exhibits new global properties expressed by order parameters, like the loss of entropy, clustering distribution indices, or infinite correlation length.

“Thus, the dynamics of the system are not fully explained by the initial structure – that structure should be invariant under any transformation with respect to the symmetries defined by an appropriate symmetry group. In fact, the structure of the system at the critical point is a result of the system’s operations, not the structure itself.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 3; reference: Callender, C. 2001. “Taking thermodynamics too seriously.” Stud. Hist. Philos. Sci. Part B Stud. Hist. Philos. Mod. Phys. 32:539-553. p. 549.


“Following Montevil and Mossio, the new global properties that emerge over time in critical systems can be called constraints. Constraints are expressed by recursive fixed-point equations that permit us to calculate critical exponents that are simply the signature of the emergence of new global properties in a local system. Constraints are neither laws, nor universal properties. They only emerge in open physical systems submitted to specific boundary conditions. They cannot be determined by classical symmetry groups in a classical phase space. Constraints are not included in the laws of conservation, because they are context- and timescale-dependent.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 4; reference: Montevil, M. & M. Mossio. 2015. “Biological organisation as closure of constraints.” J. Theor. Biol. 372:179-191.


“In general, we argue that the major difference between physical and biological open systems is that in the latter, we face extended criticality, i.e., protracted and intertwined multiple critical phase changes. A biological system is continuously generating new constraints through a continuous flow of symmetry breaking, causing its space of constraints to appear open-ended.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 4.


“In contrast [to physical dynamical systems], in biology, unpredictability is associated with the fact that pathways and trajectories always occur in an open-ended space of constraints, and this space continuously regenerates itself. Thus, a biological system appears as a kind of autonomous device that is always regenerating and propagating itself through one or through multiple thermodynamic virtuous cycles.

“The first consequence of this regeneration is that such a system is incomplete because its structure and symmetries are continuously modified by the pathway and trajectories of the system itself.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 4.


“The fact that enantiomeric excess (ee) can be directed by experimental conditions sheds new light on a long debate on which conditions and/or processes could have led to the origin of homochiral protocell and which organic molecules were the first to break the symmetry. While highlighting various abiotic mechanisms for enantioenrichment, a comprehensive review by Blackmond points out that ‘the burden of chiral selectivity might have been shared (between abiotic and biotic factors) as complexity increased’. Such a notion leaves the ‘open possibility that the prebiotic molecular pool need not have evolved completely to single chirality before the formation of the first biopolymer chains’ so that ‘the origin of biological homochirality is [not] a separate and disjoint event’.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 8; subquotes: Blackmond, D.G. 2010. “The origin of biological homochirality.” Cold Spring Harb Perspect. Biol. 2:1002147. 10.1101.cshperspect.a002147; Ribo, J.M., D. Hochberg, J. Crusats, Z. El-Hachemi, & A. Moyano. 2017. “Spontaneous mirror symmetry breaking and origin of biological homochirality.” J. R. Soc. Interface. 14:20170699. 10.1098/rsif.2017.0699.


“Furthermore, first, we can identify and analyze symmetry breakings (asymmetries) in biology functionally, i.e. synchronously and diachronically. These layers of the biosphere are singled out by relevant SB examples. Based on the scale at which they occur, the identified cases of SB can be classified in the following way:

“1. molecular: homochirality;
“2. sub-cellular: cytoskeleton assembly, ion gradients across the membranes;
“3. cellular: epithelial (apical-basal polarity), planar cell polarity, polarity of growth;
“4. tissue-related: actin filamentation, gastrulation (emergence of diploblasts vs. triploblasts);
“5. organism-related: left-right symmetry, inward/outward flow of matter and energy;
“6. species- and higher taxa-related: evolutionary radiations and extinctions.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 8.


“To sum up, research results over the last two decades have demonstrated how cell polarity, originating from SB at the levels of cell filaments, enables or streamlines cell motility, growth, shape, and left-right asymmetry.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 8.


“More specifically, a general structure of the emergence of SB in biological systems can be expressed as the following: SB at one level seems to persist, propagate, and result in further SB at different scales by becoming a stable constraint. The three basic aspects and stages of SB propagation, then, can be characterized as:

“1. Persistence/Plasticity
“2. Accumulation/Amplification
“3. Emergence of upper-scale SB.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] pp. 8, 9.


“Many authors have noticed a specific feature of all modern cosmological models, including the post-1998 ‘New standard cosmology’, namely that the apparent complexity of matter has dramatically increased since the early universe by as much as 120 orders of magnitude or more. It is accepted that SB is the source of this increasing complexity, although the details remain highly controversial. This could be thought of as one manifestation of the universe obtaining more and more structure with the passage of cosmic time. Since this increase is obviously extremely spatially inhomogeneous (99.99% of the spatial volume of the present-day universe is low-complexity intergalactic space), we are entirely justified in searching for local peaks of complexity which correspond, to the best of our empirical knowledge, to habitable planets like Earth and their biospheres. Such a conclusion poses a new challenge to Copernicanism; according to complexity metrics, our location is not random or even typical any more.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 10.


“What emerges is a general picture that should be considered, an interface of Darwinism and SB-based system analysis. The Darwinist approach is advantageous in defining the turning points of circumstances into constraints, while SB-based analysis identifies the features of a wider cycle. The latter theory tells us how to put together, in general, seemingly different processes; i.e. it identifies the structural traits of life and the biosphere. Life is not simply a thermostat controlled by positive and negative feedback; it is a peculiarly complex process, controlled, on the one hand, by positive regulation from a critically extended set of constraints (continuously creating new constraints through new SB), and, on the other hand, by negative regulation like natural selection, arising from the irreversibility of the functional incompleteness the cascade creates. The complex shape of organization in the biosphere can be characterized as a result of such a process, i.e., the association of two antagonistic processes, through which the biosphere evolves.” Korenic, Andrej, Slobodan Perovic, Milan M. Cirkovic & Paul-Antoine Miquel. 2020. “Symmetry breaking and functional incompleteness in biological systems.” Progress in Biophysics and Molecular Biology. 150:1-12. 10.1016;j.pbiomolbio.2019.02.001. [5] p. 11.


“Therefore, relevance realization is one of the key properties that sets apart living systems from non-living ones, such as algorithms and their concrete physical implementations, which we will call machines.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 2.


“Neither ‘fitness’ nor ‘relevance’ have any universal attributes: there is no trait that renders you fit in all environments, nor is there any fact that is relevant across all possible situations.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 3.


“Among the diversity of perspectives [on agency and cognition], we can identify two general trends in attitudes. Let us call them agential emergentism and computationalism.

“Computationalism, as we use the term here, encompasses various forms of cognitivism and connectionism. It is extremely popular and widespread in contemporary scientific and philosophical thinking, the basic tenet being that both natural agency and cognition are special varieties of algorithmic computation....

“The strongest versions of computationalism assert that all physical processes which can be actualized (not just cognitive ones) must be Turing-computable. This pancomputationalist attitude is codified in the strong (or physical) Church-Turing conjecture.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 4.


“To better understand and ultimately overcome this problem [attempting to compute with logical symbols the world without its having yielded its relevance], we adopt an alternative stance called agential emergentism. The basic idea is to provide a fresh and expanded perspective on life that allows us to bridge the gap between the syntactic and the semantic realms, between small and large worlds. Agential emergentism postulates that all organisms possess a kind of natural agency....

“We can define natural agency in its broadest sense as the capability of a living system to initiate actions according to its own internal norms. This capability arises from the peculiar self-referential and hierarchical causal regime that underlies the self-manufacturing organization of living matter.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 4.


“How, then, are we to understand relevance realization if not in terms of formal problem solving? One possibility is through an economic perspective, which frames the problem of relevance based on commitment, i.e., the dynamic allocation of resources by an agent to the pursuit of a range of potentially conflicting or competing goals. Opponent processing is seen as a meta-heuristic approach: the agent employs a number of complementary or even antagonistic heuristics that are played against each other in the presence of different kinds of challenges and trade-offs. The trade-offs involved can be subsumed under the general opposition of efficiency vs. resilience or, more specifically, as generality vs. specialization, exploration vs. exploitation, and focusing vs. diversifying....

“Such high-level adaptive dynamics can be embedded in a physical context through the notion of predictive processing. Predictive processing means that an agent iteratively and recursively evaluates the relevance of its sensory input through the estimation of prediction errors. It does this by measuring the discrepancy between expectations based on its internal models of the world and the sensory feedback it receives from its interactions within its current arena.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 7.


“We can now conceptualize organizational closure as the closure of constraints: the organism-level pattern of constraints restricts and channels the dynamics of the underlying processes in such a way as to preserve the overall pattern of constraints. Evidently, organizational closure is causally circular: it is a form of self-constraint.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 8.


“A more formal and abstract way to think about biological organization is Robert Rosen’s relational theory of metabolism-repair (M,R)-systems, and its recent refinement to fabrication-assembly (F,A)-systems (Hofmeyr, 2021). It treats biological organization in the rich explanatory context of Aristotle’s four ‘causes,’ or aitia....

“Hofmeyr extends Rosen’s mathematical methodology in a number of crucial aspects. First, he integrates the missing formal cause into Rosen’s framework: its role is to determine the specific functional form of each efficient processor and/or material flow. It is in this precise sense that the notion of ‘constraint’ includes aspects of both formal and efficient cause.... The resulting model is called a fabrication-assembly (F,A)-system to reflect the fact that self-manufacture consists of two fundamental aspects: self-fabrication of required components, plus their self-assembly into a functional whole.

“(F,A)-Systems highlight a number of features of biological organization that are not evident from Rosen’s original account. First of all, one of the major efficient causes of the model (the interior milieu) exists only at the level of the whole living system (or individual cell), and cannot be reduced or localized to any subset of component processes. If it was not already clear before: biological organization is an irreducible systems-level property. This is perhaps why it is so difficult to study with purely reductionist analytical approaches.

“Second, (F,A)-systems are closed to efficient cause but open to formal causation. This means that the specific form of their processors and flows constantly changes while still maintaining organizational closure. This enables physiological and evolutionary adaptation by introducing heritable variability to Rosen’s formalism. It links the fundamental biological principles of organization and variability in a way that is not possible with the less refined distinction between processes and constraints.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] pp. 9-10.


“It is important to repeat that there is nothing the bacterium explicitly intends to do, nor is it in any way aware of what it is doing, or how it selects an appropriate action. Its responses are evolved habits in the sense that there are few alternative paths of action, there is very little flexibility in behavior, and there is certainly no self-reflection. And yet, bacteria have evolved the capacity to distinguish what is good and what is bad for their continued existence, purely based on endless runs of trial-and-error in countless generations of ancestors. This is basic relevance realization grounded in adaptive evolution. And it qualifies as basic anticipatory behavior: the expected outcome of an action influences the bacterium’s present selection of actions and strategy. The fundamental requirement for a predictive model is fulfilled: there is a subsystem in the bacterium’s physiology that induces changes in its present state based on expectations about what the future may bring. This is what we mean when we say that anticipatory systems can pull the future into the present.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 12.


“To summarize: all organisms, from bacteria to humans, are anticipatory agents. They are able to set their own goals and pursue them based on their internal predictive models. Organisms, essentially, are systems that solve the problem of relevance.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 12.


“We now come to the core of our evolutionary account of relevance realization, which is based on an organism-centered agential perspective on evolution called situated Darwinism. It is an ecological theory of agency and its role in evolution, which centers around the engagement of organisms with their experienced environment. Situated Darwinism centers around the following three basic ingredients: (1) a collection of intrinsic goals for the organism to pursue, (2) a set of available actions (the repertoire of the agent, shaped with respect to its experience and expectations...), and (3) affordances in the experienced environment. In what follows, we show that the dialectic co-emergent dynamics between these three components provide an evolutionary explanation of relevance realization.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 13.


“A multifaceted and multilayered picture of relevance realization in living organisms is emerging. What we have so far are three different dialectic processes, at three different levels of organization:

“1. the process of autopoiesis (self-manufacture) – internal to the organism, established through collective co-constitution of macromolecular biosynthesis, maintenance of internal milieu, and regulated selective cross-boundary transport ....
“2. the process of anticipation–internal to the organism, but projective (about the environment), established through collective co-constitution of internal predictive models (‘expectations’), the current state of the organism, and effectors modulating this state and the sensory inputs that feed it based on model predictions–which enables the agent to pursue intrinsic goals through selection of suitable actions and behavioral strategies; and
“3. the process of integrated adaptation–transjective (grounded in the relation between agent and arena), established through collective co-constitution of the intrinsic goals, repertoires of action, and affordance landscapes of an organism-environment system–which amounts to relevance realization in its broadest evolutionary sense, a continuous tightening of the agent-arena relationship and hence the organism’s ‘grip on reality’.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 15.


“Of particular interest here [when an amoeba has much more sophisticated kind of goal-orientedness than the random tumblings of a bacterium] are two scenarios that build on each other. The first occurs when the interrelations between goals, actions, and affordances surpass a certain threshold of intricacy, such that they require a new kind of predictive internal model. This yields a definition of a cognitive system as an agent which can actively take its world to be a certain way, regardless of whether the world really is that way or not. In other words, a cognitive system is an agent that is complex enough to be mistaken or deluded about the world. While a bacterium may fail to achieve its aim of finding higher concentrations of nutrients when swimming up a concentration gradient (the distribution could be discontinuous, or other unexpected dangers may lurk at the top of the gradient), it is too simple to have a wrong model of the world. The failure modes of bacteria and cognitive systems diverge in this instance, both in the intricacy of the error and with respect to possible consequences of the error for the system.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 17.


“The ‘embodied’ part of rationality means that, in order to solve problems through logical inference, we must first turn ill-defined large-world problems into well-defined small-world ones. And this is what relevance realization does, not only in humans, but in all living organisms: it generates the predictive hypotheses and models we need to be able to engage in abduction.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 21.


“The dream of generating purely algorithmic systems able to think and act like human beings is and remains a pipe dream, because purely symbolic machines exist in small worlds, in which there is no problem of relevance to be solved.” Jaeger, Johannes, Anna Riedl, Alex Djedovic, John Vervaeke & Denis Walsh. 2024. “Naturalizing relevance realization: why agency and cognition are fundamentally not computational.” Frontiers in Psychology. 10.3389/fpsyg.2024.1362658. [4] p. 21.


“To look at the role of cognitive activities involved in procuring and eating food, this article adopts a distinctive strategy of ‘phylogenetic refinement’ (Cizek 2019). It consists in analyzing the most basic instances of cognitive activities in relatively simple organisms and describing when possible, or at least point to, the basic features of the underlying mechanisms responsible for them. If the mechanisms involved are conserved through evolution, the results can then prove useful for understanding what happens in more complex organisms up to humans. This strategy is part of a wider approach variously called ‘cognitive biology’ (Kovac 2000), the ‘biogenic approach to cognition’ (Lyon 2006), or ‘basal cognition’ (Lyon et al. 2021). It aims to investigate cognition starting from its biological roots in organisms such as bacteria, and emphasizes the continuity of cognitive phenomena across all living organisms. The biogenic approach is usually contrasted with mainstream ‘anthropogenic’ approaches, which start from humans as the paradigmatic cognitive organism, aim to investigate distinctively human cognitive activities, and limit its phylogenetic extension to species phylogenetically closely related to humans. The anthropogenic approach associates cognition with the brain, especially the most recently evolved regions such as the neocortex, and thereby denies the continuity of cognition with organisms lacking such structures.” Bechtel, William & Leonardo Bich. 2024. “Eating and Cognition in Two Animals without Neurons: Sponges and Trichoplax.” Biological Theory. 10.1007/s13752-024-00464-6. [4; unsure page numbers] p. 3.


“To understand the richness of the cognitive activities involved in making and executing decisions related to eating, it is important to move the focus from the reception and processing of information to the role these activities play in the context of the whole organism. This shift foregrounds three aspects that need to be considered, and that shed light on fundamental requirements for cognition and on the differences in how it is realized. The first is the integration of information. Control mechanisms do not necessarily operate on the basis of the measurement of one variable by one sensor but rather usually integrate measurements performed by multiple receptors. Receptors in cells are often organized in clusters, with the effector activity of a control mechanism depending on combining information from these sources. Moreover, integration of information is often achieved through crosstalk between different control mechanisms, each sensitive to different sets of features of the internal and external environment.

“The second aspect is the distribution of information. Once measurements are made and information gathered, it needs to be made accessible to different parts of the organism. Unicellular systems mostly rely on diffusion and active transport to do this. In multicellular systems, distribution is a challenge, as information may need to reach different ensembles of cells across different distances in increasingly large bodies with a high number of components....

“The third aspect is the coordination of parts to carry out the activities required by the whole organism. Controlled activities such as foraging and feeding need the coherent behavior of many cells if not of the entire body. This requires the coordinated activity of multiple control mechanisms sensitive to different sources of information and jointly operating to orchestrate the basic behaviors of individual or large ensembles of cells, often organized in modules, tissues, and organs.” Bechtel, William & Leonardo Bich. 2024. “Eating and Cognition in Two Animals without Neurons: Sponges and Trichoplax.” Biological Theory. 10.1007/s13752-024-00464-6. [4; unsure page numbers] p. 5.


“The movement of Trichoplax when not feeding appears to be random. But Smith et al. determined Trichoplax engage in chemotaxis, navigating up a chemoattractant gradient towards food. The process is not centrally controlled: rather, each epithelial cell detects the gradient and initiates its own motion. The physical connectedness of the cells and elastic forces in their mechanical response constrain the cells to move as a group. The ability of individual cells to respond on their own is evident in the individual movements of cells in the interior of the ventral surface where these constraints are relaxed.” Bechtel, William & Leonardo Bich. 2024. “Eating and Cognition in Two Animals without Neurons: Sponges and Trichoplax.” Biological Theory. 10.1007/s13752-024-00464-6. [4; unsure page numbers] p. 11.


“We identify two modes of information in biological context: The cryptic information, codified in sequences, as in the genes. As well as the explicit information that does not require a transducer by which the encoded-meaning is deciphered to exert control in the system. In explicit information, the references are directly available for use by components. Therefore, explicit information is spatial information (supported by the dynamic transfer of geometries or molecular conformations, similar to a mold that explicitly transfers shape, a process that resembles that of prions during propagation), or temporal information (supported by the acquisition and transfer of references from periodic succession of well-defined steps in a phenomenon).” Cruz-Rosas, Hugo I., Francisco Riquelme, Alejandra Ramirez-Padron & Thomas Buhse. 2020. “Molecular shape as a key source of prebiotic information.” Journal of Theoretical Biology. 499:110316. 10.1016/j.jtbi.2020.110316. [3] p. 2.


“By limiting biological information to strict genetic information, the other important flow of information, which is based on supramolecular matching and shape recognition, is not considered. Biological information based exclusively on genetic information leads to conceptual barriers and theoretical restrictions in the study of the origin of life.” Cruz-Rosas, Hugo I., Francisco Riquelme, Alejandra Ramirez-Padron & Thomas Buhse. 2020. “Molecular shape as a key source of prebiotic information.” Journal of Theoretical Biology. 499:110316. 10.1016/j.jtbi.2020.110316. [3] p. 2.


“In the context of supramolecular recognition, we can define the explicit spatial information as that one that originates from the dynamic transferring and processing of data contained in the geometry of molecular structures, which enables the system to discriminate between the shapes of components.” Cruz-Rosas, Hugo I., Francisco Riquelme, Alejandra Ramirez-Padron & Thomas Buhse. 2020. “Molecular shape as a key source of prebiotic information.” Journal of Theoretical Biology. 499:110316. 10.1016/j.jtbi.2020.110316. [3] p. 3.


“The recognition of molecular shape in the formation of enzyme-substrate and receptor-ligand complexes, the antigen recognition by antibodies in humoral immunity response and prion propagation are examples showing that the matching/ recognizing and transferring of molecular geometries profoundly contribute to the inner organization and functionality of living systems.” Cruz-Rosas, Hugo I., Francisco Riquelme, Alejandra Ramirez-Padron & Thomas Buhse. 2020. “Molecular shape as a key source of prebiotic information.” Journal of Theoretical Biology. 499:110316. 10.1016/j.jtbi.2020.110316. [3] p. 3.


“... some hypotheses based on the assumption that the molecular shape is a source of prebiotic information have been proposed. In these scenarios, information is considered conformational information (Maury, 2018) and is defined as spatial information (Cruz-Rosas et al., 2017). This information is a source of structural inheritance (Jablonka and Raz, 2009). Under prebiotic scenarios, the molecular structures that carry this information have been proposed as conformons (Ogayar and Sanchez-Perez, 1998). The simplicity of this kind of contextual information is the result of being the product of a one-step event (folding). Accordingly, the molecular conformation is the central aspect in these theories because it is the location in which both the proper processes of polymer synthesis and environmental conditions converge. Salt-induced peptide formation provides one of the plausible prebiotic scenarios for the formation of peptides, which are capable of acquiring diverse spatial arrangements. One of the simplest property on a peptide, working like a function, is the adoption of a conformation that endures during environmental perturbations. This function is realized only within a dynamic in which the most durable conformons are incorporated into a prebiotic system. The propagation of such durable conformons contributes to the stability of the system scaffold in the presence of milieu variations.” Cruz-Rosas, Hugo I., Francisco Riquelme, Alejandra Ramirez-Padron & Thomas Buhse. 2020. “Molecular shape as a key source of prebiotic information.” Journal of Theoretical Biology. 499:110316. 10.1016/j.jtbi.2020.110316. [3] p. 6; references: Maury, C.P.J. 2015. “Origin of life, Primordial genetics: Information transfer in a pre-RNA world based on self-replicating beta-sheet amyloid conformers.” J. Theor. Biol. 382:292-297; Cruz-Rosas, H.I., F. Riquelme, M. Maldonado & G. Cocho. 2017. “Critrical role of spatial information from chiral-asymmetric peptides in the earliest occurrence of life.” Int. J. Astrobiol. 16:28-39; Jablonka, E.V.A. & G.A.L. Raz. 2009. “Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution.” Q. Rev. Biol.; Ogayar, A. & M. Sanchez-Perez. 1998. “Prions: An evolutionary perspective.” Int. Microbiol;


“Traditional network-based analyses of the brain have largely ignored the spatial component of multiscale datasets, such as geometry and morphology of neurons, treating them as point-like nodes rather than physical objects with length, volume, and a branching tree structure.... There is a real need, for both network science and neuroscience, to go beyond simple connectivity information and incorporate the true physical nature of neurons, informed by weighting cell properties with their connections, allowing us to enrich our understanding of neuronal circuit operations.” Barabasi, Daniel L., Ginestra Bianconi, Ed Bullmore, Mark Burgess, SueYeon Chung, Tina Eliassi-Rad, Dileep George, Istvan A. Kovacs, Hernan Makse, Thomas E. Nichols, Christos Papadimitriou, Olaf Sporns, Kim Stachenfeld, Zoltan Toroczkai, Emma K. Towlson, Anthony M. Zador, Hongkui Zeng, Albert-Laszlo Barabasi, Amy Bernard & Gyorgy Buzsaki. 2023. “Neuroscience Needs Network Science.” The Journal of Neuroscience. 43(34):5989-5995. 10.1523/JNEUROSCI.1014-23.2023. [4] p. 5991.


“The main use of network tools in brain science has so far been limited to the mapping and analysis of static network maps, ignoring the temporal scale of brain connectivity and especially the temporal aspects of brain activity (i.e., network dynamics)....

“One central question in this field is how neuron identity, captured by gene expression profiles, location, and shape, determines the wiring patterns of neurons and leads to stereotyped connectivity and behavior. Network models of neurodevelopmental principles are needed, therefore, to validate hypotheses and make predictions for future experiments.... These models are most successful when they take into account the affordances of the niche in which organisms operate, including noise from data collection limitations and spatial restrictions, offering more accurate descriptions of the complex landscape of neuronal circuit construction.” Barabasi, Daniel L. et al. 2023. “Neuroscience Needs Network Science.” The Journal of Neuroscience. 43(34):5989-5995. 10.1523/JNEUROSCI.1014-23.2023. [4] pp. 5991-2.


“In technological networks, such as the Internet or a computer chip, structure and function are carefully separated: information is encoded into the signal; hence, the role of the network is only to guarantee routing paths between nodes. In the brain, however, action potentials do not encode information in isolation. Instead, the brain relies on population coding, meaning that encoding is implemented by the patterns of signals generated by multiple physical networks of connections. Thus, monitoring and quantifying this network structure are critical for understanding how neuronal coding achieves information processing. This makes the structure of the network more than a propagation backbone; it becomes an integral part of the algorithm itself. Thus, the connectome cannot be understood divorced from the context of the actions it performs. Hence, the modules, metrics, and generative processes that support robust representation need to be integrated with the structural representation.” Barabasi, Daniel L. et al. 2023. “Neuroscience Needs Network Science.” The Journal of Neuroscience. 43(34):5989-5995. 10.1523/JNEUROSCI.1014-23.2023. [4] p. 5992.


“There is a substantial body of literature looking at theoretical, behavioral, and neural aspects of decision certainty, also referred to as decision confidence. It has been shown that prefrontal and parietal cortical areas represent decision certainty. While these studies have established that multiple brain areas contribute to decision certainty, they have not examined the network computational mechanisms that drive variability in decision making under different levels of certainty.

“A prominent family of models that account for flexible decisions are the two-state attractor models. In these models, decisions are made when network activity settles into one of two attractor basins.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1970.


“To the best of our knowledge, there has been no direct neural evidence that the geometry of attractor basins, which reflect the energy landscape that drives neural activity, predicts decision certainty. In this work, we investigated population dynamics of prefrontal neurons using high channel-count recordings while monkeys performed a decision-making task. We trained monkeys to choose between rejecting and accepting offers of different reward sizes and delay. By linking neural attractor dynamics to decision certainty, we provide evidence that the energy landscape in prefrontal cortex predicts decision certainty in behavior.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1971.


“Monkeys almost always accepted offers with large rewards and short delays, and almost always rejected offers with small rewards and long delays.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1971.


“We also analyzed whether decision consistency was reflected in reaction time.... We found that the average reaction time correlated significantly with decision consistency for each offer in reject decisions. Reaction times were slower when decision consistency was smaller.. This correlation may not entirely reflect decision consistency, but rather motivation.... To control for the effect of motivation, we performed an additional combined regression analysis.... This further suggests that decision consistency is reflected in reaction times, after controlling for motivation.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1972.


“However, neural activity is also subject to noise, and the noise can drive the neural activity out of one basin and into another. Our hypothesis, therefore, suggests that deeper attractor basins will lead to more consistent decisions because variability in neural activity is less likely to stochastically drive population activity out of the deeper basins.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1974.


“The steepness of attractor basins predicts reaction time.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1975.


“In this paper, we investigated the neural underpinnings of choice consistency by analyzing dynamics in prefrontal population activity during decision making. We found that attractor basins were shallower following cues that signaled intermediate value offers, which also led to lower decision consistency. Correspondingly, attractor basins were deeper following cues that signaled high or low value offers, that led to higher decision consistency.... Our results provide neural evidence that attractor dynamics predict decision consistency.” Wang, Siyu, Rossella Falcone, Barry Richmond & Bruno B. Averbeck. 2023. “Attractor dynamics reflect decision confidence in macaque prefrontal cortex.” Nat. Neurosci. 26(11):1970-1980. 10.1038/s41593-023-01445-x. [5; unsure page numbering] [5] p. 1977.


“In this paper, we employ GARD computer simulations to quantitatively explore network reproduction behavior. We provide concrete evidence that reproducing states of molecular networks constitute compositional dynamic attractors. We argue that such attractor characteristics may have greatly enhanced the chance of appearance of supramolecular reproducing entities under chaotic prebiotic settings, thus augmenting the probability of life’s emergence.” Kahana, Amit, Lior Segev & Doron Lancet. 2023. “Attractor dynamics drives self-reproduction in protobiological catalytic networks.” Cell Reports Physical Science. 4:101384. 10.1016/j.xcrp.2023.101384. [3] p. 2.


“Based on quantitative kinetic simulations, we have been able to provide concrete formal evidence that a reproducing state of a catalytic network is a dynamic attractor. Studies of life and lifelike complex networks suggest that their dynamic fates may represent attractors and that changes in the behavior of a living cell could be interpreted as switching among attractor basins....

“The significance of our analyses is in showing that attractor dynamics may push a system toward reproduction.” Kahana, Amit, Lior Segev & Doron Lancet. 2023. “Attractor dynamics drives self-reproduction in protobiological catalytic networks.” Cell Reports Physical Science. 4:101384. 10.1016/j.xcrp.2023.101384. [3] p. 11.


“Integrator and discrete attractor models can explain many aspects of neuronal activity and have been proposed to underlie short-term memory and decision making....

“We found that membrane potential and spiking dynamics funneled towards discrete endpoints and that these dynamics were robust to optogenetic perturbations. Occasionally, perturbations caused switches from one trajectory to the other, followed by incorrect choices. These data are inconsistent with integrator models, but are consistent with discrete attractor dynamics underlying short-term memory.” Inagaki, Hidehiko K., Lorenzo Fontolan, Sandro Romani & Karel Svoboda. 2019. “Discrete attractor dynamics underlies selective persistent activity in the frontal cortex.” Nature. 566:212-217. 10.1038/s41586-019-0919-7. [3; unsure page numbering] p. 212.


“In a system following discrete attractor dynamics, activity is expected to converge to discrete endpoints over time (funneling). In contrast, in a system following integrator dynamics, funneling is not expected. During the delay epoch, Vm [membrane potential voltage] funneled to a narrow distribution at the time of movement onset.” Inagaki, Hidehiko K., Lorenzo Fontolan, Sandro Romani & Karel Svoboda. 2019. “Discrete attractor dynamics underlies selective persistent activity in the frontal cortex.” Nature. 566:212-217. 10.1038/s41586-019-0919-7. [3; unsure page numbering] p. 213.


“We performed a series of experiments to probe the mechanisms underlying persistent preparatory activity in ALM [anterior lateral motor cortex]. First, membrane potential dynamics and modulation of membrane potential were inconsistent with cell-autonomous mechanisms as a primary mechanism for persistent activity. Second, during the delay epoch, activity funneled toward two discrete endpoints, both at the level of membrane potential and spike rate, consistent with discrete attractor dynamics, but not with integrators. Third, after perturbations, detailed activity trajectories recovered to reach one of the two discrete endpoints, again consistent with discrete attractor dynamics. Fourth, when delay duration was randomly varied, activity during the delay epoch was approximately stationary, showing that ramping is not a necessary component of preparatory activity. These experiments provide direct evidence for discrete attractor dynamics as a mechanism underlying short-term memory.” Inagaki, Hidehiko K., Lorenzo Fontolan, Sandro Romani & Karel Svoboda. 2019. “Discrete attractor dynamics underlies selective persistent activity in the frontal cortex.” Nature. 566:212-217. 10.1038/s41586-019-0919-7. [3; unsure page numbering] p. 215.


“Altogether, we propose that flexible yet robust discrete attractor dynamics subserves short-term memory in frontal cortex in a wide-range of behaviors.” Inagaki, Hidehiko K., Lorenzo Fontolan, Sandro Romani & Karel Svoboda. 2019. “Discrete attractor dynamics underlies selective persistent activity in the frontal cortex.” Nature. 566:212-217. 10.1038/s41586-019-0919-7. [3; unsure page numbering] p. 215.


“Ironically, over the past years, the same sequencing technology has exposed new cracks in the edifice of the cancer genetics paradigm.... Some sequencing results are compatible with the postulate of oncogenic mutations, but many inconvenient findings are overlooked for lack of critical discourse, resulting in an unbalanced view. Thus, an open discussion of the growing sequencing data that are contradictory to the genetic paradigm is due. The (apparent) paradox goes both ways: many cancers harbor no consistent driver mutations, while canonical oncogenic mutations are found in tissues that remain free of cancer.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 2.


“Since one should not postulate the end of a paradigm without specifying the ‘alternatives’ that may correct or replace it, we will discuss here two sets of ideas that have been sidelined by the dominance of the genetic paradigm of cancer. First, that ‘cancer is not a disease of the genes’, but of gene regulation and thus, of the cell. We must consider the dynamics of the collective action of genes in a network that governs cell behaviors. Second, that ‘cancer is not a disease of the cell’, but of tissues; we must consider principles of tissue organization.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 6.


“‘Normalization’ is perhaps the most prosaic anomaly to the mutation paradigm. When Peter Nowell popularized clonal evolution of cancer cells, Beatrice Mintz’s group presented evidence that teratocarcinoma cells injected into mouse embryos gave rise to cancer-free chimeric mice in which the mutated cancer cells were present in most organs. Such reversion of the malignant phenotype (i.e., normalization) within a proper tissue context has been reproduced in a variety of animal models.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 7; references: Nowell, Peter. 1976. “The clonal evolution of tumor cell populations.” Science. 194(4260):23-8. 10.1126/science.959840; 149: Mintz, B. & K. Illmensee. 1975. “Normal genetically mosaic mice produced from malignant teratocarcinoma cells.” PNAS USA. 72(9):3585-9. 10.1073/pnas.72.9.3585.


“The central idea, as can be formulated mathematically, is that in coordinating the activities of genes, the GRN [gene regulatory network] tends to interlock them in distinct, self-stabilizing gene expression configurations, called attractor states. A key postulate, formulated in 1969 by Stuart Kauffman, is that attractors represent the gene expression patterns that correspond to cell types (or functional cell states). Being attractor states, such gene expression patterns are robust to perturbations; they re-establish spontaneously after disturbances, and thus can be inherited across cell generations. The existence of multiple attractor states is a feature of a particular class of complex dynamical systems to which GRNs belong.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 8; reference: Kauffman, S. 1969. “Homeostasis and differentiation in random genetic control networks.” Nature. 224(5215):177-8. 10.1038/224177a0.


“Now, here is a crucial corollary: on the epigenetic landscape of a given GRN, there exist, for mathematical and network-evolution reasons, many more attractor states than are occupied by cells in the healthy adult organism. The phenotype of these unused attractors has been proposed to represent malignant cells; in this view, cancer is a possibility immanent to metazoans, and hence is less suited to be seen as ‘being caused by something’, but rather is primarily the ‘unleashing of something latent’. Carcinogenesis would then be the accidental entry into these unused latently present attractors, which thus have been referred to as ‘cancer attractors’.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 10.


“A compelling illustration of the need for a tissue-based rather than cell-centric view is the finding that while a bulk injection of hepatocarcinoma cells into the liver generates a liver tumor, injection of the same amounts of the same cell type into the spleen, which distributes transplanted cells individually throughout the liver, fails to generate a tumor.... These observations led to the concept that there is no ‘cancer cell’ because cells declared as such (e.g., because of canonical oncogenic mutations) behave as normal cells.

“Instead, cancer can be broadly understood as ‘development gone awry’. Within this perspective, the tissue organization field theory is based on two principles that unite phylogenesis and ontogenesis. Firstly, the default state of all cells is constitutive proliferation with variation and motility. Therefore, proliferation and motility do not require an explanation; instead, what ought to be explained is why cells do not proliferate and move. Secondly, cancer is a tissue-based disease, whereby the tissue organization constraints to the default state of its cells are weakened. Consequently, cells become freed to express their default state, thus proliferating, generating variation and moving. This explains tumor growth by accrual of new cells, as well as invasion and metastasis.” Huang, Sui, Ana M. Soto & Carlos Sonnenschein. 2025. “The end of the genetic paradigm of cancer.” PLOS Biology. 23(3):e3003052. 10.1371/journal.pbio.3003052. [4] p. 14.


“An attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long-term memory, short-term memory, attention, and decision making. The article then shows how the random firing of neurons can influence the stability of these networks by introducing stochastic noise, and how these effects are involved in probabilistic decision making, and implicated in some disorders....” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 119.


“An important and useful property of these attractor networks is that they complete an incomplete input vector, allowing recall of a whole memory from a small fraction of it. The memory recalled in response to a fragment is that stored in the memory that is closest in pattern similarity. Because the recall is iterative and progressive, the recall can be perfect.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] pp. 120-1.


“An attractor network trained with patterns that are continuous with each other can maintain the firing of its neurons to represent any location along a continuous physical dimension such as spatial position, head direction, etc. and is termed a Continuous Attractor neural network.... Attractor networks can operate with both continuous and discrete patterns, and this is likely to be important in episodic memory, in which typically a spatial position (e.g., a place) and discrete object-related information are components.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 122.


“Attractor networks appear to operate in the prefrontal cortex, an area that is important in attention and short-term memory....” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 122.


“The prefrontal attractor can be stimulated into activity by the first stimulus when it is inactive, but once in its high firing rate attractor state, it is relatively stable because of the internal positive feedback, and is not likely to be disturbed by further incoming stimuli.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 123.


“It is hypothesised that some of the cognitive symptoms of schizophrenia, including poor short-term memory and attention, can be related to a reduced depth in the basins of attraction of the attractor networks in the prefrontal cortex that implement these functions.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 127.


“The neocortex has local recurrent collateral connections between the pyramidal [neurons] that achieve a high density only for a few millimetres across the cortex. It is hypothesised that this enables the neocortex to have many local attractor networks, each concerned with a different type of processing, short-term memory, long-term memory, decision making, etc. This is important, for recall that the capacity of an attractor network is set to first order by the number of connections onto a neuron from other neurons in the network. If there were widespread recurrent collateral connections in the neocortex so that the whole neocortex operated as a single attractor, the total memory capacity of the neocortex would be only that of a single attractor network (of order thousands of memories), and this possibility is thus ruled out.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 130.


“However, it has been suggested that one network in the brain, the hippocampal CA3 network, does operate as a single attractor network. Part of the anatomical basis for this is that the recurrent collateral connections between the CA3 neurons are very widespread, and have a chance of contacting any other CA3 neuron in the network. The underlying theory is that the associativity in the network allows any one set of active neurons, perhaps representing one part of an episodic memory, to have a fair chance of making modifiable synaptic contacts with any other set of CA3 neurons perhaps representing another part of an episodic memory. This widespread connectivity providing for a single attractor network means that any one part of an episodic memory can be associated with any other part of an episodic or event memory.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] p. 130.


“I propose that attractor networks are fundamental design features of the neocortex and hippocampal cortex. In the neocortex the attractor networks are local and therefore there can be many of them. They allow many items of information to be held on-line, and thus provide the basis and/or underpinning for powerful computations that require short-term memory, working memory, planning, attention, and even language.” Rolls, Edmund T. 2009. “Attractor networks.” WIREs Cognitive Science. 10.1002/wcs.1. [4] pp. 130-1.


“Neuronal ensembles are defined here as a group of neurons that display recurring patterns of coordinated activity. We argue that ensembles are endogenous building blocks of neural circuits, which act as modular functional units.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 875.


“Hebb also hypothesized that assemblies could be triggered by the activation of a subset of key neurons, thus generating ‘pattern completion,’ i.e., the ability of a part of a system to activate the whole. Pattern completion is a hallmark of memory retrieval and of many brain functions, including speech, motor behavior, emotions, and cognition.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 875; reference: Hebb, D.O. 1949. The Organization of Behaviour. Wiley.


“Hopfield predicted that, similarly to magnetic states, strongly coupled neurons in recurrently connected networks naturally form stable activity states, which he called ‘attractors,’ as they ‘attract’ the activity of the population.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 875; reference: Hopfield, J.J. 1982. “Neural networks and physical systems with emergent collective computational abilities.” PNAS USA. 79:2554-2558.


“Independently, Abeles also reached the conclusion that cortical function must be organized as groups of synchronously active neurons. Realizing that most cortical synapses are weak and stochastic and have short-term depression dynamics, and the fact that the action potential threshold imposes a strong non-linearity in the activation function of the neuron, Abeles proposed that the only way for neuronal activity to propagate through the cortex was via coactive groups of neurons. These ‘synfires chains,’ another conceptualization of the idea of an ensemble, would sequentially activate each other, forming temporal chains of synchronous activity.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] pp. 875-6; reference: Abeles, M. 1991. Corticonics. Cambridge UP.


“Thus, these groups of coactive neurons can occur with or without external sensory stimulus, and their reproducible patterns of neural activation can be categorized as circuit attractors. It is important to note that the neurons of an ensemble could also be involved in different types of activity, unrelated to the group.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 876.


“Besides ensembles, other terms have been used to describe patterns of coordinated neural activity, such as assemblies, attractors, reverberations, synfires, domains, oscillations, trajectories, clusters, groups, packets, domains, flashes, songs, bumps, avalanches, trajectories, and states, among others.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 877.


“Although there is no set rule, most studies define ensembles as groups of neurons that are repeatedly activated within a period of a few milliseconds to a few seconds. In addition, individual neurons can join different ensembles, which enables combinatorial, compositional, and hierarchical arrangement of ensembles, just like in other emergent systems.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 877.


“Importantly, hippocampal ensembles can be causally linked to behavior.... This suggests that hippocampal ensembles could also function as engrams. Experimentally, engram cells are often defined as groups of neurons expressing immediate early genes following a given experience, that when reactivated, reproduce the behavior observed during that experience.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 877.


“In summary, cortical ensembles echo many of the properties from hippocampal ensembles, including their sequential structure and endogenous nature, as they can occur during ongoing activity but be recruited by behavioral tasks or evoked by sensory information.” Yuste, Rafael, Rosa Cossart & Emre Yaksi. 2024. “Neuronal ensembles: Building blocks of neural circuits.” Neuron. March 20. 10.1016/j.neuron.2023.12.008. [4] p. 879.


“What has been increasingly recognized is that the noise in cognition is often not noise added in either the sensory or response systems [input or output systems] but the noise present in the cognitive computations themselves. In addition, it has been shown that noise is often not Gaussian, not independent, and indeed has other interesting structure.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 573.


“Not only is noise unavoidable and interesting to study, but the presence of noise in cognition may well be essential to cognitive functioning–allowing a local sampling algorithm to explore alternative hypotheses about the world.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 573.


“Response noise is variability introduced after the important cognitive computations....” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 575.


“In contrast to sensory and response noise, computational noise is noise arising from the cognitive operations that map from sensory input to responses.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 575.


“... sensory and response noise, either individually or combined, are insufficient to explain the noise in human behavior, and therefore a substantial proportion of the noise has to occur in the cognitive computations instead. We also have seen that noise does not have a simple form and instead is non-Gaussian and shows intricate dependencies.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 579.


“Each of these existing frameworks has had success in explaining specific results based on noise in cognitive computations, but is a more general explanation of noise possible? One principled route to producing such a general explanation is to start with probabilistic models about the world, such as Bayesian or ideal observer models, but to acknowledge that the brain cannot possibly apply such probabilistic models using exact symbolic calculations using the mathematics of probability theory because these would be computationally intractable. This means an approximation is needed, and one of the most widespread approaches to approximation in computational statistics and machine learning is sampling.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 580.


“Sampling typically involves using some variant of so-called Markov chain Monte Carlo (MCMC) sampling, a sampling algorithm that starts with a particular hypothesis (e.g., this image shows a cheetah) and makes stochastic and local changes to the hypothesis (e.g., this image shows a leopard). When implemented correctly, this algorithm generates samples from the probability distribution over possible hypotheses while remaining psychologically more plausible than representing the entire probability distribution.

“This viewpoint postulates that the brain will roughly follow Bayesian probability theory but will be subject to systematic biases resulting from computational constraints on cognition, limiting the number of samples drawn. These biases will arise in a variety of ways: First, small samples will depend on their starting point (because the choice of starting point will only ‘wash out’ after a large sequence of samples has been drawn, so that the entire probability space has been explored); this dependence on starting point has been argued to account for effects such as anchoring and adjustment, sub- and superadditivity, and causal reasoning errors.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 580.


“... the view of noise has not always been negative; indeed, some researchers studying noise have identified situations in which it could be useful. The most obvious case is in competitive contexts in which being unpredictable is important (e.g., selecting where to kick a soccer ball in a penalty shootout), but the noise can also be useful in the individual tasks we discuss here....

“But we argue that noise may better be viewed as a feature rather than a bug, even outside of the restricted scenarios discussed above. Our specific proposal is that the brain manages the probabilistic inference required to deal with a highly uncertain world through sampling–and this process of sampling is, by its very nature, noisy. Indeed, when we view the brain as a Bayesian sampler, the noisiness of human thought and behavior is not a failure to be ironed out but central to the basic operation of human cognition.... ... it [noise and sampling] allows the brain to consider only one or a small number of hypotheses at a time and with probabilities proportional to the relative time spent considering each hypothesis. This is more psychologically plausible than implementing exact Bayesian inference and is congruent with psychological ideas about considering a single hypothesis at a time.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 584.


“Even when the most probable hypothesis has been found, noise can still play a useful role. Noise allows for integrating over hypotheses that point toward the same action because the most likely hypothesis may not necessarily point to the most likely action. For example, there may be a crowd of geese running across the road, and although it may be that driving through a gap without hitting any geese with your car is most likely, integrating over all the possible errors in perception and execution means the right decision is to stop.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 585.


“If this viewpoint is right, then the ‘noisy’ nature of cognition arises from its very essence. If perception and cognition involve probabilistic inference, and that inference is carried out through sampling, then when asking a question again, different samples will, of course, generate different outcomes. Thus, we should expect human thought and behavior to be variable through and through as a reflection of the computational noise that drives the basis ‘engine’ of cognition. Not only could one say that noise in cognition is a feature and not a bug but even that it is an essential feature, one that underpins our ability to deal with an uncertain world of such complexity that precise analysis is computationally impossible.” Sanborn, Adam, N., Jian-Qiao Zhu, Jake Spicer, Pablo Leon-Villagra, Lucas Castillo, Johanna K. Falben, Yun-Xiao Li, Aidan Tee & Nick Chater. 2025. “Noise in Cognition: Bug or Feature?” Perspectives on Psychological Science. 20(3):572-589. 10.1177/17456916241258951. [5] p. 585.


“In this review, we will describe and evaluate the hypothesis that attractor dynamics in widespread regions of the central nervous system play a key role in constructing some of these representations, generating long time-scales to support integration and memory functions, and endowing all these functions with robustness. We will review the specific predictions of attractor-based models and the now-extensive body of work testing these predictions. Thus, we will illustrate that the theory and validation of computation with attractor dynamics in the brain is one of the biggest success stories in systems neuroscience.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 744.


“Attractors exist in various flavors: an attractor may consist of a single state or a set of states that trace out a complex shape, such as a curved manifold. States on an attractor may be stationary, or might flow along the attractor to trace out trajectories that are periodic or chaotic. Various combinations of such attractors, of different dimension, geometry, and topology, may coexist in different regions of the state space of a single dynamical system. Typically, the set of attractors in a dynamical system comprises a small subset of the state space, and attractor manifolds are usually much lower-dimensional than the state space. In cases where a system has multiple attractor states, the initial condition determines the attractor state to which the system flows.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 745.


“In a nutshell, the critical signatures of attractors in real systems can be summarized as: localization of the states of a system to a lower-dimensional subset, flow of the states towards the subset after perturbation, and long-time and (effectively) autonomous stability of states in that subset.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 745.


“The general principle underlying the formation of non-trivial attractor states in neural circuits is strong recurrent positive feedback. Positive feedback fights activity decay to stabilize certain states, and has been conjectured by James, Hebb and others as the basis for the stabilization of memory traces and persistent activity in the brain.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 746.


“The general principle for the formation of stationary continuous attractors is pattern formation. Simple and spatially local competitive interactions lead to the emergence of rich stable spatial activity patterns – neurons with excitatory coupling between them become co-active, and suppress the rest of their neighbors through inhibition – a linear instability.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 746.


“Large non-symmetric (and nonlinear) networks with strong connectivity generically exhibit limit cycle attractors or chaotic dynamics. Just as point attractors emerge generically in large networks with strong symmetric weights and bounded state spaces, chaotic attractors emerge generically in large recurrent networks with strong asymmetric weights.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 746.


“The fundamental predictions of attractor models center on the state-space dynamics of the circuit, at first explicitly discussed and tested in a few papers: 1) That the system’s states should be found localized at or around a much lower-dimensional set of states corresponding to the attractors in the state space. 2) That perturbations of the system should flow quickly back to the low-dimensional states. 3) That the set of attractor states – quantified by either direct characterization of the full state space or by the relationships between cells – should be invariant, persisting over time and after removal of tuned input, across conditions, across behavioral states, and even when there are induced variations in the mapping from internal states to external inputs. 4) Integrator networks should further exhibit the property of isometry, in which lengths of coding space along a dimension are allocated to equal displacements along a dimension of the external variable. 5) Additional predictions of attractor dynamics models, that are not as fundamental in the sense that they are not theoretically necessary or sufficient but are nevertheless of high significance because they are highly supportive of the mechanisms of attractor dynamics, are anatomical and structural correlates: the existence of low-dimensional structures and symmetries in connectivity between cells.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 748.


“As we illustrate next, theoretically-motivated analyses of population data have now firmly established that low-dimensional attractor dynamics are ubiquitous in the brain, across levels in the brain’s hierarchy and across species.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 749.


“Visual and auditory percepts including binocular rivalry, the Necker cube, and some auditory illusions offer clear examples of bistability in neural processing. In these illusions, the brain selects one possible interpretation of an ambiguous input, often switching between possibilities. Though the phenomenon has long been known and studied, no localized circuit has been identified as the basis of perceptual bistability.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 749.


“To date, there are somewhat less direct data and exhaustive analyses to establish discrete multistability as a circuit-level brain process, in comparison to the evidence for continuous attractor networks. However, there are many likely candidates systems and brain regions with dynamics suggestive of and consistent with discrete multistability, at least of the special case of WTA [winner-take-all] attractor dynamics, including in mammalian hippocampus and auditory cortex, and the fly and mammalian olfactory system.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 749.


“The oculomotor integrator, together with the HD [head-direction] circuit, was one of the first systems in neuroscience to be studied theoretically and experimentally as a continuous attractor network – specifically as a line attractor. This network, presynaptic to the motor neurons that control horizontal eye position, is highly conserved across vertebrates, from fish to primates. It integrates pulse-like saccadic eye movement command signals to generate step-like stable muscle tension command signals that persist autonomously at various graded activity levels after removal of the movement cue and even in the dark in the absence of visual feedback and thus enable stable gaze fixation at various eccentricities.... Remarkably, the same system also integrates smooth head velocity signals to permit gaze stabilization during head movement.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 750.


“Strikingly, all established stationary continuous attractor networks in the brain are also integrators.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 753.


“The theory of attractor dynamics in the brain has provided a powerful and unifying conceptual framework for understanding integration, representation, memory, error-correction, and efficient learning and inference in the brain. The experimental effort to study candidate attractor circuits and test their predictions has been a fertile field of research, and population-wide physiology techniques have led to breathtaking direct visualizations of attractor dynamics at work in the brain.

“The theory is also proving to be a powerful tool in interpreting how artificial neural networks (ANNs) solve complex tasks. ANNs trained to robustly solve memory, integration, and decision-making tasks in domains as diverse as spatial navigation, vision, and language, develop attractor dynamics, suggesting that not only are attractor networks able to solve such problems but might be necessary when the computing elements are memoryless neurons.” Khona, Mikail & Ila R. Fiete. 2022. “Attractor and integrator networks in the brain.” Nature Reviews Neuroscience. 23:744-766. 10.1038/s41583-022-000642-0. [5; unclear page numbering] p. 754.


“If the stimulus has led the ANN [artificial neural network] into an attractor rapidly, then the read-out neuron will fire and the biological system will be able to proceed in the special way, appropriate for the recognition of that particular stimulus. When the action takes place on the level of retrieval, this is the end result. But since both the attractor and the read-out mechanisms are proposed as universal mechanisms, this reasoning can be repeated on higher levels of processing, where ANN’s may deal with various compositions of sensory inputs, together with inputs from other cortical components. Again, the point of view advocated here is that any cognitive operation must end up with a rapid drift toward an attractor, a fact which can be recognized by the corresponding read-out cells.

“The fact that resident cells can recognize special dynamical sequence and lead to biological function (such as motor response) is freedom from homunculus.

“In other words, the neutral fact that the network enters a repeating pattern of neural activities is the signal that a cognitively significant event is taking place, without the observing eye of an all knowing little person.” Amit, Daniel J. 1989. Modeling Brain Function: The World of Attractor Neural Networks. Cambridge UP. p. 41.


“A general characteristic of biopolymer backbones that contributes to polyfunction is the capacity to fundamentally remodel structural and functional landscapes via extremely subtle chemical changes.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 663.


“Molecular complementarity within and between biopolymers contributes to fine control of structure and function. The polypeptide backbone is intrinsically self-complementary, as seen in the matched hydrogen bonding donor/ acceptor arrays of α-helices or β-sheets. Polyglucose is self-complementary, as seen in assemblies of amylose, cellulose, and many other assemblies.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 663.


“One of the most astounding proficiencies of biopolymers is their ability [to?] control their own destinies by manipulating kinetic trapping and thermodynamic stability. The extent and type of biopolymer assembly modulates chemical lifetimes in ways that are not predicted by ΔG(r)‡(int) [intrinsic activation free energy for hydrolysis; “r” is for reverse direction of hydrolysis]. To describe this phenomena in general, we appropriated the term recalcitrance and define it as a general tendency of assembly to increase chemical lifetimes (persistence).” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 663.


“Biopolymers fall on a continuum; some biopolymers maintain reduced reactivity in assemblies while others are essentially unreactive in assemblies.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] pp. 663-4.


“Nucleic Acids are incredibly sophisticated in that they appear to have the greatest range and control of persistence.... Using simulation and experiment we validated a Goldilocks model of RNA recalcitrance.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 664.


“Biopolymers can shelter and protect each other. Nucleic acids are recalcitrant when bound by proteins. Hetero-recalcitrance is the basis of enzymatic and chemical footprinting of DNA-protein or RNA-protein complexes.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 665.


“In contrast to enzymes, recalcitrance can decrease a reaction rate in one direction without affecting the rate in the reverse direction. Recalcitrance increases thermodynamic stability and modulates reactivity in one direction only.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 665.


“A mutualism is reciprocal exchange; a species proficient in obtaining certain benefits confers those on a second species, which reciprocates by conferring different benefits on the first species. Mutualisms are everywhere in the biosphere and are fundamentally important in ecology. All species on Earth participate in mutualisms. Mutualisms can increase productivity, abundance, and temporal stability of both mutualists and nonmutualists in food webs. Mutualisms (i) sponsor coevolution, (ii) foster innovation, (iii) increase fitness, (iv) inspire robustness, (v) are resilient and resistant to change, and (vi) involve partners that are distantly related with contrasting yet complementary proficiencies.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 665.


“The formalisms describing mutualisms on levels of cells, organisms, and ecosystems apply equally well to molecules. For example, biopolymers are synthetically interdependent. RNA synthesizes protein in the ribosome and protein synthesizes RNA in polymerases. Mutualisms describe heterorecalcitrance. By forming assemblies, biopolymers protect each other from chemical assault. Proteins and peptides promote folding and functions of RNA and vice versa. Mutualisms describe protein-based pores and pumps in bilayer compartments. A cell can be understood as a consortia of molecules in mutualism relationships; an Amazon Jungle of molecules. Mutualisms drive coevolution, thereby resolving ‘chicken and egg dilemmas in the chronology of RNA and protein origins.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] pp. 665-6.


“In evolutionary models of proposed here [sic], molecular mutualisms predate biopolymers. In these models, mutualisms were important among molecular ancestors of DNA, RNA, protein and polysaccharides, providing mechanisms of biopolymer coevolution.... We hypothesize that ancestral mutualisms involved heterorecalcitrance, chaperoning of folding or solubility, catalysis and autocatalytic cycles.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 666.


“Evolution gives rise to emergence. The products of evolution are always interdependent multicomponent systems that exhibit emergence, where system properties differ fundamentally from the properties of isolated system components. Emergence can be envisioned as passage through a metaphorical door; when a system transitions to a new emergent state, new rules materialize. Emergence gives rise to complex functions that are not evident in the isolated parts of the system. The ribosome, the spliceosome, and the mitochondrion are creative inventions of evolution that demonstrate emergence.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 667.


“Each biopolymer is an emergent molecule. The structures, functions, and properties of biopolymers are different from those of the monomeric building blocks. Monomeric amino acids do not self-assemble into enzymes, fibers, compartments, or motors. Those assemblies are emergent on polymerization. Similarly, the structures and functions of polysaccharides cannot be achieved by monomeric sugars, as glucose alone does not form fibers, helices, or dendrites. The same holds true for RNA; monomeric nucleotides in aqueous solutions do not spontaneously form base pairs. Each type of biopolymer behaves differently from its nonpolymerized constituents, consistent with predictions of creation through evolution. The emergent properties of biopolymers are evidence for their creation via evolutionary processes.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] p. 667.


“Our working definition of chemical evolution is continuous chemical change with exploration of new chemical spaces and avoidance of equilibrium.” Matange, Kavita, Eliav Marland, Moran Frenkel-Pinter & Loren Dean Williams. 2025. “Biological Polymers: Evolution, Function, and Significance.” Accounts of Chemical Research. 58:659-772. 10.1021/acs.accounts.4c00546. [6] pp. 667-8.


“Neuroscience seeks to answer the following central question: How does the brain generate behavior? Broadly speaking, there are three types of study: lesion, activity, and manipulation.... Activity studies measure brain signals. The classic technique is to insert a microelectrode into the tissue of interest.... Manipulation studies directly alter the state of the brain by either silencing or enhancing signals. Again, the goal is to see how sensations and actions are affected.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 6, 7.


“... we can think of the brain, with all its different parts, as evolution’s solution to the problem of uncoupling inputs from outputs. Without this flexibility, animals are bound to perish.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 34.


“As stated, rats freeze in response to novel stimuli in unfamiliar environments more so than in familiar places. Though intuitive, this observation reflects a fundamental principle of brain function–context sensitivity. The brain does not simply react to sensory stimuli; instead, incoming data are incorporated into ongoing processing that encompasses the states of the brain and the body, explaining why the exact same stimulus exerts very different effects depending on the situation: in one setting a stimulus may lead to inquisitive approach, in another to moving away.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 37-8.


“But the reader should remember that we cannot simply point to a brain structure and say that a behavior resides there. Instead, a central thesis of this book is that anatomically distributed circuits bring about the behaviors in question. (Even these distributed circuits need to be understood in terms of a fully behaving animal immersed in a broader context.)” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 45.


“To infer the existence of separate mental processes, researchers rely on the logic of dissociations. Consider, first, a single dissociation. Let A and B be two tasks (say, one involving verbs, another involving nouns) and let m be a ‘manipulation.’ A single dissociation is observed if m affects performance on A but not on B....

“The logic of dissociation is central to neuroscience and has long been used to localize mental functions.... ... in the 1950s investigators started to question the single dissociation’s application. For example, in some cases it may well be that general deficits following a lesion could explain the pattern of results; perhaps the lesion impairs most tasks that are difficult, and task A happens to be harder than task B....

“The inconclusiveness of the methodology motivates the double dissociation logic. A single dissociation is observed if region 1 affects performance on task A but not on task B. A double dissociation is observed if, in addition, region 2 affects performance on B but not on A.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 50, 51.


“Indeed, reductionism is the declared philosophy of most scientists. Reduce everything to the smallest parts, determine their properties, and you explain the whole system.... As developed throughout the book, I believe such an approach provides at best an impoverished description of brain function, as most of the explanatory work needs to be done at the level of interactions. Unfortunately, neuroscience as a discipline is all too reductionist.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 54.


“The peripheral nervous system contains the parts of the nervous system other than the brain and spinal cord. The autonomic nervous system, in particular, consists of the neurons that innervate the internal organs, the bloods vessels, and the glands. Its sympathetic subdivision tends to be most active during a crisis, sometimes indicated by ‘fight, flight, fright, and sex’. The parasympathetic division facilitates digestion, growth, immune responses, and energy storage. In most cases, the activity of the two divisions is reciprocally related; when one is up, the other is down.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 67.


“It [knowledge of the hypothalamus] participates in complex homeostatic mechanisms and contributes to neuroendocrine outputs affecting brain and body glands. And it contributes to wide-ranging processes: circadian rhythms, wakefulness and sleep, stress responses, temperature regulation, food intake, thirst, sexual behaviors, and defensive behaviors. This is a staggering list of critical functions for such a small structure....” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 70.


“More generally, instead of outflow or inflow, it’s best to characterize areas in terms of integration and distribution of signals: the more it has incoming pathways, the more it can integrate signals; the more it has outgoing pathways, the more it can distribute them.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 71.


“If an experiment effectively ‘uses up’ attention by making one condition very challenging, one’s ability to process other things (even an abrupt visual onset) is pretty much eliminated–much like a driver will miss a crossing pedestrian right in their line of sight if consumed by their phone....

“So, while emotion-laden stimuli are clearly more potent than neutral ones, they are not so strong as to be processed ‘no matter what.’” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 82, 83.


“Although not automatic, visual processing of emotion-laden stimuli is quite remarkable. Areas in the occipital and temporal cortex that process visual attributes are strongly engaged by emotion-laden stimuli. That is to say, when the brain processes visual content with emotional significance, the visual cortex responds more vigorously. It is as if the ‘volume’ of the stimulus is turned up when it is emotional, with a lot of visual cortex reflecting this.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 83.


“Selection of information for further analysis is a key problem that needs to be solved for effective learning and arguably many other behaviors. How can a limited-capacity information processing system that receives a constant stream of diverse inputs–such as the nervous system–be designed to selectively process those inputs that are most significant to the objectives of the system? The amygdala seems to be intimately involved in solving this problem. Put another way, the region cares about selective information processing.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 84.


“For James, emotion did not depend on separate processes specially devoted to this mental faculty. Instead, it was tied to the changes that occur in the body during a triggering event, such as in his famous example of encountering a bear in the woods. For him, then, the feeling of the changes in the body that follow an ‘exciting fact,’ as they occur, is the emotion.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 94.


“We are in constant synergy with our surroundings....” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 106.


“Imagine a page with words used for colors, such as ‘red,’ ‘green,’ and so on, written in black ink. Now imagine writing the words with pens of different colors, including one that matches the word and one that doesn’t–for example, the word ‘red’ written in red ink (called congruent) or the word ‘red’ written in green ink (called incongruent). Your task is to tell the color of the ink used to write the word. If the word’s name doesn’t match the ink color, the task feels somewhat harder than it should be. In the Stroop task, the participant is asked to either read the word or to name the color in which it is written in different trials. Every stimulus contains two properties (word meaning and color)....

“The Stroop task is called a conflict task because during incongruent trials one is asked to focus on a particular dimension (color) in the presence of competing information (word meaning). Both detecting the presence of conflict and resolving it are important cognitive functions.... But if a habitual action is uncalled for, it should be possible to recalibrate it, modify it, or call it off completely. That’s when cognitive control comes into play....

“The Stroop task illustrates a fundamental aspect of cognitive control and goal-directed behaviors: the ability to select a weaker but task-relevant response (or source of information) in the face of competition from an otherwise stronger but task-irrelevant one. Researchers believe that this is the central contribution of the prefrontal cortex, which allows adherence to goals in the presence of competing, stronger actions.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 121, 122.


“According to the traditional view, vision is relatively passive, like a camera pointed at the world, clicking away. In the predictive framework, vision is active and guided by endogenous computations that try to anticipate the most valuable future information for the animal....

“The upshot is that brains are not passive. When a stimulus is processed, it does not encounter a tabula rasa. Instead, it is registered against a host of expectations constructed from prior experience, leading to the idea of a matching process between incoming information and feedback ‘template’ signals. The template represents the system’s predictions of the input and is updated to reflect the animal’s past history. Despite differences, predictive-brain approaches share a key concept: the brain doesn’t reconstruct the external world but constructs a version of it.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 125, 126.


“Thus, one definition of emergence is as follows: a property that is observed when multiple elements interact that is not present at the level of the elements.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 134.


“The study of species coexistence focuses almost exclusively on pairs of competitors so that when considering large groups of plants or animals, the strategy is to look at all possible couples. For example, one studies three pairs when three species are involved, or six pairs when four species are considered, or more generally, n(n-1)/2 interactions between n species. Do we lose anything when examining only pair-wise interactions? Higher-order interactions are missed, as when the effect of one competitor on another depends on the population density of a third species or an even larger number of them.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 138.


“Recurrent networks, where connections can be both feedforward and feedback, are more interesting in the context of complex systems. In this type of organization, at least some connections are bidirectional and the systems can exhibit a range of properties. For example, competition can occur between parts of the network, with the consequent suppression of some kinds of activity and the enhancement of others. Interested in this type of competitive process, in the 1980s, Stephen Grossberg ... developed Adaptive Resonance theory. In the theory, a resonance is a dynamical state during which neuronal firings across a network are amplified and synchronized when they interact bidirectionally–they mutually support each other.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 140; reference: Grossberg, S. 2021. Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. Oxford UP.


“A contrasting view [from viewing the brain as a decentralized collection of parts] favors distributed processing through interactions of multiple parts. Accordingly, instead of information flowing hierarchically to an ‘apex region’ where all the pieces are integrated, information flows in multiple directions without a strict hierarchy. An organization of this sort is termed a heterarchy to emphasize the multidirectional flow of information.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 143.


“Modern research on brain anatomy from a comparative viewpoint indicates, in contrast, that brain evolution is better understood in terms of the reorganization of large-scale connectional systems.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 153.


“Now, I will outline a framework for thinking about large-scale brain circuits that I’ve called functionally integrated systems. Before doing so, I will outline five broad principles of organization, establishing concepts that undergird these functional circuits. To anticipate, some of the consequences of the principles are as follows: The brain’s anatomical and functional architectures are highly nonmodular; signal distribution and integration are the norm, allowing the confluence of information related to perception, cognition, emotion, motivation, and action; and the functional architecture is composed of overlapping networks that are highly dynamic and context-sensitive.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 168.


“It turns out that the brain is more interconnected than would be necessary for it to be a small world. That is to say, there are more pathways interconnecting regions than the minimum needed to attain efficient communicability....

“A central reason the brain is not a small world is because it contains a subgroup of regions that is very highly inter-connected....

“Their [work of two computer scientists on tracing studies of the macaque brain] computational analyses uncovered a ‘tightly integrated core circuit’ with several properties: (i) It is a set of regions that is far more tightly integrated (that is, more densely connected) than the overall brain; (ii) information likely spreads more swiftly within the core than through the overall brain; and (iii) brain communication relies heavily on signals being communicated via the core. The proposed core circuit was distributed throughout the brain; it wasn’t just in the prefrontal cortex, a sector often underscored for its integrative capabilities, or some other anatomically well-defined territory. Instead, the regions were found in all cortical lobes, as well as subcortical areas such as the thalamus, striatum, and amygdala.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 171; referenced study: Modha, D.S. & R. Singh. 2010. “Network Architecture of the Long-Distance Pathways in the Macaque Brain.” PNAS. 107(30):13485-13490.


“In another study, a group of neuroanatomists and physicists collaborated to describe formal properties of the monkey cortex. They discovered a set of 17 heavily interconnected brain regions across the parietal, temporal, and frontal cortex. For these areas, 92 percent of the connections that could potentially exist between region pairs have indeed been documented in published studies. So, in this core group of areas, nearly every one of them can talk directly to all others–a remarkable property. In a graph, when a subset of nodes is considerably more well connected than others, it is sometimes referred to as a ‘rich club,’ in allusion to the idea that in many societies a group of wealthy individuals tends to be disproportionately influential....

In sum, the theoretical insights of network scientists about ‘small worlds’ demonstrated that signals can influence distal elements of a system even when physical connections are fairly sparse. But cerebral pathways vastly exceed what it takes to be a small world. Instead, what we find is a ‘tiny world.’” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 171-3; referenced study: Markov, N.T., M. Ercsey-Ravasz, D.C. Van Essen, K. Knoblauch, Z. Toroczkai & H. Kennedy. 2013. “Cortical High-Density Counterstream Architectures.” Science. 342(6158).


“Functional connectivity thus answers the following question: How coordinated is the activity of two brain regions that may or may not be directly joined anatomically?... Functional connectivity measures the extent to which signals from two regions are in synchrony. Whether or not the regions are directly connected by an anatomical pathway is unimportant.... There are multiple ways to capture this concept, but the simplest is to ascertain how correlated the signals from regions A and B are. The stronger their correlation, the higher the functional association or functional connection. Correlation is an operation that is summarized by values from -1 to +1.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 174-5.


“Networks of brain regions collectively support behaviors. The network itself is the unit, not the brain area.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 176.


“Hubs come in many different flavors, such as connector hubs that have links to many communities and provincial hubs that are well connected within their particular community. We can thus think of connector hubs as nodes that are more ‘central’ in the overall system than provincial nodes.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 184.


“We suggested that a better brain unit is a network, not a region. But in highly interconnected systems like the brain, subdividing the whole system into discrete and separate networks still seems too constraining.... An alternative is to consider networks as inherently overlapping. In this type of description, collections of brain regions–networks–are still the rightful unit, but a given region can participate in several of them....” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 184.


“There’s a second, more radical way in which networks are dynamic. That’s when they are viewed not as fixed collections of regions but instead as coalitions that form and dissolve to meet computational needs. For instance, at time t1, regions R1, R2, R7, and R9 might form a natural cluster; at a later time t2, regions R2, R7, and R17 might coalesce.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 187.


“I propose that large-scale connectional systems, according to the ideas developed in this chapter, are critical for understanding how complex behaviors are instantiated by the brain. I call them functionally integrated systems.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 190.


“Emotion is at times likened to a ‘biasing’ mechanism, such as directing perception to focus on a particularly relevant object, or shifting cognition from one type of information to another. Emotion is not adequately captured by this idea–it’s much more. Emotion dynamically influences the properties of large-scale networks, including those that are described as perceptual, motor, motivational, or cognitive.

“The proposal helps clarify, too, why some structures are so important for emotion, such as the amygdala and the hypothalamus–they are important hubs of distributed functionally integrated systems.... Ultimately, emotion–insofar as it is meaningful to speak of ‘emotion’–like every other mental domain, is a large-scale network property of the nervous system.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 191.


“Experiments show that if, during the extinction process (when the CS1 [conditional stimulus #1] is presented without the concomitant UCS [unconditioned stimulus]), another stimulus (CS2) is presented alongside the original CS1 will not be treated as safe. This situation is at times called ‘protection from extinction.’ In other words, the relationship between the original CS and the UCS is maintained, and when the CS is presented alone, it produces a conditioned response–‘fear’ continues. The absence of the UCS is being attributed to the additional factor (the CS2), and the animal had better be careful (about CS1). A similar protection from extinction takes place when a new action concurrently performed by the animal leads to safety (that is, prevents the occurrence of the UCS). Here, the action is attributed with the power to ward off the punishment. So the animal will still fear CS1.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 197.


“In sum, extinction is more than a simple form of inhibition. It is a sophisticated form of learning, and as such the formation of an ‘extinction memory’ involves processes akin to those observed in learning in general: acquisition, consolidation, and retrieval. What is being learned is safety. The manner by which this memory influences behavior depends on how it was established (acquisition), how it was strengthened (consolidation), and how it will be reactivated (retrieval) in particular situations.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. pp. 197-8.


“When a CS [conditional stimulus] no longer predicts a UCS [unconditioned stimulus], the specific environment where extinction learning takes place is paramount. The animal learns that the CS in this environment is now safe. Indeed, if the CS now reappears in a novel context, the animal displays defensive behaviors—the CS does not signal safety there.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 199.


“The argument made in this book is that we should conceptualize evolution in terms of the reorganization of larger-scale connectional systems. Instead of more cortex sitting atop the subcortex in primates relative to rodents–which presumably allows the ‘rational’ cortex to control ‘primitive’ parts of the brain–more varied ways of interactions are possible, supporting more mental latitude.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 216.


“We can highlight two properties of the brain that immediately pose problems for standard, Newtonian causation. First, anatomical connections are frequently bidirectional, so physiological influences go both ways, from A to B and back. If one element causally influences another while the second simultaneously causally influences the first, the basic concept breaks down. Situations like this have prompted philosophers to invoke the idea of ‘mutual causality.’ For example, consider two boards arranged in a  shape so that their tops are leaning against each other; so, each board is holding the other one up. Second, convergence of anatomical projections implies that multiple regions concurrently influence a single receiving node, making the attribution of unitary causal influences precarious.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 225.


“I would venture that progress [in neuroscience] has been stymied by such approach [reductionism] and that the time is ripe for the field to phase-transition into a period when a truly dynamic and networked view of the brain takes hold.” Pessoa, Luiz. 2022. The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together. MIT Press. p. 229.


“A more rigorous distinction [between sensation and perception] is that sensation is the instantaneous feeling that receptors are being stimulated, whereas perception compares sensation and memories of similar experience to identify the evoking stimulus.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 54.


“But then if perceptual awareness is understood as an interpretation of sensation by the brain, it is an active process. Physiologically, awareness requires that the signal’s trace be distributed in many brain structures and linger in neuronal networks for some time. This fact is interesting because initiating movement also has a volitional component; we are aware of our voluntary actions, as opposed to reflex movements and automatic, well-learned actions, such as walking.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 55.


“When we shout loudly near someone’s ears, she may have difficulty hearing for a few seconds. In contrast, we can resume normal conversation right after our own shouting is heard by our ears. We are protected in that case by multiple mechanisms in our auditory system, as the Greeks speculated long ago. Today, we have a name for it: corollary discharge. As the name implies, this ancillary activity occurs simultaneously with the action output. The action-initiating circuits of the brain send action potentials not only to the downstream motor pathways but also synchronously to other areas in the brain. This secondary activity provides a feedback-reporting mechanism for self-organized action. ‘I am the agent that brought about change in the sensors.’” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 62.


“... neurons cannot interpret the relevance of the signals conveyed by sensory inputs because they cannot ground their response without some independent verification. For neuronal networks to interpret the world, they need two types of information for comparison. The extra information can be supplied by the movement-induced changes of the brain’s sensors. Only by comparing two signals, one of which is grounded by movement or previous knowledge can the brain figure out what happened out there.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 62.


“Sampling of the visual world is not continuous because it is interrupted by saccades, with each saccade causing a loss of up to 10% in sampling time. However, this loss has important advantages. First, blurred vision is prevented because during the saccade the visual input is suppressed. Second, corollary discharge is an important timing signal that helps coordinate neuronal activity beyond the visual system. Third, suppressed spiking in several types of visual neurons during the saccade allows them to replenish their resources, for example by restituting dendritic sodium and calcium ion channels inactivated during intense spiking. As a result, after the saccade, the visual system is transiently more sensitive to stimulation, a considerable gain.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 68.


“Active sensing refers to a brain-initiated search as opposed to the response to an expected event. Perhaps another word, ‘observation,’ equally well captures the same process. In the real world, stimuli are not given to the brain. It has to acquire them. The sensitivity of sensors depends, in part, on the effectors that can move them and maximize their efficacy.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 69.


“Although the brain has no a priori clues about what its sensors are sensing or what its effectors are effecting, the developing brain does not start from scratch; it benefits enormously from inherited and early programs. But one-size-fits-all blueprints are not adequate to do the job because bodies come in different shapes and forms.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 77.


“These seemingly aimless movements in newborn rodents are the same as fetal movements or ‘baby kicks’ observed in later stages of pregnancy in humans....

“In addition, twitches of skeletal muscles increase the probability that the skin over the muscle will touch another pup in the nest or touch the wall of the womb in case of human fetuses....

“How can such dumb ‘training’ from muscle twitch combinations contribute to the formation of the body map? In the newborn rat pup, every twitch and limb jerk induces a ‘spindle-shaped’ oscillatory pattern in the somatosensory cortex lasting for a few hundred milliseconds.... Both in the pup and in a prematurely born human baby, these are the first organized cortical patterns. When long-range corticocortical connections form after birth in the rat, the spindle oscillation can serve to bind together neuronal groups that are coactivated in the sensory cortical areas as a result of the simultaneous movement in neighboring agonistic muscles. Likewise, muscles with an antagonistic movement relationship in the body will induce consistent activity-silence relationships in their sensory cortex and create an inhibitory relationship between the respective neuronal groups.... Thus, the initially meaningless, action-induced feedback from sensors transduces the spatial layout of the body into temporal spiking relationships among neurons in the brain. This developmental process is how the brain acquires knowledge of the body it controls or, more appropriately cooperates with. Thus, a dumb teacher (i.e., the stochastically occurring movement patterns) can increase the brain’s smartness about its owner’s body landscape.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 77-8.


“... things and events in the world can acquire meaning only through brain-initiated actions. In this process, the brain does not represent the world in its numerous and largely irrelevant details but extracts those aspects that have become relevant to the organism by exploration. Thus the brain builds a simplified, customized model of the world by encoding the relationships of events to each other. These aspects of model building are uniquely different from brain to brain.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 81.


“... the idea that a group of neurons that can align themselves for a particular purpose and disband themselves when not needed emerged. This is the cell assembly or neuronal ensemble hypothesis....

“The concept is most often associated with Donald O. Hebb, who coined the term in his classic book The Organization of Behavior. Hebb recognized that a single neuron cannot affect its targets reliably and suggested that a discrete, physically interconnected group of spiking neurons (the cell assembly) is the unit that can represent a distinct percept, cognitive entity, or concept. An assembly of neurons does not live in isolation but communicates effectively with other assemblies. Because of the assumed strong interconnectivity of the assembly members, the activation of a sufficient number of them can activate the entire assembly, a process described in early texts as ‘ignition’ of the assembly.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 84-5.


“Once a cell assembly is formed, activation of a small group of members can reactivate its entire spatiotemporal signature.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 87.


“Due to connectedness, activity in a few neurons tends to activate all members of an assembly. As a result, the pattern as a whole becomes ‘auto-associated’ and fixed to represent a particular item. The most popular model based on these principles is the Hopfield attractor network. Activity in the Hopfield network varies with time and can jump or move slowly from one stable state (called the ‘attractor’) to the next.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. Note, p. 87; reference Hopfield, J.J. 1982. “Neural networks and physical systems with emergent collective computational abilities.” PNAS USA 79:2554-2558.


“They trained a monkey to move its arm to one of eight possible targets and observed a striking relationship between the discharge activity of single neurons and the direction of the monkey’s arm movement. Many neurons in the motor cortex had a preferred direction of reach. That is, they fired action potentials maximally when the monkey’s hand moved in a particular direction, less so when it moved toward a neighboring target, and not at all when the hand moved opposite to the preferred direction....

“To assess the contribution of all neurons to any given action, Georgopoulos formulated a population vector hypothesis. In such a vector, the contributions of neurons with different preferences are summed to produce a final movement command. Each neuron fires the most spikes when the arm moves toward its preferred target, but neurons with nearby preferred directions can also support the same direction less strongly, so the final vote is calculated by vectorial summation of preferred directions of individual neurons weighted by their firing rates. By examining the firing rates of many direction-tuned neurons in a given time window, the population vector model can precisely describe the resulting movement direction....

“There is a clear parallel between cell assemblies and population vectors. However, in contrast to the mathematically defined population vector, the cell assembly concept is only loosely described as representing ‘things’ through excitatory connections.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 88, 90; referenced study: Georgopoulos, A.P., J.T. Lurito, M. Petrides, A.B. Schwartz & J.T. Massey. 1989. “Mental rotation of the neuronal population vector.” Science. 243:234-236.


“In my view, a fundamental problem with Hebb’s ‘representational concept’ is that it presupposes that the same inputs always mobilize the same set of neurons because this framework suggests that objects in the world should correspond to neuronal responses in the brain [outside-in framework].” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 91.


“... the brain’s fundamental priority is not to faithfully ‘represent’ the surrounding world but to simulate practically useful aspects of it based on prior experience and select the most advantageous action in the current situation. From this perspective, an objective definition of the cell assembly requires two related key conditions: a reader classifier and a temporal frame. Let me elaborate on this bold statement a bit. Inspired by the population vector concept, I suggest that a cell assembly can only be defined from the perspective of downstream ‘reader’ mechanisms because the biological relevance of a particular constellation of active neurons can only be judged from its consequences. In my world, the term ‘reader’ refers to a mechanism that can use the inputs it receives to respond one way or another. The reader mechanism can be a muscle, a single neuron, groups of neurons, a machine, or even a human observer who interprets the meaning of the inputs....

“Reading the impact of a cell assembly requires a temporal integration mechanism. Neurons come together in time; that is, they synchronize to achieve an action that is not possible for single members alone.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 91-2.


“We also reasoned that members of the assembly should work together within a measurable time window. Like members of an orchestra, neurons in a circuit can effectively time their actions relative to others. So we tried to determine the time window within which neurons can best predict the timing of each other’s spikes. By varying the analysis window experimentally, we found that the best prediction of the spike timing of single hippocampal neurons from the activity of their peers was when the time window varied between 10 and 30 ms.

“This is an important time window in neurophysiology because many physiological variables share it. First and foremost, the membrane time constant (τ) of cortical pyramidal cells is exactly in this range, and it determines their integration ability. Discharges of upstream neurons within this window can successfully trigger an action potential in the downstream reader neuron. Generating spike responses in reader neurons is the main reason for a cell assembly to come together. Therefore, from the point of view of a single reader neuron, all neurons whose spiking activity contributes to its own spike can be regarded as a meaningful assembly. Other upstream neurons that fire outside this critical time window (i.e., nonsynchronously) can only be part of another assembly. Thus, by monitoring spiking activity of reader neurons, one can objectively determine whether the upstream neurons are part of the same assembly and serve the same goal or belong to different assemblies. Members of an assembly can project individually to hundreds or thousands of other neurons....

“Another argument for the physiological importance of the cell assembly’s ephemeral lifetime is that its 10- to 30-ms time window is similar to the duration of fast synaptic signaling mechanisms. Both excitatory and inhibitory receptors work in this temporal range. The temporal interaction between the opposing excitatory and inhibitory postsynaptic effects gives rise to an oscillating tug of war and forms the basis for one of the best known brain rhythms, the gamma oscillation. The time scale of gamma waves also corresponds to the temporal window of spike timing-dependent plasticity, a mechanism that can modify the synaptic connections between neurons.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 93-4, 95.


“To be effective, cell assemblies acting within single gamma waves (10-30 ms epochs) must mobilize enough peer neurons so that their collective spiking activity can discharge the target (reader) neuron(s). Whether different constellations of spiking upstream neurons are regarded as parts of the same or different assemblies can only be specified by downstream reader neurons(s). Because of the all-or-none spike response of the target neuron, the cell assembly defined by this reader neuron denotes a discrete, collective unitary event, which we can term ‘fundamental cell assembly or, by analogy to written language, a ‘neuronal letter.’ Several of these gamma assemblies can be concatenated to comprise a neural word....

“Every neuron can be a reader, and every reader can be part of an assembly, much like members of an orchestra who both produce actions and respond to others’ actions.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 95-6.


“Acting in assemblies has several advantages. Simple chains of neurons would be vulnerable to synaptic or spike transmission failures from one neuron to the next, resulting in the loss of neuronal messages. Furthermore, minor differences in synaptic weights between the leading and trailing neurons could divert the flow of neuronal traffic in unpredictable ways in the presence of noise. In contrast, cooperative assembly partnership tolerates spike rate variation in individual cells effectively because the total excitatory effect of the assembly is what matters to the reader. Interacting assembly members can compute probabilities, rather than convey deterministic information, and can robustly tolerate noise. We can call the assembly a unit of communication or a putative ‘neuronal letter.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 99.


“Large and small brains share the same main goal: to predict the future consequences of their actions.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 103.


“In short, cognition is time-deferred action.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 105.


“... the hippocampal map is not static. If the animal is placed in a different environment, a different set of place cells becomes active. Neighboring place neurons in one environment may have a very different spatial relationship with one another. For example, a pair of place cells representing adjacent pieces of the floor in one apparatus may become silent in another, or just one of them may remain active but at a different position. If both of them continue to fire, the distance between their preferred firing locations may be different in the two mazes. Thus, each environment is represented by a unique combination of active place cells and place fields....

“The spatial layout of individual place cells (i.e., the map) is not related to the location relationships of place cells in the hippocampal circuitry. Two neighboring pyramidal neurons are just as likely to represent adjacent or distant patches of the environment. Instead, place cells dynamically and relatively randomly reconfigure under various conditions. Largely because of this arrangement, the densely connected hippocampal recurrent collaterals can generate discrete maps individualized to the many environments an animal visits in its lifetime.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 112-3.


“‘Each place cell receives two different inputs, one conveying information about a large number of environmental stimuli or events, and the other from a navigational system which calculates where an animal is in an environment independently of the stimuli impinging on it at that moment....” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 113; this is quotation of O’Keefe from chapter 11, p. 499, in Andersen et al. 2007 but without further source info.


“Several experiments support the primacy of action in navigation. First, the firing frequency of place cells depends on the animal’s speed. Second, on a running track, a robust place field observed on the left-to-right journey is often absent on the return run.... Third, under certain circumstances, the same physical space can be represented by different sets of place cells depending on other contextual variables.... Thus, place fields can arise independently of the visual landmarks, although landmarks can control the expression of place cells. Fourth, changing the features of a landmark without changing its position can affect the map.... Fifth, when a rat is repeatedly disoriented (by turning it by hand multiple times before placing it into a cylinder with few landmarks), its place fields tend to destabilize. Sixth, when the distance between the start and goal boxes changes, only a fraction of neurons remain under the control of distant room cues....

“The list goes on. The profound implications of these observations led McNaughton, Carol Barnes, and their colleagues to postulate a hypothetical ‘path integrator’ system, a sort of a body cues feedback-supported guiding mechanism, based on information from local visual and somatosensory cues, proprioceptive feedback from the body (e.g., muscles, tendons), the number of steps taken, vestibular input (translational and rotational head accelerations), and, likely corollary discharge from self-motion activity. Integrating self-motion allows animals to move through space even without prominent landmarks while keeping track of their starting location. The path integrator system can operate without a prior spatial reference or even in complete darkness by calculating the distance traveled and the turns the animal made.

“Dead reckoning, another name for path integration, was one of the earliest forms of marine navigation....

“Navigation by path integration is intuitively appealing. Not only sailors but also animals can find their way without landmark cues.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 114, 115, 116.


“Allocentric map representation and self-referenced or egocentric path-integration route information work together. The environmental conditions determine which strategy dominates. In cue-rich environments, representations can be updated frequently by changes in the configuration of sensory inputs. In environments with few stationary landmarks or in complete darkness, path integration is the default mode.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 117.


“My first proposal to address these problems [differences between memory and future planning] is that the brain mechanisms that evolved initially for navigation in physical space by dead reckoning are basically the same as those used for navigation in ‘cognitive space’ to create and recall episodic memory. My second, related proposal is that the neural algorithms evolved to support map-based navigation are largely the same as those needed to create, store, and remember semantic knowledge. My third proposal is that generation of semantic (allocentric) knowledge requires prior self-referenced episodic experience, akin to map creation by dead-reckoning exploration.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 124.


“The relationships among events and objects, known as semantic proximity, shares many features with distance relationships in landmark navigation.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 127.


“While spatial navigation, memory, planning and imagination are distinct terms, their neuronal substrates and neurophysiological mechanisms are identical or at least similar.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 128.


“To recapitulate, the mirror neuron system allows us to read the intentions of others by interpreting body language. The expansion of the action system to spoken language permitted us to establish an extensive and effective communication system by interpreting speech, which can be viewed as a metaphoric form of action. The invention of language accelerated externalization of brain function, creating a collective species memory.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 138.


“We discussed several examples of how disengagement of brain networks from their external inputs can be useful for cognitive operations. The key physiological mechanism of this scenario is a corollary discharge-like system that allows the brain to interpret the activity of action circuits even in the absence of overt movement and sensory feedback from muscles. Within such an internalized world, brain networks can anticipate the consequences of imagined actions without the need to act them out. Instead, the outcomes can be tested against previously acquired knowledge, which creates new knowledge entirely through self-organized brain activity.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 138.


“... I suggest that neuronal rhythms provide the necessary syntactical rules for the brain so that unbounded combinatorial information can be generated from spike patterns.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 143.


“There are numerous brain rhythms, from approximately 0.02 to 600 cycles per second, covering more than four orders of temporal magnitude. ... but it was only recently recognized that these oscillation bands form a geometric progression on a linear frequency scale or a linear progression on a natural logarithmic scale, leading to a natural separation of at least ten frequency bands. The neighboring bands have a roughly constant ratio of e =2.718–the base for the natural logarithm. Because of this non-integer relationship among the various brain rhythms, the different frequencies can never perfectly entrain each other. Instead, the interference they produce gives rise to metastability, a perpetual fluctuation between unstable and transiently stable states, like waves in the ocean. The constantly interfering network rhythms can never settle to a stable attractor, using the parlance of nonlinear dynamics.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 145.


“The excitatory pyramidal neurons (also called principal cells) are considered to be the main carriers of information in the cortex. Their potential runaway excitation is curtailed by inhibitory interneurons: the 15-20% of cortical neurons that contain the inhibitory neurotransmitter gamma aminobutyric acid (GABA). The main function of these neurons is to coordinate the flow of excitation in neuronal networks. There are several different classes of inhibitory interneurons with specialized functions....

“Although there is no agreed job description for each of these interneuron types, their overall task is akin to traffic controllers in a big city. the ability to stop or slow excitation and route the excitatory traffic in the desired direction is an important requirement in complex networks. To be effective, the various traffic controllers should be temporally well-coordinated for each given job. Inhibition of excitatory neurons can be conceived as the punctuation marks of a neural syntax that can parse and segregate neuronal messages.

“The segregating or gating effect of neuronal oscillations can be illustrated by considering a single neuron whose membrane potential is fluctuating around the action potential threshold. The outcome of afferent excitation of a neuron depends on the state of the neuron. If the membrane potential is close to the threshold, a very small amount of excitation is enough to discharge the cell. However, when afferent excitation arrives at the time of hyperpolarization, the input may be ignored. Because axons of the interneurons target many principal cells, inhibition can effectively synchronize the action of the principal cells. If the discharge of interneurons is temporally coordinated–for example, by oscillatory mechanisms–many pyramidal cells in the network can produce synchronous output and exert a stronger effect on their downstream targets compared with their noncoordinated or asynchronous firing.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 148, 150.


“Inhibition is the foundation of brain rhythms, and every known neuronal oscillator has an inhibitory component. Balance between opposing forces, such as excitation and inhibition, can be achieved most efficiently through oscillations.... Inhibitory interneurons can act on many target neurons synchronously, effectively creating windows of opportunity for afferent inputs to affect inhibition-coordinated local circuits. In summary, oscillatory timing can transform both interconnected and unconnected principal cell groups into transient coalitions, thus providing flexibility and economical use of spikes.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 150-1.


“Neuronal oscillations have a dual function in neuronal networks: they influence both input and output neurons. Within oscillatory waves, there are times when responsiveness to a stimulus is enhanced or suppressed. We can call them ‘ideal’ and ‘bad’ phases. Oscillation is an energy-efficient solution for periodically elevating the membrane potential close to threshold, thus providing discrete windows of opportunity for the neuron to respond. The physiological explanation for this gating effect is that the bad phase of the oscillatory waves is dominated by inhibition, as discussed earlier, whereas excitation prevails at the ideal phase. The same principle applies at the network level: when inputs arrive at the ideal phase of the oscillation–that is, at times when neurons fire synchronously and thus send messages, they are much less effective compared to the same input arriving during the bad phase of the oscillator, when most neurons are silent.

“As discussed earlier, activation of inhibitory interneurons can hyperpolarize many principal neurons simultaneously. Recruitment of inhibitory interneurons can happen via either afferent inputs (feedforward) or via pyramidal neurons of the activated local circuit (feedback). As a result, the same mechanism that gates the impact of input excitation also affects the timing of output spikes in many neurons in the local circuit. Such synchronized cell assembly activity can have a much larger impact on downstream partners than on individual, uncoordinated neurons with irregular interspike intervals. This dual function of neuronal oscillations is what makes them a useful mechanism for chunking information into packages of various lengths.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 152-3.


“Temporal organization of neuronal activity, as represented by rhythms, is a fundamental constraint that needs to be preserved when scaling brain size. Indeed, perhaps the most remarkable aspect of brain rhythms is their evolutionarily conserved nature. Every known pattern of LFPs [local field potentials], oscillatory or intermittent, in one mammalian species is also found in virtually all other mammals investigated to date....

“On the one hand, this may not be so surprising. After all, neurotransmitters, their receptors, and the membrane time constants of principal cells and interneurons are also conserved, and these properties underlie various oscillations. Thus, irrespective of brain size, the management of multiple time scales in neuronal networks is supported by the same fundamental mechanisms. On the other hand, the speed of communication between areas varies considerably between small and large brains, making the conservation of rhythms unexpected. For example, for coherent perception of multimodal inputs, the results of local computation in the thalamus and several primary sensory cortical areas should arrive within the integration time window of the target associational cortices. The same applies to the motor side of the brain. ... the fundamental properties of myosin and actin are largely conserved across mammals. Therefore, the motor command computations in the motor cortex, cerebellum, and basal ganglia should be performed in comparable time windows, and the command signals to the spinal cord should be delivered within the same time range in different species. However, the distances of these structures vary by orders of magnitude across species. Thus, all of the timing constraints required for adequate function have to be reconciled with the complexity imposed by the growing size of the brain. This is not a trivial task, given a 17,000-fold increase of brain volume from the small tree shrew to large-brain cetaceans. The constancy of the many brain oscillations and their cross-frequency coupling effects across species suggest a fundamental role for temporal coordination of neuronal activity.

“There appear to be at least two mechanisms that allow scaling of neuronal networks while conserving timing mechanisms. The first mechanism compensates for the increase in neuronal numbers and the enormous numbers of possible connections by shortening the synaptic path length between neurons, defined as the average number of monosynaptic connections in the shortest path between two neurons.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 153, 154-5.


“When two oscillators with identical frequency engage each other, the outcome depends on the phase of the two rhythms. In-phase interactions induce resonance and, as a result, amplification. In contrast, opposing phase interactions may annihilate or dampen the rhythm. Oscillators with noninteger relationships induce perpetual interference. This is typical of brain rhythms, and the interference mechanism explains why brain dynamics is constantly changing, similar to the interference of ocean waves. Occasionally, the oscillatory reader mechanism may transiently adjust its phase to the incoming inputs. Such phase adjustment is among the most important flexible features of brain oscillators. This is similar to how musicians in an orchestra keep the beat. If the first violinist is a bit faster, the rest of the musicians adjust the timing of their movements.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 157.


“... the evolving neuronal assembly trajectory concept, the idea that the activity of a group of neurons is somehow ignited in the brain, which passes its content to another ensemble, and the second ensemble to a third, and so forth until a muscular action or thought is produced. Creating ideas is that simple. To support cognitive operations effectively, the brain should self-generate large quantities of cell assembly sequences.

“... the only reason I can write this chapter is because continually changing neuronal assemblies in my brain evolve in a perpetual chain. In fact, this idea is the only current contender to explain internally generated actions and thoughts.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 166.


“Sequential activity of neuronal ensembles can be brought about by changing constellation of environmental landmarks and/or proprioceptive information from the body. Alternatively, sequential activation can be supported by internally driven self-organized patterning [firing patterns that pass an initial signal from one to another].” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 173.


“The linking of items in episodic memory, analogous to linking of place cells by theta-gamma coupling, could explain two important principles of memory recall: asymmetry, which is the finding that forward associations are stronger than backward associations, and temporal contiguity, the finding that recollection of an item is facilitated by the presentation or spontaneous recall of another item that occurred around the same time.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 176.


“Instead, learning may be an inside-out matching process: when a spontaneously occurring neuronal trajectory, drawn from the available huge repertoire of trajectories, coincides with a useful action, that trajectory acquires meaning to the brain.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 189.


“Lewis Thomas’s beautiful metaphor of population cooperativity [‘Single locusts are quiet, meditative, sessile things, but when locusts are added to other locusts, they become excited, change color, undergo spectacular endocrine revisions, and intensify their activity until, when there are enough of them packed shoulder to shoulder, they vibrate and hum with the energy of a jet airliner and take off.’] could equally well describe a peculiar hippocampal population pattern, called sharp wave ripples–just substitute neurons for locusts. The sharp wave ripple is a randomly emerging local field potential event occurring when many neurons emerge from their sessile state and fire together shoulder to shoulder. I have been enthralled by their beauty and power from the first moment I heard their buzzing sound in my postdoctoral years. I felt as if I had been listening to a group of orchestra musicians idly tuning their instruments, and the next moment they united in the thrilling harmonies of Beethoven’s Fifth Symphony. I still think it is the most beautiful pattern the brain produces. Sharp wave ripples represent among the most synchronous population patterns in the mammalian brain, more synchronous than the responses evoked by sensory stimulation of any strength. yet they are self-organized and spontaneously emitted by hippocampal circuits....

“There is no trigger for the occurrence of sharp wave ripples. They are not caused by anything. Instead, they are released, so to speak, when subcortical neurotransmitters reduce their grip on hippocampal networks, as routinely happens during nonaroused or idle waking states, such as sitting still, drinking, eating, grooming, and non-rapid eye movement (REM) sleep. Sharp wave ripples are produced by tens of thousands of neurons in the hippocampus, subicular complex, and entorhinal cortex, firing over just 30-100 ms at times when the brain is disengaged from the environment.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 200.


“The sharp wave ripple in the hippocampus is the most synchronous population event in the mammalian brain. Ripple events can play palindrome, so that place cell sequences prior to choosing a particular path are replayed in the same order as during crossing the path, only much faster. At the end of the travel path, the same sequence is replayed but now backwards, as if the brain recapitulated a virtual reversed run. Thus, ripple sequence events represent mental travel both into the future and the past.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 216.


“I conclude that human-made instruments and other artifacts have become an extension of the action-perception loop, as well as the media through which abstract ideas emerge and spread quickly.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 221.


“A thought, which can be conceptualized as a buffer for a deferred action ....” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 222.


“These anatomical considerations [including sharing large spindle cells that can send action potentials quickly across long distances] reveal that motor and prefrontal areas of the cortex share many anatomical features. The main functional difference is that while activity in the motor cortex leads to immediate action, activity in the prefrontal cortex may only simulate action, which we call plans and imagination.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 223.


“Artifacts are externalized versions of a thought, a reflection of contemplation, and a way to communicate personal knowledge to others even after the creator has vanished. Artifacts are semantic entities, which can be labeled and remembered as separate from other things. The root of this mirroring between action and perception may be built upon mechanisms analogous to active sensing, with its corollary circuits. However, the feedback in this extended loop is not a specialized circuit within the brain but a sequence between action-produced artifacts and their reflection back to the brain.

“These externalized mental products come to exist outside their creator’s brain as permanent social memory, enriching the collective mind.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 225.


“In a modern experiment, illiterate women in India learned how to read and write their mother tongue, Hindi, while their brains were repeatedly imaged. After just 6 months of literacy training, several areas of their brains reconfigured, including the thalamus and even the brainstem.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 227.


“Perhaps the most obvious example of gain modulation is the response of sensory systems to natural stimuli of differing intensities, such as the adaptation to light intensity....” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 285.


“Gain and normalization are simple but fundamental mechanisms that can support numerous functions in the brain. These mechanisms are called by a variety of names, such as coordinate transformation, place anchoring, abstraction, and attention. They are of fundamental importance, as illustrated by the numerous mechanism for gain control that exist in the brain, including divisive inhibition, short-term plasticity of synapses, and subcortical neuromodulation. Gain control allows inputs from the retina and the positions of the eyes in their sockets, the head, and the hands to affect the magnitude of responses to visual inputs in multiple brain regions, particularly the parietal cortex. Gain control mechanisms can shift coordinate representations, for example, from visual space to head space to hand space or recognize an object as the same when it is viewed from different directions. The mechanisms of translation and object invariance are the neuronal basis of abstraction, a process of ignoring features that are not essential to recognizing entities. Gain control is important in the hippocampal system, allowing judgment of distances independent of locomotion speed. Attention may be viewed as internalized gain control.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 300.


“... ratio judgment is a natural function of the brain due to the way it calculates.

“Multiplication, division, fraction, proportion, normalization, and gain have been recurring terms in the preceding chapters because they are ubiquitous operations in brain circuits. All these operations utilize ratios.... Hence ... log-scaling in the brain.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 302.


“To perform effectively, physiological brain operations must occupy a wide dynamic range between silence and supersynchrony.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 303.


“Yet, one of these [among the very few laws of neuroscience], the Weber law or also called the Weber-Fechner law has a breathtaking simplicity and generality....

“He [Fechner] trusted Weber’s observations and calculated mathematically that sensation is a logarithmic function of physical intensity. Therefore, when stimulus strength multiplies, the strength of perception adds. If the importance of a law depends on its generalizability, the Weber-Fechner rule is important. It applies to vision, hearing, and taste. Distance perception, time perception, and reaction time also vary logarithmically with the distance or time interval, respectively.... Decision-making and short-term memory error accumulation also obey the law. this is an impressive list. The Weber-Fechner law was conceived 150 years ago, but the reason that our subjective sense of many variables contains these particular systematic patterns has remained obscure.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 305-6.


“Perhaps the most common skewed distribution in biology is the logarithmic-normal or log-normal distribution. This distribution is right-skewed on a linear scale but looks bell-shaped when the logarithms of the observed values are plotted. In other words, a log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed....

“The log-normal distribution also describes the firing rates of cortical pyramidal neurons. The activity of these cells varies from nearly complete silence to several spikes per second. When the long-term average firing rates of many neurons are plotted on a linear scale, the distribution is strongly skewed, with many slowly firing neurons at the left end and a minority of highly active neurons occupying the right tail of the plot. There is no ‘golden mean’ or representative average neuron in this distribution because the mean is strongly biased by the fast firing minority. A median, which is the middle value in the distribution, is a bit more descriptive, but the shape of the distribution is more informative than any single value.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 308-9.


“Because axon diameter and myelination determine the conduction velocity of neurons, evolutionary adjustment of these variables appears to be most important for brain size-invariant scaling....

“Axon diameters in the brain vary over several orders of magnitude, and their distribution is strongly skewed. In humans, the great majority of callosal axons have diameters smaller than 0.8 μm, but the thickest 0.1% of axons can have diameters as large as 10 μm.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 310-311.


“In summary, a disproportionate increase in larger diameter axons [over evolutionary timescales] with fast conduction velocities keeps communication speed similar [across animal’s overall speed of body coordination] as brain size increases.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 312.


“Excitatory inputs onto other excitatory neurons form synaptic connections with microscopic protrusions of the dendrites, known as spines because of their appearance under the microscope....

“Microscopic imaging studies have documented a large variety of spine sizes on single pyramidal neurons. Giant spines can be several hundred times larger than the smallest ones.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 312.


“Importantly, the log firing rates of spontaneous spikes and spikes evoked by stimuli strongly correlate. If a stimulus evokes an extra spike in a neuron firing at a rate of one per second, the same stimulus may induce ten excess spikes in another neuron whose baseline firing rate is ten per second. This is a huge difference in the number of induced spikes, yet proportionally they yield the same value. In other words, the response of a neuron to extrinsic inputs is proportional to its long-term firing rate, reminding us of the Weber-Fechner law.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 314-5.


“The impact of a neuronal assembly on its targets depends on its degree of synchrony. There is a difference between a hundred neurons firing together in seconds or in a short gamma cycle. Temporal synchrony can be measured by quantifying the fraction of spiking neurons in any given time window. Such measurements show that ‘ensemble size’—the number of neurons active in a particular time window–does not vary around a typical mean. Instead, the magnitude of synchrony follows a log-normal distribution.... Rapidly firing neurons are more frequent participants in large population events as more frequently occurring spikes are expected to coincide more often with any other event than spikes of slow neurons. However, what is even more critical is their better connectedness to each other and everyone else in the population. This oligarchic ‘hub’ nature of connectedness makes fast firing neurons more frequent partners and often leaders of large population events. Not only the magnitude but the duration of population events is also log-normally distributed, including that of hippocampal sharp wave ripples, neocortical slow oscillations, and thalamocortical sleep spindles.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 316-7.


“Individual neurons maintain their firing rate ranks over days, weeks, and months, as if they sense their own firing outputs and adjust them to a set point customized to each cell. The distribution of intrinsic firing rates reflects a fundamental biophysical heterogeneity in neuronal populations, of which the neuron’s firing rate acts as a readily identifiable marker. Of course, the firing rates of individual neurons vary transiently (e.g., in response to relevant stimuli).... However, when the rate of a neuron is sampled over a long period of time, it will be dominated by the activity in its ‘idling’ state.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 319-320.


“At least one mechanism, called spike timing-dependent plasticity, depends on the temporal order of spikes. If neurons A and B are mutually connected, and A consistently fires before neuron B within the duration of a gamma cycle, the synapse A to B will be strengthened but B to A will be weakened. Because the probability of temporal coincidence increases with the firing rates of the coactive neurons, this synaptic plasticity rule favors preferential strengthening of synapses involving neurons with higher firing rates. In the waking brain, the asynchronously active heterogeneous population leads to asymmetric strengthening of synapses on neurons with higher spontaneous firing rates. The consequence of this process is easy to imagine: excitable neurons become progressively more excitable, which can eventually destabilize the network.... One mechanism that counteracts this pressure is the up-state transition of non-REM sleep.

“During the transitions from down state to up state in non-REM sleep, neurons fire in a sequence. A neuron’s place in this order is correlated with it baseline firing rate, such that neurons with higher firing rates tend to spike before those with lower firing rates. A simple but important consequence is that neurons with high and low firing rates get temporally segregated. Because high-firing neurons tend to fire earlier than low-firing neurons after the down-to-up transition, the plasticity rule tends to increase the weights of synapses from high-firing onto low-firing neurons while decreasing the weights of synapses from low-firing onto high-firing neurons. This redistribution of synaptic weights during non-REM sleep pulls both ends of the log-normal firing rate distribution closer to the mean and acts as a homeostatic counter to changes in the asynchronous wake state. This normalization mechanism is an important function of sleep.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 322.


“That is, every hippocampal neuron is viewed as a potential place cell. This idea was tested by training rats to run novel maze tracks with lengths of 3, 10, 22, and 48 meters. Some place cells that fired on the short tracks formed additional fields on the larger tracks, but most new place cells were recruited from the pool of initially silent cells. The number of fields formed was strongly skewed: a few neurons had many fields, whereas many neurons had one or none, similar to the behavior of hippocampal neurons in a single maze. Extrapolation from the observed log distributions suggested that nearly all hippocampal pyramidal cells would be active in an environment with a diameter of approximately 1 kilometer, which is believed to correspond to the ecological niche of rats. In another experiment, rats were tested in multiple rooms. Most pyramidal neurons fired only in a single room, but a small minority fired in multiple rooms or all rooms, producing a log-normal distribution of the overlap of neuronal activity in the different rooms. Overall, these experiments demonstrate that the skewed distribution of place fields is a general rule, irrespective of the nature or size of the testing environment.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 324-5.


“The overall wisdom that we can draw from the many physiological experiments performed in numerous laboratories and in different parts of the cortex is that some neurons may appear to treat multiple stimuli and situations as the same and similar; that, is they generalize input features. Other neurons, on the other hand, appear to be super-specialists and respond only to a single feature of the many options. However, the most important message of this discussion is that the generalizers and specialist form a continuum.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 325.


“Such individual neuron analyses [studying individuals rather than assemblies] demonstrate that learning-induced plasticity is not equally distributed among hippocampal pyramidal neurons after an encounter with a novel spatial task. Instead, the sequences of place cells in the novel environment are formed from neurons that already had place cell features before the experience and do not change much during learning. We can call them ‘rigid’ cells. These rigid neurons belong to the high firing end of the population, and they are more strongly connected to each other. Many of their afferent connections may already be too strong, also called ‘saturated,’ and therefore cannot get much stronger. At the other end of the spectrum are the ‘plastic’ neurons. They also may have a place field from early on in a new environment, but they can modify their firing patterns and can become incorporated into the backbone of the rigid group after group experience. Thus, the hippocampal network contains a continuum of neurons spanning a range of rigid to plastic features. The plastic cells show lower mean firing rates, more specific firing fields, and larger changes in firing rates and field specificity during the first minutes of exploration than the rigid cells....

“Plastic neurons also have higher place-specific indices compared with rigid cells and typically only one place field. The firing patterns of the plastic and rigid neurons change differently during learning. While both fast- and slow-firing neurons can have place fields from the beginning of maze exploration, slow- but not fast-firing neurons increase their spatial specificity steadily during learning, as measured by the ratio of the number of spikes emitted inside versus outside the place field.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 328, 329.


“The law [Weber-Fechner law] states that for any sensory modality, perceptual intensity is a logarithmic function of physical intensity. Therefore, as stimulus strength multiplies, the strength of perception only adds. Distance perception, time perception, and reaction time also vary logarithmically with the distance and time interval, respectively. Error accumulation in short-term memory, and other phenomena also obey this law.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 333.


“Neurons on the two ends of the log-normal distribution of activity organize themselves differently. Fast-firing neurons are better connected with each other and burst more than slow-firing neurons. The more strongly connected faster firing neurons form a ‘rich club’ with better access to the entire neuronal population, share such information among themselves, and, therefore, generalize across situations. In contrast, slow firing neurons keep their independent solitude and elevate their activity only in unique situations.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 334.


“We have two brains in our skull or at least two virtual divisions. First, there is the ‘good-enough’ brain. This is largely prewired and acts quickly via a minority of highly active and bursting neurons connected by fast-conducting axons and strong synapses into a network....

“However, good enough is far from perfect. We would not want to drive a car with 60-80% accuracy or submit a scientific paper with such precision. To perform better, we also need to deploy the second virtual brain: a large fraction of slow-firing neurons with plastic properties that occupy a large brain volume connected by weaker synapses into a more loosely formed giant network. Their work is absolutely critical for increasing the accuracy of brain performance.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. pp. 337, 338.


“Yet it is not clear how neurons in such a brain would know where to direct attention or what events in the world to process. Equally importantly, there is no easy way to understand how a blank slate, passive observer brain, embedded in an outside-in framework, can become a doer and creator of goals.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 340.


“Formally, the Bayesian method is a disciplined statistical method to collect and evaluate data.... The Bayesian brain model is a representational framework with a strong emphasis on evaluation of perceptual inputs and decision-making with no or very little role attributed to action or internal motor reafferentation to sensory processing. Its predictions rest on an internal or a generative model of how sensory inputs unfold. It posits that the brain–more precisely, our perceptual system–makes assumptions about the objective world....

“The Bayesian model presumes that more complex brains, such as ours, can accurately estimate the properties of the objective world on the basis of sensory information alone.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 341.


“If brain networks and dynamics are preformed, what advantages do they offer over the blank slate model? First and foremost, its preexisting ‘ideal forms’ provide the necessary balance to keep the brain’s dynamical landscape stable and robust against other competing needs, such as wide dynamic range, sensitivity, and plasticity. There is no threat of catastrophic interference because preformed brain networks are not significantly perturbed by new experiences. Indeed, computational models attempting to avoid catastrophic interference include two different synaptic populations. One set can change rapidly but decays to zero rapidly (‘fast’ weights); the remaining set is hard to change but decays only slowly back to zero (‘slow’ weights). The weighting used in the learning algorithm is a combination of slow and fast weights, reminiscent of the two ends of the log distribution of synaptic weights in real brains. Second, newly acquired experience is not created in the sense of adding new words to a vocabulary list. Instead, the preformed brain is an already existing dictionary, although its numerous words and sentences are initially meaningless. As a sharp piece of lifeless stone has the potential to become an essential tool for scraping meat or a weapon, neuronal words have the potential to become meaningful to the organism after experience attaches utility to them.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 343.


“Evolving cell assemblies, therefore, reflect the default functional mode of the brain. In fact, it would be hard to imagine nondynamic, stationary circuits in which neurons would be silent or idling.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 344.


“For the inside-out model, it is experience that adds meaning to preformed neuronal trajectories and their combinations.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 344.


“The brain’s hierarchically related oscillations serve a dual purpose: they maintain stability and robustness on the one hand and offer a needed substrate for syntactical organization of neural words and sentences on the other. This is the organization I call the preformed or preconfigured brain: preexisting dictionary of nonsense words combined with internally generated syntactical rules.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 344.


“Meanings are action-calibrated neuronal trajectories.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 345.


“Because the brain’s action signals are always copied to sensory circuits, meaningless neuronal words can become meaningful by comparing actions with sensations. The copy of the output provides sensory circuits with a second opinion, a sort of a reality check against what comes into the brain through the sensors.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 345.


“Matching preconfigured patterns with experience is not unique to the hippocampus. In the motor cortex, neuronal firing sequences observed after learning a particular movement or intention of movement are remarkably similar to pre-existing neuronal patterns produced before learning, thus demonstrating that neuronal populations are constrained to give rise to neuronal sequences from a large reservoir of internally induced patterns. Instead of random and unlimited combinations, a particular preexisting pattern from the large reservoir is assigned to a new movement and perceived pattern, and thereby the neuronal sequence acquires a behavioral meaning.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 349.


“Gestalt psychologists recognized that the mind has an innate ability to perceive patterns based on similarity, proximity, continuity, closure, and connectedness. All these terms describe a relationship. Detailed knowledge of the parts of an image is not necessary to recognize their sum (i.e., the whole).” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 350.


“Even in the most unexpected situation, our brain generalizes by relating the novel situation to something else. Only after some discrepancies with importance to the organism are detected does the brain try to identify circumstances that can differentiate the current event from previously experienced similar ones.” Buzsaki, Gyorgy. 2019. The Brain from Inside Out. Oxford UP. p. 358.


“Concretely, ecological psychology and, by extension, radical embodied cognitive (neuro)science rest upon two fundamental motifs or ideas regarding perception and the control of action. The first motif pertains to ecological information, proposing that each perceivable property of the environment, no matter how subtle, is specified by a corresponding higher order variable of information, regardless of its complexity. The term ‘ecological information’ refers to those high-order patterns of stimulation flows that constitute perceptual information. These patterns are multiple, from the horizon ratio of a given figure and the time to contact (TTC) known as tau to the centrifugal expansion of the optical flow in forward locomotion and the infinitesimal accretion/deletion of textures and non-diffeomorphic patterns in object segmentation....

“Along with the motif of ecological information, ecological psychologists and radical embodied cognitive (neuro)scientists propose the self-organization of behavioral control in the spirit of James Gibson’s famous motto ‘behavior is regular without being regulated’. This second motif states that motor control consists in the establishing of brain-body-environment coordinative structures (or synergies) constrained by ecological information. As in the previous case, this approach spares the organism, and more concretely the brain, from constructing an internal model of the body-environment system because the explanatory strategy already includes both the real body and the real environment in it. The two proposed motifs can be summarized as follows: Behavior is controlled with respect to perceptual information available in the flow of stimulation, where perceptual (ecological) information is usually described in terms of high-order patterns of that flow. This is the information-based control laws hypothesis.” Raja, Vicente & Klaus Gramann. 2025. “Ecological Resonance Is Reflected in Human Brain Activity.” Psychophysiology. 62:e70136. 10.1111/psyp.70136. [3] p. 2.


“These results are compatible with both the hypothesis of information-based control laws and the hypothesis of ecological resonance. As two main aspects of the perception-action loop, the combination of these two hypotheses constitutes the core both of the ecological approach to motor control and of ecological/radical embodied neuroscience. Thus, these results support the possibility of an alterative approach to perceptually guided action that avoids postulating internal models as cental controllers of behavior.” Raja, Vicente & Klaus Gramann. 2025. “Ecological Resonance Is Reflected in Human Brain Activity.” Psychophysiology. 62:e70136. 10.1111/psyp.70136. [3] p. 9.


“In this study we asked what would happen if we provided these biobots with the ‘raw materials’ for building a nervous system? That is, if we used neural precursor cells in addition to embryonic ectodermal cells, would such neural precursor cells indeed differentiate into functional neurons within the biobot?...

“We show that neural precursor cells harvested from Xenopus embryos and implanted in biobots made from Xenopus ectodermal cells indeed differentiate into functional neurons and extend their processes within and toward the neurobot’s out surface. We show that neurobots exhibit significant differences in behavior and anatomy compared to their non-neuronal counteparts.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] pp. 3-4.


“As with non-neuronal biobots, by the third day, multiciliated cells start appearing on their outer surface and the bots start moving around in the dish. Similar to biobots, neurobots have a lifespan of about 9-10 days without being fed, and survive by consuming maternal yolk platelet present in all early Xenopus embryonic tissue. Interestingly, by day 6 neurobots tend to have a more elongated shape than biobots, and they become significantly larger. To investigate whether the difference in size and elongation is simply due to implanting the animal caps with additional cells, we generated a third type of bot (sham neurobots) in a manner similar to neurobots, except that the implanted cells were not allowed to remain separated for 3 hours. Instead, they were reaggregated shortly after dissociation to prevent the induction of neural fate.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 4.


“There was a large degree of variability in the structure of sprouting in different neurobots. No two neurobots showed identical neural architecture.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 5.


“There was a large degree of variability in these trajectories, with some bots moving in circular/oval trajectories with relatively constant diameter; bots that followed circular trajectories varying in diameter over time; ones that made more complex, sometimes spirograph-like patterns; those that were seemingly following the dish’s boundaries; bots that circled over very small areas; and those that did not move at all. Interestingly, all moving bots tended to exhibit repeating behavioral motifs.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] pp. 5-6.


“However, we found that the minimum movement speed of neurobots was significantly higher than that of biobots, indicating that neurobots tended to move more than biobots, remaining idle less often.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 6.


“Interestingly, we found that neurobots showed a significantly higher degree of trajectory complexity compared to biobots.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 6.


“Moreover, we found that gene expression levels in biobots and sham neurobots were much more correlated to one another than to neurobots.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 8.


“Neurobots contained genes encoding various neurotransmitter receptors including glutamate, kainate receptors, GABAergic and glycinergic receptors, genes encoding voltage gated calcium channels, as well as those involved in the uptake of neurotransmitters. Genes with important roles in synaptic plasticity were also present in neurobots.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 10.


“Finally, we tested the hypothesis that neurobots are expressing a more ancient transcriptome as a result of their nascent evolutionary history.... Interestingly, we found that more than 54% of upregulated genes in neurobots fall into the two categories of most ancient genes.... Therefore, we conclude that the development of neurobots involves a transcriptomic shift towards very ancient genes for neurobots compared to biobots and shams.” Fotowat, Haleh, Laurie O’Neill, Leo Pio-Lopez, Megan Sperry, Patrick Erickson, Tiffany Lin & Michael Levin. 2025. “Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System.” BioRxiv. 2025-04. [4; unclear page numbering] p. 11.


“The resemblance between the evolutionary and comparative study of development and cognition lies not only in the seemingly insurmountable difficulties of reaching a satisfactory definition that encompasses the varied phenomena to which their extensions purportedly refer, but also in that many of the same types of questions we can ask about the evolution of cognition and cognitive capacities. This includes, among others: How many times have they evolved in phylogenetic history? Which cognitive or developmental traits are homologous or homoplastic in which taxa? Could certain cognitive or developmental processes in a particular organism be sui generis due to its prior evolutionary trajectory or are they rather indicative of a larger lineage or clade?” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 5.


“Through our comparison between how ‘development’ has been approached from an evolutionary perspective in Evo-Devo, we identify four specific parallels with the basal cognition approach that bear upon its prospects for becoming a fully evolutionary research field that is able to address the scope problem: First, both fields conduct comparative causal-mechanistic investigations to uncover shared developmental or cognitive toolkits and capacities. Second, researchers in each area assume panextensionalist positions about the phylogenetic scope of ‘cognition’ and ‘development’ in an attempt to counteract purported evolutionary biases of oligoextentionalist positions that only grant cognition or development to few organisms (i.e., neuronal metazoans and clonal multicellular organisms, respectively). Third, when investigating developmental and cognitive traits, sound phylogenetic thinking can aid in distinguishing between homologies and homoplasies, as well as in countenancing convergence in evolutionary scenarios. Fourth, by adopting a fully evolutionary perspective, the loss, gain, and uniqueness of particular developmental and cognitive traits and capacities should be studied in particular lineages.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 5.


“In both research areas [cognition, evo-devo], phylogenetic commonalities and evolutionary conservation are thus explanatorily important. However, their epistemic emphasis differs: in Evo-Devo, the focus is on shared explanantia that account for similar or different phenotypes across diverse lineages, while in basal cognition research, the emphasis is on shared explananda–specifically, the particular cognitive capacities that mechanistic-comparative research must investigate across diverse creatures, including both neural and non-neural organisms.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 7.


“Oligoextensionalist views should be familiar to all of us: viz., cognition exists only in metazoans with proper neural systems, while ‘true’ development is limited to five multicellular clades and only to those clades: animals, plants, red algae, brown algae, and fungi. In contrast, panextensionalist views for either cognition or development are less widespread, but they still can be vigorously found in scientific and philosophical scholarship. Under these lights, all living systems are deemed veritably cognitive and with respect to development, all living systems are taken to exhibit and embody this process in their life histories, from extremophile bacteria to blue whales.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 10.


“For the domain of cognition, being a panextensionalist is one way of subscribing a strong life-mind continuity thesis (i.e., the idea that mind is, and always has been, prefigured in and indissociable from all forms of life). In this sense, some defenders of disparate frameworks such as the theory of autopoiesis, enactivism, and the free energy principle can be counted as panextensionalists regarding cognition.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 10.


“Homology entails an identify relationship between traits in different species based on historical continuity, irrespective of realized form and function (e.g., human arms and bat wings are two characters states of the same homologous character, namely the tetrapod forelimb). In contrast, homoplasy refers to the relationship between traits in two or more unrelated species that bear similarity that is not explainable by recourse to common ancestry. Homoplasy can occur by convergent evolution (e.g., when similar selective pressures are at play and yield analogous traits), by parallelism (e.g., when shared developmental mechanisms independently produce comparable traits), or by reversals and atavisms.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 15(note).


“The panextensionalist framing of basal cognition research that we explored in the preceding section unwittingly assumes that the similarities in the cognitive toolkit are indicative of homology.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 16.


“In this paper, we have uncovered four parallels between the fields of Evo-Devo and basal cognition: (i) the search for shared, conserved toolkits and the importance of comparative causal-mechanistic research; (ii) panextensionalist framings to counteract the evolutionary limitations of oligoextensionalism about development and cognition; (iii) the implementation of phylogenetic thinking to uncover homologies and homoplasies (especially possible convergent routes of evolution); and (iv) the possibility to study losses, gains, and uniqueness of traits, including complexification and simplification in particular lineages.” Fabregas-Tejeda, Alejandro & Matthew Sims. 2025. “On the prospects of basal cognition research becoming fully evolutionary: promising avenues and cautionary notes.” HPLS. 47:10. 10.1007/s40656-025-00660-y. [5] p. 22.


“We found that, across a battery of measures of statistical organization, including networks, higher-order interactions, dynamic information integration, and time-resolved fluctuation analysis that basal Xenobots display patterns of information that resemble those seen in the human brains, and are universally more organized than a null model that preserves cell-level dynamics.” Varley, Thomas F., Vaibhav P. Pai, Caitlin Grasso, Jeantine Lunshof & Michael Levin. 2025. “Identification of brain-like complex information architectures in embryonic tissue of Xenopus laevis organoids.” Communicative & Integrative Biology. 18(1):2568307. 10.1080/19420889.2025.2568307. [3] p. 21.


“Here, we showed that two radically different biological systems: human brains, and basal Xenobots (self-assembling epithelial cell constructs) display a common set of organizational features and dynamics, including sophisticated functional connectivity networks, higher-order information of multiple types, and dynamic integration of information. In human brains, these features have been associated with meaningful differences in cognitive and behavioral state, raising questions about how to interpret their presence in the context of non-neural tissue. Despite their comparatively simpler make-up, and the artificial processes involved in making them, these results show that basal Xenobots are undeniably complex systems in their own right, displaying a rich information structure and emergent organizational features. Given the lack of (apparent) structural connectivity (a key difference from brains), basal Xenobots displaying complex, emergent statistical structures may have alternative ways of propagating signals and processing them. We propose that these results show that ‘brain-like’ patterns of information processing may not be specific to the brain at all; that ideas like ‘information processing’ and ‘information integration,’ may be even more relevant to non-neural, biological systems than is generally appreciated and the functional similarities between different types of embodied autonomous agents can now be quantified.” Varley, Thomas F., Vaibhav P. Pai, Caitlin Grasso, Jeantine Lunshof & Michael Levin. 2025. “Identification of brain-like complex information architectures in embryonic tissue of Xenopus laevis organoids.” Communicative & Integrative Biology. 18(1):2568307. 10.1080/19420889.2025.2568307. [3] p. 23.


“Here we will argue, however, that detailed examination of brain parts or their selective perturbation is not sufficient to understand how the brain generates behavior. One reason is that we have no prior knowledge of what the relevant level of brain organization is for any given behavior. When this concern is coupled with the brain’s deep degeneracy, it becomes apparent that the causal manipulation approach is not sufficient for gaining a full understanding of the brain’s role in behavior.” Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] p. 480.


“Behavior is the internally coordinated responses (actions or inactions) of whole living organisms (individuals or groups) to internal and/or external stimuli, excluding responses more easily understood as developmental changes” Quoted in: Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. (Quote by: Levitis, D.A., W.Z. Lidicker & G. Freund. 2009. “Behavioural biologists don’t agree on what constitutes behaviour.” Anim. Behav. 78:103-110.) 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] p. 482.


“The phenomenon at issue here, when making a case for recording from populations of neurons or characterizing whole networks, is emergence–neurons in their aggregate organization cause effects that are not apparent in any single neuron. Following this logic, however, leads to the conclusion that behavior itself is emergent from aggregated neural circuits and therefore should also be studied in its own right.” Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] p. 484.


“The study poses the question of whether a neuroscientist could understand a microprocessor. They applied numerous neuroscience techniques to a high-fidelity simulation of a classic video game microprocessor (the ‘brain’) in an attempt to understand how it controls the initiation of three well-known videogames (which they dubbed as ‘behaviors’) originally programmed to run on that microprocessor. Crucial to the experiment was the fact that it was performed on an object that is already fully understood.... In the study, (simulated) transistors were lesioned, their tuning determined, local field potentials recorded, and dimensionality reduction performed on activity across all the transistors. The result was that none of these techniques came close to reverse engineering the standard stored-program computer architecture.” Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] p. 484; “study” is experiment by Jonas, E. & K. Kording. 2017. “Could a neuroscientist understand a microprocessor?” PLoS Comput. Bio. 13:e1005268.


“Why is it the case that explanations of experiments at the neural level are dependent on higher-level vocabulary and concepts? The answer is that this dependency is intrinsic to the very concept of ‘mechanism’.... Crucially, the components of a mechanism do different things than the mechanism organized as a whole (i.e., emergence). A reductionist treatment of the components must be combined with investigation of how the total mechanism is organized and how it behaves when embedded in an environment; an approach that unavoidably spans two levels.” Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] p. 485; this paragraph references: Bechtel, W. 2008. Mental Mechanisms: Philosophical Perspectives on Cognitive Neuroscience. Routledge.


“Neuroscience has been focused of late on neural circuits.... There seems to be an implicit assumption that implementation-level description will not only allow causal claims but also somehow lead to algorithmic and computational understanding (‘naive’ emergence). We contend that such an approach is simply not going to yield the kind of insight and explanation that we ultimately demand from the neurosciences, at least those parts concerned with developing an understanding of the link between brain and behavior that goes beyond causality claims.

“Since the causal-manipulation view by itself will not lead to understanding, a more pluralistic conception of mechanistic understanding can only help neuroscience.... Here we have argued that when scientists ask ‘how does the brain generate behavior,’ they are in fact asking a question best approached through behavioral work, specifically task analysis, aided by theory, that allows behavior to be decomposed into separable modules and processing operations.” Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver & David Poeppel. 2017. “Neuroscience Needs Behavior: Correcting a Reductionist Bias.” Neuron. 10.1016/j.neuron.2016.12.041. [5] pp. 487-8.


“Accordingly, the idea that we may not be able to neatly define discrete brain mechanisms underlying behavior has begun to spread more seriously throughout the broader human neuroscience community.” Noble, Stephanie, Joshua Curtiss, Luiz Pessoa & Dustin Scheinost. 2024. “The tip of the iceberg: A call to embrace anti-localizationism in human neuroscience research.” Imaging Neuroscience. 2:1-10. 10.1162/imag_a_00138. [3] p. 3.


“Overall, based on the evidence summarized above, we endorse a largely antilocalizationist and anti-reductionist view for much of neuroscience. We hypothesize that at least partial holism may be a more serious contender for explaining brain function than has historically been appreciated, yet we do not go so far as to espouse equipotentialism (i.e., that all brain units are interchangeable....” Noble, Stephanie, Joshua Curtiss, Luiz Pessoa & Dustin Scheinost. 2024. “The tip of the iceberg: A call to embrace anti-localizationism in human neuroscience research.” Imaging Neuroscience. 2:1-10. 10.1162/imag_a_00138. [3] p. 5.


“A network attractor of an input-free dynamical system consists of an attracting set X that can be decomposed into a number of invariant sets Ai and orbits (or trajectories) that connect these sets. These connections may or may not involve a threshold, or minimum perturbation which is required for trajectories to traverse the connection.” Ashwin, Peter, Muhammed Fadera & Claire Postlethwaite. 2024. “Network attractors and nonlinear dynamics of neural computation.” Current Opinion in Neurobiology. 84:102818. 10.1016/j.conb.2023.102818. [4] p. 2.


“A heteroclinic network consists of connecting orbits between invariant sets–for those connecting orbits the threshold is zero–with the consequence that the Ai are saddles rather than attractors.” Ashwin, Peter, Muhammed Fadera & Claire Postlethwaite. 2024. “Network attractors and nonlinear dynamics of neural computation.” Current Opinion in Neurobiology. 84:102818. 10.1016/j.conb.2023.102818. [4] p. 2.


“As already mentioned, existence criteria for robust heteroclinic networks will typically only be approximately present in realistic models. Excitable networks–those for which the thresholds for connections are non-zero–are less restrictive, but inputs are required to get any non-trivial dynamics beyond an initial transient. If we go beyond the realm of deterministic models to include stochastic inputs, noisy network attractors originating from heteroclinic or excitable networks cannot be easily distinguished from each other and it becomes a modelling decision as to which type of network attractor to use. The presence of noise may lead to interesting behaviours such as memory of previous visits, even in the low noise limit. It is these noisy network attractors that we argue are a plausible model for neural computations.” Ashwin, Peter, Muhammed Fadera & Claire Postlethwaite. 2024. “Network attractors and nonlinear dynamics of neural computation.” Current Opinion in Neurobiology. 84:102818. 10.1016/j.conb.2023.102818. [4] p. 2.


“A common problem faced by models [of neural networks] ... is the trade-off between sensitivity (the ability to produce responses for inputs that may be of low amplitude) and robustness (the need for the system to function correctly in the presence of noise). Indeed, it is easy to come up with models of cognitive processes that have one but not the other. Excitable network attractors have dynamics that make them robust to subthreshold inputs/noise and sensitive to super-threshold signals. This is because every connection in an excitable network attractor has an associated excitability threshold: the minimum perturbation required to make a switch via that connection. Since the effect of noise is usually much smaller than those of inputs from the environment, the excitability threshold for a connection can be adaptively chosen, for example by evolving weights within the network, to be larger than the effect of noise and smaller than the input amplitude.” Ashwin, Peter, Muhammed Fadera & Claire Postlethwaite. 2024. “Network attractors and nonlinear dynamics of neural computation.” Current Opinion in Neurobiology. 84:102818. 10.1016/j.conb.2023.102818. [4] pp. 4-5.


“The network attractors discussed in this review that can be analysed in detail have mostly simple temporal (equilibrium) dynamics at the invariant sets representing discrete states, as well as simple spatial structure. In practical applications to neural systems, the dynamics will be much more complex than this, both in terms of time dynamics and in terms of spatial patterning.” Ashwin, Peter, Muhammed Fadera & Claire Postlethwaite. 2024. “Network attractors and nonlinear dynamics of neural computation.” Current Opinion in Neurobiology. 84:102818. 10.1016/j.conb.2023.102818. [4] p. 5.


“Yet, not all cases of correlations in nature are considered instances of coding. Climate scientists, for example, rarely ask how rain encodes atmospheric pressure. Another key element of the coding metaphor is that the spike trains are considered messages for a reader, the brain, about the original message: this is the representational sense of the metaphor.” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 3.


“These three elements (correspondence, representation, causality) constitute the conceptual scaffold of the neural coding metaphor....

“The general argument is as follows. Scientific claims based on neural coding rely on the representational sense or at least on the causal sense of the metaphor. But none of these two senses is implied by the technical sense (correspondence). When we examine the representational power of neural codes, we realize that coding variables are shown to correlate with stimulus properties but the code depends on the experimental context (stimulus properties, protocol, etc). Therefore neural codes do not provide context-free symbols. But context cannot be provided by extending the code to represent a larger set of properties, because context is what defines properties (e.g. the orientation of a bar [to a retinal cell]. Thus, neural codes have little representational power.” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 3.


“Finally, the neural coding metaphor tries to fit the causal structure of the brain (dynamic, circular, distributed) into the causal structure of neural codes (atemporal, linear), substituting the arbitrary temporality of algorithms for the temporality of the underlying physical system. The two causal structures are incongruent. Without denying the usefulness of information theory as a technical tool, I conclude that the neural coding metaphor cannot constitute a general basis for theories of brain function because it is disconnected from the causal structure of the brain and incompatible with the informational requirements of cognition.” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 3.


“To him [neurophysiologist John Eccles], the logical solution [to how can brain events give valid pictures of the external world] was a form of dualism much like Cartesian dualism, except he did not believe that the interaction between mind and brain occurred at a single place (Descartes’ pineal gland). Dualism is a natural solution if neural activity is thought to encode information by reference to the external world, because the external world belongs to a different domain.

“A number of philosophers and psychologists have proposed alternative solutions. O’Regan and Noe proposed the analogy of the ‘villainous monster’. Imagine you are exploring the sea with an underwater vessel. But a villainous monster mixes all the cables and so all the sensors and actuators are now related to the external world in a new way. How can you know anything about the world? The only way is to analyze the structure of sensor data and their relationships with actions that you can perform. If dualism is rejected, then this is the kind of information that is available to the nervous system. A salient feature of this notion of information is that, in contrast with Shannon’s information, it is defined as relations or logical propositions: if I do action A, then sensory property B happens; if sensory property A happens, then another property B will happen next; if I do action A in sensory context B, then C happens.” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 11: references: Eccles, John. 1965. “Conscious Experience and Memory.” In: Brain and Conscious Experience. pp. 314-344. Springer.; O’Regan, J.K. & A. Noe. 2001. “A Sensorimotor Account of Vision and Visual Consciousness. Behav. Brain Sci. 24:939-973.


“Consider a fictional organism with two ears – let us call it a Martian iguana.... The iguana is fixed on the ground, and there is another organism – let us call it a frog – which produces sounds. The frog is usually still and produces some random sounds repeatedly, but occasionally it jumps to a new position. The question is: what kind of information can the iguana have access to, based on the acoustical signals at the two ears?

“When a source produces a sound, two sound waves SL and SR arrive at the two ears, and these two sound waves have a particular property: they are delayed versions of each other (SL(t) = SR(t-Δ)). In Gibsonian terminology, there is ‘invariant structure’ in the sensory flow, which is to say that the signals obey a particular law. Thus, the sensory world of the iguana is made of random pairs of signals which follow particular laws that the iguana can identify. This identification is what Gibson called the ‘pick-up of information’. Evidently, ‘information’ is not meant in the sense of Shannon but in the sense of laws or models of the sensory input. Note that the model in question is not a generative model as in predictive coding, but relations between observables, like the models of physics.

“A first interesting aspect of this alternative of information is that the topology of the world projects to the topology of sensory laws. By this, I mean that two different sounds produced by the frog at the same position will produce pairs of signals (SL, SR) that share the same property (the sensory law). This can be assessed without knowing what this property corresponds to in the world (i.e., the frog’s position).

“Thus, the iguana can observe sensory laws that have some particular properties, but do these laws convey any information about where the frog is? For an external observer, they certainly do, since the delay Δ is lawfully related to the frog’s position. For the iguana, however they do not because that lawful relation cannot be inferred from just observing the acoustical signals. Thus, this organism cannot have any sense of space, even though neural coding theories would pretend that it does, based on the correspondence between frog position and the activity of the iguana’s auditory neurons.

“Let us now consider in addition that the iguana can turn its head. It can then observe a lawful relation between a proprioceptive signal (related to the head’s position) and the observed delay Δ, which holds for some time (until the frog jumps to another position). Now when the iguana observes sounds with a particular delay, it can infer that if it were to move its head, then the delay would change in a particular predictable way. For the iguana, the relation between acoustical delay and proprioception defines the spatial position of the frog. We note that the perceptual inference involved here does not refer to a property in the external world (frog position), but to manipulations of an internal sensorimotor model.

“Thus, the kind of information available to an organism is not Shannon information (correspondence to external properties of the world), but internal sensorimotor models. The interest of such models for the animal is that they can be manipulated so as to predict the effect of hypothetical actions.” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] pp. 11-12.


“Technical results are based on the first aspect [correspondence, representation and causality aspects of the coding metaphor], but their interpretation and claimed significance draw on the two other aspects which are not subject to the same scrutiny. Many neural coding theories rely on the idea that the brain manipulates neural representations of stimulus properties, as if the variable of a neural code were a processor register that the brain can store, retrieve and combine arbitrarily, while knowing what the variable refers to. But what is the evidence that such neural representations exist, and what is the evidence that the brain can manipulate spikes in this way?” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 20.


“But the specific point here is not so much that cognition is dynamic, but rather that its neural basis is a dynamical system and must be understood as such. It is a special kind of dynamical system, in that it is composed of units (neurons) which are also dynamical systems. The causal role of spikes in this system is to mediate coupling between these dynamic units. They are transient events that are better understood as actions than as representations (a representation is not an event).” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 21.


“In terms of neural modeling, this [understanding spikes as actions rather than as representations] requires considering sensorimotor systems. Paradoxically, it is customary in systems neuroscience to model perceptual abilities by considering only the corresponding sensory areas. We speak for example of the visual system as a set of anatomical structures from the eye to the visual cortex. But the visual system defined in this way is not actually a system, if it is disconnected from the elements without which it cannot have any function. It follows that models of perceptual systems are in effect not biological models, but chimaeras obtained by attaching a neural model of a sensory area to an abstract construct (‘decoder’) that maps the activity of neurons to descriptors of behavior, and often to an even more problematic abstract construct (‘encoder’) that maps stimulus parameters to model inputs. This methodology embraces both behaviorism (neural activity is only responses to stimuli) and dualism (something else makes sense of neural activity).” Brette, Romain. 2019. “Is coding a relevant metaphor for the brain?” Behavioral and Brain Sciences. 42:e215. 10.1017/S0140525X19000049. [6; unclear page numbering] p. 21-2.


“Bioelectric phenomena are ubiquitous. Lipid membranes are insulators bathed in a conducting milieu. Any lipid membrane can, in principle, support a transmembrane electric field and thus a voltage difference between its faces. The electric field tugs on charges in the membrane; these charges may be in lipid head groups, transmembrane proteins, or membrane-embedded redox molecules.” Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.” Annu. Rev. Biophys. 43:211-32. 10.1146/annurev-biophys-051013-022717. [4] p. 212.


“Mechanisms by which membrane voltage can influence membrane-associated processes [include:] (a) Voltage gating of ion channels is well known, but voltage can also affect (b) conformations of proteins other than ion channels, (c) binding of charged ligands, (d) concentrations of charged amphiphiles, and (e) distributions of charged species within the membrane [from one side of the bilayer to the other].” Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.” Annu. Rev. Biophys. 43:211-32. 10.1146/annurev-biophys-051013-022717. [4] p. 221.


“However, the role of voltage goes further. Every membrane-bound enzyme, receptor, or transporter experiences conformational stresses as a result of membrane voltage pulling on its charged amino acids.... However, the default assumption should be that any transmembrane protein is affected by membrane voltage; the only question is about the size of the effect for physiological voltage swings.” Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.” Annu. Rev. Biophys. 43:211-32. 10.1146/annurev-biophys-051013-022717. [4] p. 222.


“For a membrane voltage of -120 to -180 mV, the equilibrium concentration of cationic species is 100–1,000-fold higher inside the cell than outside it. This fact presents a serious challenge for bacteria: To prevent buildup of toxic cationic compounds, bacteria have evolved a range of cationic export machinery.” Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.” Annu. Rev. Biophys. 43:211-32. 10.1146/annurev-biophys-051013-022717. [4] p. 222.


“Finally, there is evidence that membrane voltage couples directly to the mechanical properties of the membrane, particularly its bending modulus.” Cohen, Adam E. & Veena Venkatachalam. 2014. “Bringing Bioelectricity to Light.” Annu. Rev. Biophys. 43:211-32. 10.1146/annurev-biophys-051013-022717. [4] p. 223.


“Thus, different types of cells may possess a characteristic resting membrane potential, which can be due to a unique profile of ion regulators or to its physiological history, as many of these ion regulators can open and close post-translationally due to other events in the cell. From this perspective, the neuron is a specialized cell type with drastic electric changes. In this brief review, we focus on nonexcitable cells.” Zhang, GuangJun & Michael Levin. 2025. “Bioelectricity is a universal multifaced signaling cue in living organisms.” Molecular Biology of the Cell. 36:pe2, 1-8. 10.1091/mbc.E23-08-0312. [4] p. 1.


“In animals, stable cell volume is maintained by pumping Na+out and K+ in through Na+/K+ ATPase to counter the Donnan effect, unstable osmotic fluctuations caused by intracellular impermeant molecules from cell metabolism.” Zhang, GuangJun & Michael Levin. 2025. “Bioelectricity is a universal multifaced signaling cue in living organisms.” Molecular Biology of the Cell. 36:pe2, 1-8. 10.1091/mbc.E23-08-0312. [4] pp. 1-2.


“Morphogen proteins and transcription factors play crucial roles in embryogenesis, and some of these are downstream of bioelectric cues. The role of bioelectricity is becoming evident in the developmental biology of multiple organisms such as Xenopus, zebrafish, mice, and fruit flies and has been extensively reviewed.... This finding [radical morphological changes following changes to electric channels] suggests that bioelectricity may serve as organ-level or whole-body-level instructional signals.... Bioelectric signals are critical for embryonic patterning that sets organ shapes and sizes, as reported in zebrafish fin-size – altered mutants caused by multiple K+ channels, connexins, and solute carriers. In addition, changes in the bioelectric state of certain embryonic regions in a developmental stage can change the identity of that region to different organs.... Moreover, it was discovered that external electrical currents could regulate tissue size and shape in vitro. It is clear that bioelectricity plays a crucial instructional role in embryonic development.” Zhang, GuangJun & Michael Levin. 2025. “Bioelectricity is a universal multifaced signaling cue in living organisms.” Molecular Biology of the Cell. 36:pe2, 1-8. 10.1091/mbc.E23-08-0312. [4] p. 3.


“Strikingly, a temporary alternation of the bioelectric state gave rise to planaria, which have a permanently altered target morphology–their fragments continue to generate two heads in perpetuity with no further manipulation, motivating a model in which the bioelectric circuit holds anterior-posterior axis polarity as a kind of rewritable memory separate from the genetics.” Zhang, GuangJun & Michael Levin. 2025. “Bioelectricity is a universal multifaced signaling cue in living organisms.” Molecular Biology of the Cell. 36:pe2, 1-8. 10.1091/mbc.E23-08-0312. [4] p. 3.


“Biochemical molecules are deemed as the mainstream signaling carriers. Bioelectricity is different from traditional biochemical signals in many ways. The bioelectric signals generally serve as overall body patterning instruction and coordinate and program overall body anatomy. Essentially, the bioelectric control could be an epigenetic mechanism that guides morphogenesis usually through a robust and dynamic status (i.e. cellular voltage memory). Furthermore, there is a lack of a 1:1 relationship between bioelectric outcome and gene products. This is because bioelectricity is an overall readout of multiple contributors (channels, gap junctions, etc.) and multiple downstream output routes to biochemical machinery on the cellular level.” Zhang, GuangJun & Michael Levin. 2025. “Bioelectricity is a universal multifaced signaling cue in living organisms.” Molecular Biology of the Cell. 36:pe2, 1-8. 10.1091/mbc.E23-08-0312. [4] p. 4.


“However, although highly successful in predicting and explaining many of the electric characteristics of the action potential, the HH [Hodgkin and Huxley] model, nevertheless cannot accommodate the various non-electrical physical manifestations (mechanical, thermal and optical changes) that accompany action potential propagation, and for which there is ample experimental evidence.... Here we present our perspective that this may be an unfortunate state of affairs as these different biophysics-informed approaches to incorporate also non-electrical signs of the action potential into the modeling and explanation of the nerve signal, in our view, are well suited to foster a new, more complete and better integrated understanding of the (multi)physical nature of neuronal excitability and signal transport and, hence, of neuronal function.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] pp. 1-2.


“In the HH-model the propagating action potential, and therefore neuronal excitability, is described as a purely electrical phenomenon and the axon, along whose surface membrane the action potential moves, is (to be) modeled as a modified electronic circuit with the cell membrane, as ‘seat’ of excitability, acting as a capacitor and the attendant ‘ion channels’ as resistors.... As a consequence of this apparent descriptive as well as predictive power, the electronic circuit- and electrical/electrochemical conductance-based framework of neuronal excitability outlined in the HH theory and model was accepted fairly quick in broad areas of neuroscience, in general, and computational neuroscience and neurophysiology in particular. There it has served for the past 70 years, albeit often in some modified form, as a foundation for both theoretical and experimental research from molecular to circuit level and proved instrumental for building and maintaining the understanding of the primary function and modus operandi of the nervous system (including the brain) as a (binary) electronic information processing device with the attendant neuronal networks acting as an electrical wiring grid.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 3.


“... the mainstream ‘HH framework’ has also been criticized for its treatment of neurons as ‘essentially inanimate objects,’ i.e., threshold logic devices, in which information processing is considered solely in terms of membrane and synaptic activities whilst ignoring other (intra)neuronal, biological variables.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 4.


“... Ichii Tasaki, one of the most vocal opponents of the modern, prevailing view of neuronal excitability, observed that ‘with the advent of the age of electronic engineering, .... the traditionally close tie between physical chemistry and physiology was weakened considerably. Driven by the increasing need for advanced knowledge of various electronic devices employed in their experiments, investigators of physiology started to interpret physiological findings in terms of electronic engineers’ concepts, e.g., positive feedback, channels, gates, equivalent circuits, and less emphasis was placed, .... on physicocochemical approaches’. In recent years, however, inspired by the large body of work of Tasaki and others from the 1970's onward, the interest in developing such a broad(er), physico-chemical framework of neuronal excitability, has been rekindled again.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 5: reference/subquote: Tasaki, Ichii. 1982. Physiology and electrochemistry of nerve fibers. NY: Academic Press.


“... HJ [Heimburg & Jackson] derived a wave equation for single electromechanical pulses in lipid membranes (a soliton) and proposed that the quantized, all-or-none, conduction events coupled with reversible mechanical (e.g., thickness, swelling, pressure) as well as thermal changes observed during action potential propagation can be explained by considering the nerve signal as an ‘acoustic pulse along the membrane’ in which the movement of a single adiabatic wave (a soliton) through the lipid bilayer is responsible for axonal conduction of the pulse.... Because of the (at least partly) reversible nature of the structural changes in the membrane (i.e., compression followed by relaxation measured as a change in density of the lipid molecules), once moving, the self-sustaining and localized density pulse will present itself also as a voltage pulse, generally known as the propagating action potential in the electric HH framework. Thus, in this thermodynamics-based framework, movement of the action potential relies on the same fundamental principles that cause the propagation of sound waves in a material instead of the flow of ions or current. Accordingly, the electro-mechanical phenomenology of the nerve signal emerges naturally from the collective properties of the axonal membrane, in which a compression wave propagates, analogous to a sound wave.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 6; reference: Heimburg, T. & A.D. Jackson. 2005. “On soliton propagation in biomembranes and nerves.” PNAS USA. 102:9790-9795.


“... these data provide support for the central idea upon which Kaufmann’s and HJ’s thermodynamic theory of the nerve signal is built which is that the wave front of the action potential propagates as the result of a reversible elastic- process similar to the propagation of sound and not as the outcome of an irreversible- diffusive- process alike the ‘burning of a fuse of gunpowder,’ as proposed by Hodgkin for the HH model.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 7; reference: Kaufmann, K. 1989. Action potentials and electrochemical coupling in the macroscopic chiral phospholipid membrane. 1st edition. Caruaru.


“Being similar in its general principles of structural organization to its counterparts in other cells, the surface of the axon may therefore be considered to comprise of [sic] both the axolemma, a lipid bilayer membrane with transmembrane and membrane-bound proteins and sugars, and an underlying, primarily actin-based, cortical cytoskeleton or cell cortex that is connected to the membrane by specific and non-specific molecular interactions. In the older literature, this cell cortex is also referred to as the ectoplasm which led some investigators to describe the axonal surface as the axolemma-ectoplasm complex.... In fact, studied most extensively for the actin cortex of animal cells, today most biological membranes are thought to be mechanically stabilized by a cytoskeletal structure that provides not only mechanical rigidity but also exerts forces on the membrane.

“In comparison to this very active field of biophysical investigation, however, only limited attention has been paid to the possibility that the cortical, actin-based, cytoskeleton of axons might participate (also) in neuronal excitability, serving the purpose of nerve signal generation and/or conduction.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 8.


“However, whilst extremely successful in providing the foundations for large areas of contemporary neuroscience, in particular computational neuroscience and neurophysiology, at the same time the apparent success of the electronics-based framework of neuronal excitability introduced by Adrian distracted from some inconsistencies in its theoretical foundations and inability to plausibly account for experimental observations that show the nerve impulse to be a multi-physics phenomenon, manifesting itself not only by electrical but also by other co-propagating, non-electrical, signs.” Drukarch, Benjamin & Micha M.M. Wilhelmus. 2023. “Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.” Frontiers in Cellular Neuroscience. 10.3389/fncel.2023.1232020. [4] p. 10; reference: Adrian, E. 1932. Nobel lecture: The activity of the nerve fibres. Nobelprize.org/prizes/medicine/1932/adrian/lecture/


“Through a process of perceptual organization that is still not well understood, the primate visual system transforms visual input consisting of a stream of retinal images into a percept of stable, discrete objects. This process has traditionally been broken down into two separate problems: the ‘segmentation problem,’ which addresses how visual pixels can be grouped into distinct objects within a single image, and the ‘tracking problem,’ which addresses how objects can be identified across images despite changing appearance.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 1.


“Gibson pointed out that the key to understanding human vision is to insert between the 3D environment and the eye a new item, the field of ambient optic arrays.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 2.


“We explain how Gibson’s theory can be formulated in precise mathematical terms and be implemented computationally. Mathematical analysis shows that object surface information is redundantly represented by the field of ambient optic arrays through two of its topological structures: the pseudogroup of stereo diffeomorphisms and the set of infinitesimal accretion borders.... Complete information for perception of objects as discrete, persistent units is contained in the visual environment itself within the field of ambient optic arrays.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 2.


“Given a complex scene containing multiple objects, the goal of segmentation is to identify object boundaries. An efficient way to approach this is to start with a map of all the edges in the image, since object boundaries should be a subset of these edges. The key difficulty is that some edges are ‘texture edges’, while others are true object edges.... However, if an image patch contains an object edge, then on one side of the edge the image patches will be diffeomorphic, but on the other side they will not be, because there will be a piece of the background visible from one perspective but not the other, leading to a one-sided breakdown in diffeomorphism.... Object borders are accompanied by diffeomorphism on only one side. Moreover, we can identify this as the side that owns the edge. By repeating this process across the entire image, we can convert an edge map into a truly informative map of object borders.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 2.


“Once segmentation has been framed in this surface representation framework, the solution to the invariant tracking problem, which has been considered one of the hardest problems in vision, becomes almost trivial. How can we know whether two discrete patches belong to the same invariant surface? We can determine this by checking whether the two patches are connected through a series of overlapping surface patches. Thus, in the surface representation framework, an invariant object constitutes an equivalence class of surface patches, where the equivalence relation is defined by surface overlap. Importantly, the same diffeomorphism machinery for solving segmentation also allows us to compute these surface overlaps, and thus to connect (i.e., track) different views of the same surface over time. Even if a surface undergoes a drastic transformation in appearance (e.g., the front and back views of a horse), as long as successive views are related by local diffeomorphisms, then the tracking process can readily link the views.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] pp. 2-3.


“We prove that segmentation of an image into separate surfaces can be accomplished through detection of occluding contours (which carry information about spatial separation of visible surfaces), and tracking of invariant surfaces in an image sequence can be accomplished by detection of diffeomorphisms (which carry information about overlap relations between surfaces visible from different views).” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 9.


“In our framework, an occluding contour is simply a singularity in the perspective projection, with the associated property of being an infinitesimal accretion border; this concept of occluding contour lies at the foundation of our formulation of image segmentation.” Tsao, Thomas & Doris Y. Tsao. 2022. “A topological solution to object segmentation and tracking.” PNAS. 119(41):1-12. e2204248119. 10.1073/pnas.2204248119. [5] p. 10.


“In his phenomenology, Dreyfus adopted the Gibsonian concept of affordances–the environment is said to consist of action possibilities. Yet, Dreyfus and Kelly were equally drawing upon insights from Gestalt psychology when arguing that when experienced, affordances show up as invitations. And it is this very idea of the demand or invitational character of the environment that receives traction in current ecological thinking.” Withagen, Rob. 2025. “The Gibsonian movement and Koffka’s Principles of Gestalt Psychology.” Theory & Psychology. 35(1):61-77. 10.1177/09593543241280056. [4] p. 62.


“In 1928, after receiving his degree, Gibson moved to Smith College, at which Koffka had been appointed a year earlier. Koffka took a position in the United States some years before the Nazis came to power in Germany. After some visiting professorships, Koffka accepted the generous offer of a five-year research appointment, free from any teaching duties, at Smith College. It was at this small women’s college in Northampton, Massachusetts, that Koffka wrote his landmark book Principles of Gestalt Psychology. In addition, he organized a weekly seminar (from 1928 to 1941) in which Gibson also took part. Gibson was now learning Gestalt psychology from one of its founding fathers.” Withagen, Rob. 2025. “The Gibsonian movement and Koffka’s Principles of Gestalt Psychology.” Theory & Psychology. 35(1):61-77. 10.1177/09593543241280056. [4] p. 63.


“... Gibson introduced the concept of affordances as an alternative to the notion of demand character, stressing the relatively permanent ecological facts in an animal’s environment.” Withagen, Rob. 2025. “The Gibsonian movement and Koffka’s Principles of Gestalt Psychology.” Theory & Psychology. 35(1):61-77. 10.1177/09593543241280056. [4] p. 71.


“Incorporating the idea of demand character in the ecological movement has, in my view, significantly enriched the perspective. From its very inception, the concept of affordances has been severely criticized for insufficiently accounting for our behavior. After listing almost 30 possibilities for action that a single piece of paper offers us (e.g., making paper dolls, writing sonnets, cutting it in pieces) and arguing that the list could be easily extended, Cutting concluded:

“‘My behavior is virtually unconstrained by its affordances. To be sure, it does not afford flying to Baghdad upon, but the exclusion of a large domain of behaviors does not diminish the fact that an infinity remain. To apply to adult human beings, it would seem that the theory of affordances needs full-blown theories of personality and of choice.’

“And more recently, Ratcliffe argued:

“‘Things do not simply ‘afford’ activities; they appear significant to us in all sorts of different ways. It is not helpful to say that a bull affords running away from while a cream cake affords eating. What is needed ... are distinctions between the many ways in which things appear significant to us and, in some cases, solicit activity.’

“The concept of invitations remedies these shortcomings. It captures in what way things are significant to us–it describes what the environment does to the agent.” Withagen, Rob. 2025. “The Gibsonian movement and Koffka’s Principles of Gestalt Psychology.” Theory & Psychology. 35(1):61-77. 10.1177/09593543241280056. [4] p. 72; subquotes: Cutting, J.E. 1982. “Two ecological perspectives: Gibson vs. Shaw and Turvey.” American Journal of Psychology. 95(2):199-222. 10.2307/1422466. p. 216; Ratcliffe, M. 2015. Experiences of depression: A study in phenomenology. Oxford UP. p. 61.


“But it is likely that in earlier multicellular forms morphological plasticity based on an interplay of intrinsic physical properties and external conditions was even more prevalent. This is because ancient organisms undoubtedly exhibited less genetic redundancy and metabolic integration and homeostasis, than modern organism and were thus more subject to external molding forces.” Newman, Stuart A., Gabor Forgacs & Gerd B. Mueller. 2006. “Before programs: The physical origination of multicellular forms.” Int. J. Dev. Biol. 50:289-299. 10.1387/ijdb.052049sn. [3] p. 290.


“We have argued that the inherent material properties of [early, especially] organisms and their tissues, in interaction with the physical environment would have led to stereotypical outcomes that are reflected in structural similarities in body plans across all metazoan taxa....

“If such forms were functionally adaptive or even neutral, they would have served as templates for the accumulation of stabilizing and reinforcing genetic circuitry.” Newman, Stuart A., Gabor Forgacs & Gerd B. Mueller. 2006. “Before programs: The physical origination of multicellular forms.” Int. J. Dev. Biol. 50:289-299. 10.1387/ijdb.052049sn. [3] p. 296.


“The effect of such canalizing evolutionary change is not so much to turn organisms into morphologically different ones, but to turn them more into ‘themselves’: types that are less morphologically plastic and therefore less mutually interconvertible, than ones molded by relatively unconstrained physical mechanisms. This view assigns a different role to natural selection in the process of phenotypic evolution than what is usually portrayed. Rather than being responsible for the origination of novelties it explains their stabilization and spread....

“The molecular basis of canalizing evolution typically involves genetic redundancies, including duplication of developmental control genes, as well as chaperone proteins acting as ‘phenotypic capacitors’.” Newman, Stuart A., Gabor Forgacs & Gerd B. Mueller. 2006. “Before programs: The physical origination of multicellular forms.” Int. J. Dev. Biol. 50:289-299. 10.1387/ijdb.052049sn. [3] p. 296.


“The major role of molecular evolution over the last half billion years, we suggest, has been, rather, the integration and ‘generative entrenchment’ of physically inherent morphological motifs into the developmental repertoire.” Newman, Stuart A., Gabor Forgacs & Gerd B. Mueller. 2006. “Before programs: The physical origination of multicellular forms.” Int. J. Dev. Biol. 50:289-299. 10.1387/ijdb.052049sn. [3] p. 296.


“The neural manifold hypothesis claims that very high dimensional datasets–specifically, in the form of neural population dynamics–have much lower dimensional manifolds that capture their principal structure–that generate specific behaviors (i.e., neural modes).” Favela, Luis H. 2024. “What is next for affordances? Taking brains seriously in organism-environment systems.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 214-231. NY: Routledge. p. 220.


“... the perceptual system is not the system that perceives affordances; it is the system that is the affordance event. As a result, the perceptual system is the brain, body, and environment, and an affordance is a description of the occurrence of a successful activity emerging during the perception-action loop.” Favela, Luis H. 2024. “What is next for affordances? Taking brains seriously in organism-environment systems.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 214-231. NY: Routledge. p. 223.


“The core assumption is that, in humans, three neurocognitive systems (i.e, motor control/dorso-dorsal system, technical reasoning/ventro-dorsal system, and semantic knowledge/ventral system...) are in charge of processing three different kinds of physical relationships (i.e., affordances, mechanical actions, and contextual relationships, respectively).” Osiurak, Francois & Giovanni Federico. 2024. “Affordance and Tool Use: A Neurocognitive Approach.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 232-248. NY: Routledge. p. 233.


“The rationale for distinguishing affordances from mechanical actions is based on findings that have demonstrated that different neurocognitive systems are at work when people process affordances versus mechanical actions.” Osiurak, Francois & Giovanni Federico. 2024. “Affordance and Tool Use: A Neurocognitive Approach.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 232-248. NY: Routledge. p. 240.


“As explained in the previous section, the 3AS model posits that affordance perception is in service of other cognitive processes. Individuals have intentions, that is, high-order goals such as feeding. In some cases, these intentions require solving physical problems by realizing mechanical actions between external objects. The selection of a specific mechanical action eventually biases the perception of relevant affordances.... In this context, affordances are nothing more than the description of motor action possibilities.... The motor-control system perceives only those affordances that are relevant for a specific purpose.” Osiurak, Francois & Giovanni Federico. 2024. “Affordance and Tool Use: A Neurocognitive Approach.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 232-248. NY: Routledge. p. 242.


“Intriguingly, when participants, blindfolded and standing on a force plate, employ hand movements to perceive properties of an object haptically, the temporal structure of their postural center of pressure (CoP) predicts their judgments of object heaviness and length. Despite the object being held in the hand and not underfoot, the relationship between the feet and the ground surface influences the effortful touch of the hand. Even in cases where the participant remains stationary, the temporal structure of the CoP holds direct implications for perceiving objects supported by the shoulders, with differences arising depending on whether the person focuses on the entire object or just a part of it.” Mangalam, Madhur, Louise Barrett & Dorothy M. Fragaszy. 2024. “From Turing to Gibson: Implications of Affordances for the Sciences of Organisms.” The Modern Legacy of Gibson’s Affordances for the Sciences of Organisms. Mangalam, Madhur, Alen Hajnal & Damian G. Kelty-Stephen (eds). pp. 249-267. NY: Routledge. p. 260.


“As a mathematical matter, any system that can be put into the form <S, T, φt> can be studied as a dynamical system, where S is the space of possible states of the system, T is the ordered set of possible times at which the state is defined, and φt is an evolution operator which transforms some initial state x0 ∈ S at t0 ∈ T into another state xt ∈ S at time t ∈ T.” Beer, Randall D. 2023. “On the Proper Treatment of Dynamics in Cognitive Science.” Trends in Cognitive Science. 00:1-14. 10.1111/tops.12686. [4] p. 3.


“Confusingly, ‘cognition’ (interpreted as a causal mechanism) is supposed to explain ‘cognition’ (interpreted as a type of behavior). Even worse, as a matter of historical fact, the proposed cognitive mechanism was actually originally abstracted from our own introspection about what appears to be going on during our cognitive behavior, deeply entangling the two readings.” Beer, Randall D. 2023. “On the Proper Treatment of Dynamics in Cognitive Science.” Trends in Cognitive Science. 00:1-14. 10.1111/tops.12686. [4] pp. 5-6.


“Indeed, the development of dynamical approaches has been deeply entangled with the development of situated and embodied approaches. This leads to the idea that a brain-body-environment (BBE)(or, more generally, an agent-environment) system should be our minimal unit of analysis for understanding cognition.” Beer, Randall D. 2023. “On the Proper Treatment of Dynamics in Cognitive Science.” Trends in Cognitive Science. 00:1-14. 10.1111/tops.12686. [4] p. 7.


“If natural environments were completely random, the best an organism could possibly do would be to react as quickly as possible to each new challenge. Mathematically, a purely reactive agent is just a function, always producing the same action in response to the same stimulus as it stumbles from one crisis to the next. However, real environments exhibit both spatial and temporal locality and their changes from place to place and moment to moment are generally continuous and law-governed. By virtue of its internal state, a dynamical agent can exploit these environmental regularities in a way that a reactive agent cannot, by appropriately coupling its internal dynamics to the dynamics of its environment across a range of ecologically relevant timescales. The central role of internal state in a dynamical account puts the lie to any accusation of stimulus-response behaviorism.” Beer, Randall D. 2023. “On the Proper Treatment of Dynamics in Cognitive Science.” Trends in Cognitive Science. 00:1-14. 10.1111/tops.12686. [4] p. 8.


“Avalanches: A series of bursts of activity (in neural networks) that can be described by a power law in terms of size distribution.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 3.


“Cascade: A physical process characterized by a blending of information or structure built at multiple scales. For instance, when events spread from one scale to another, e.g., cellular to genetic, or cellular to whole-tissue, and then to organ-and to whole-organism scale, what we have is a cascade of effects.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 3.


“Criticality: Criticality is a phenomena that produces power-law distributed avalanche sizes in certain complex systems with several interacting components, such as neural networks, forest fires, and power grids.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 3.


“Multifractality: A generalization of a fractal system in which one fractal dimension is not enough to describe its dynamics; instead, a continuous spectrum of exponents (the so-called singularity spectrum) is needed.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 3.


“We refer to the twin metaphors given to us by Turing: the computer metaphor and the cascade-instability metaphor. The computer metaphor is most dominant in behavioral and cognitive sciences. The cascade metaphor has sooner taken hold in the biological sciences, slipping into the cracks between genetic determinism and phenotypic and spreading through the more fluid aspects of biological form.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 5; references: Turing, A.M. 1950. Computing Machinery and Intelligence. MIT Press; Turing, A.M. 1952. “The chemical basis of morphogenesis.” Philos. Trans. R. Soc. B. Biol. Sci. 237:37-72. 10.1007/BF02459572.


“This cascade instability has been a long-struggling metaphor for the mind struggling to assert itself on equal footing as the computer. What we think has brought the matter to a tipping point is the elaboration of multifractal modeling. Multifractal modeling provides a reliable and versatile empirical anchoring of cascade instability.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 5.


“Multiple data-analytic strategies can help uncover or quantify cascade structure in biological and psychological measurements. The preponderance of these strategies involves estimating the strength of power-law scaling, the tendency for measurements to show events growing or decreasing according to a power-law function of measurement scale. In large part, there are two classes of such analyses. The first involves examining a given measurement, such as a measurement series across space or time to assess how it varies across many measurement scales.... The second class of cascade-assessing analyses involves testing for power-law scaling in histograms or probability-distribution functions (PDFs). In this second class, a measurement is examined by counting individual events (e.g., avalances) within the measure, and the events of each size (or range of sizes) are counted.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] pp. 8-9.


“The elaboration of the grid-based methodology of evaluating evidence of cascades is called multifractal analysis.... To summarize what multifractal analysis does: multifractal analysis examines the heterogeneity of a measurement series by modeling the scaling relationship according to which the proportion of total area under the curve increases with timescale. It estimates this scaling relationship for differently-sized events in the same measurement series and the variability of estimable scaling relationships (i.e., the ‘multifractal spectrum width’) to quantify heterogeneity.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 10.


“... a growing empirical work has analyzed measured postural sway or postural center of pressure (CoP), and converged around two significant points. First, postural sway exhibits the kind of power-law scaling and heavy-tailed PDFs consistent with cascading processes and inconsistent with synthetic surrogate data mimimicking the linear properties of the measurements. Second, the empirical estimates of cascade instability provide compelling predictors of postural outcomes, suggesting that cascade-driven models might inform an explanation of postural control.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 16.


“In a sense, we can think of cascades as the forms that our body takes on to absorb and quickly release fluctuations that might destabilize posture. We can also envision these cascading bodily forms as a resource for resetting our posture to fit the task context. In much the same way Turing’s patterns arose from instability, the cascading instability of posture is critical for producing new bodily patterns to suit the ongoing activity....

“That is, cascade-inspired postural research asks the empirical question of ‘What shape?’ with the expectation that the shape of sway will be hierarchically organized, for example, with finer details branching from or nested within larger details. And the traditional approach has largely only asked ‘How much?’ with the expectation that sway is homogeneous enough to be well represented by a standard deviation.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] pp. 16, 17.


“Cascades are not material-specific; no single tissue alone can embody a cascade. Instead, just as a neurotransmitter operates by having the right shape to fit into a post-synaptic neuron, the physiology here exerts its control through morphology, that is, through geometry. The multiplicative cascades in postural sway and their entailment in these stabilization tasks are deeply rooted in the tensegrity-like structures. ‘Tensegrity’ is a pormanteau term linking ‘tension’ with ‘integrity,’ denoting a prestressed construction of the body that embodies nonlinear interactions across spatial and temporal scales. As noted above, the recipe for the cascade embodied by a tensegrity is not material but formal: tensegrities compromise a balancing of tension and compression elements across many different scales, and often, the compression elements at one scale serve as tensional elements at another.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 19.


“Cascade dynamics characterize how bodies explore their surroundings to arrive at perceptual judgments and, more generally, develop perceptual relationships with events in the world. We might encounter this ‘effort’ most explicitly in the domain of ‘effortful’ or ‘dynamic touch’ using muscular stretch to sense various properties of wielded objects (e.g., heaviness, length, width, shape, and extent along different dimensions).” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 23.


“The bodily response to an object held by the hand is thoroughly global. Occluding the grasped object does not decompose the organism into separable perceptual subsystems (e.g., visual and haptic). Instead, it perturbs a bodywide cascade coursing across disparate motoric degrees of freedom.” Kelty-Stephen, Damian G. & Madhur Mangalam. 2022. “Turing’s cascade instability supports the coordination of the mind, brain, and behavior.” Neuroscience & Biobehavioral Reviews. 141:104810. 10.1016/j.neubiorev.2022.104810. [6; page numbering from bootleg copy] p. 25.


“From a statistical view-point, as in a fractal object, brain activity shows similar properties in a wide range of temporal scales. Self-similarity may not be exact so that brain activity displays multifractality. Some regions of the phase space may take extremely long times to be reached, indicating that brain activity is generically (weakly) non-ergodic. Memory of past activity decays sufficiently slowly that the time it takes for two time-points to totally decorrelate may diverge: scale separation is lost, microscopic fluctuations renormalize given [sic; ‘giving’?] rise to macroscopic effects, and a characteristic time ceases to exist.” Papo, David, Joaquin Goni & Javier M. Buldu. 2017. “Editorial: On the relation of dynamics and structure in brain networks.” Chaos. 27:047201. 10.1063/1.4981391. [4; unclear page numbering] p. 2.


“Once nodes are endowed with their own dynamics, it is possible to distinguish between dynamics in the network, i.e., node dynamics, and topological dynamics on the network, i.e., the temporal evolution of the network’s topological properties. The interdependence of these two dynamics is a defining feature of adaptive networks such as the brain. By gauging the interactions between these two dynamics, it becomes possible to study how this relationship can be related to the emergence of function in healthy brains, normal aging, and in various pathologies.” Papo, David, Joaquin Goni & Javier M. Buldu. 2017. “Editorial: On the relation of dynamics and structure in brain networks.” Chaos. 27:047201. 10.1063/1.4981391. [4; unclear page numbering] p. 2.


“Given the spatial and temporal structure of brain activity, the analysis of the interplay between topology and dynamics in neural activity represents a vast and still insufficiently explored field of investigation.” Papo, David, Joaquin Goni & Javier M. Buldu. 2017. “Editorial: On the relation of dynamics and structure in brain networks.” Chaos. 27:047201. 10.1063/1.4981391. [4; unclear page numbering] p. 2.


“In a sense, anatomical networks can be seen as homeomorphic to resting dynamical ones in the limit of an infinitely slow time scale.... At fast time scales, on the other hand, anatomy is best regarded as a boundary condition for the dynamics....” Papo, David, Joaquin Goni & Javier M. Buldu. 2017. “Editorial: On the relation of dynamics and structure in brain networks.” Chaos. 27:047201. 10.1063/1.4981391. [4; unclear page numbering] p. 2.


“In AS [Anticipatory Systems, book by Robert Rosen] Rosen develops the evolutionary consequences of the fundamental concept of error in predictive models and its relation to system complexity. Rosen states there an important, but often neglected, fact of life that evolution depends on ‘the proliferation of inequivalent models,’ within the organism, and that in this sense he says, ‘biology is the science of mutability; i.e., the science or [sic, ‘of’?] error’. Rosen then concludes: ‘The relation between them (error and complexity) can be summed up in the proposition ‘simple systems do not make errors’. This is because errors occur only in models, not in the systems being modeled. It follows that, ‘a complex system is one in which errors can occur,’ and this leads to Rosen’s concept of a complex system as a system that contains an internal model.” Pattee, H.H. 2007. “Laws, Constraints and Modeling Relation – History and Interpretations.” Chemistry and Biodiversity. 4:2272-2295. [5] p. 2.


“Rosen’s description of the modeling relation in LI [Life Itself] is essentially the same as it was in AS, but in LI he applies it to our brain’s model of life while in AS it was generally applied to the organism’s adaptive internal predictive controls. As a basic epistemology there is nothing novel in this view of a model. It goes back to Plato’s shadow image on the wall of a cave, the projection metaphor still used in physics to describe measurement. The idealist is justified in claiming that all we can experience directly is this shadow. On the other hand, the materialist is justified in claiming that something is casting the shadow. The difficult epistemic questions remain about how we project or encode images and how we judge the correspondence between the consequents of the image in the ideal model and the image of the consequents of material nature.

“There is a more subtle epistemic limitation that Hertz added to his condition for a good model that bears directly on Rosen’s and my disagreement about causal categories. Hertz continues:
‘For our purpose it is not necessary that the (images) should be in conformity with the things in any other respect whatever. As a matter of fact, we do not know, nor have we any means of knowing, whether our conception of things are in conformity with them in any other than this one fundamental respect’.” Pattee, H.H. 2007. “Laws, Constraints and Modeling Relation – History and Interpretations.” Chemistry and Biodiversity. 4:2272-2295. [5] p. 12: reference: Hertz, H. 1984. The Principles of Mechanics. NY: Dover. Orig. German ed.: Prinzipien Mechanik. 1894. p. 2?


“... Rosen placed heavy reliance on the ontological status of Aristotle’s causal categories. I agreed that Aristotle’s description of material, efficient, formal, and final causes was metaphorically useful to illustrate the concept of the inequivalence of models, but I did not see that Rosen provided any persuasive reasoning or evidence for his association of these metaphorical, and rather ambiguous, Aristotelian categories of causality with the physicist’s well-defined inequivalent categories of initial conditions (states), measurement constraints, and natural laws [material, formal, and efficient causes respectively?]. Rosen simply asserts that Aristotle’s causes are ‘tacit’ in the physicist’s categories....” Pattee, H.H. 2007. “Laws, Constraints and Modeling Relation – History and Interpretations.” Chemistry and Biodiversity. 4:2272-2295. [5] p. 13.


“Such analyses [of linearly independent effects that can be addressed with normal Gaussian patterns] are appropriate for systems whose behaviors are determined by the sum of very many components each with independent effects; measurements of many behaviors, however, often exhibit skewed, that is, nonnormal distributions such as power-law distributions. Power-law distributions for many empirical time series exhibit the decay or growth of probability according to a fractional exponent on time, and so systems whose distributions follow a single power law are often called ‘fractal.’” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 2.


“Examples of phenomena typically thought of (and modeled) in these SOC-[self-organized criticality]related terms of local interactions span a wide range of biological systems: schools of fish, flocks of birds, cell motility, and ant foraging. The ongoing influence between each component and all of its neighbors suggests interdependence, but the cross-scale aspect of this interdependence is limited....

“SOC models of schooling fish do not take into direct consideration such microscale factors as the friction of fluid flow around specific heterogeneities in the body surfaces of individual fish or such macroscale factors as the prevailing currents and streams in the surrounding water. For instance, the power-law size distributions of actual fish schools are largely dependent on more global heterogeneities in the dispersion of fish populations. SOC formalisms based in local interactions may not include sufficient cross-scale interactions to support realistic patterns.

“Thus, this third sense of interactivity [not zero interactivity nor self-organized criticality which are local interactions only and of short time duration] involves interactions among factors at many different scales at once.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 5.


“In Lewis Fry Richardson’s view of turbulence, ‘big whirls have little whirls that feed on their velocity, and little whirls have lesser whirls and so on to viscosity.’” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 5; subquote: Richardson, L.F. 1922. Weather prediction by numerical process. Cambridge UP.


“The interdependence of events across scales need not conflict with the observation of local-scale interactions shaping global forms (as in SOC). Rather, we may think of the two perspectives as complementing one another. In turbulence, it is possible that large-scale factors serve as contextual constraints upon smaller scale events and that, within large-scale constraints, small-scale factors may serve to perturb large-scale factors. Turbulence resembles SOC in producing power-law distributions: the ‘whirls having lesser whirls’ leads to a self-similar form. A major difference here is that a power law in turbulence reflects interactions across scales whereas the power laws in SOC need not. For instance, despite the observation of comparable power-law scaling in electrical activity in the brain, in seismic waves, and in stock-market fluctuation, each of these phenomena may exhibit different effects of long-term dynamics on short-term dynamics. More than that, turbulence may produce systems exhibiting a variety of power laws. That is, turbulence may not be singly fractal–or ‘monofractal’–but instead variably fractal–or ‘multi-fractal.’” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 6.


“Gaussian distributions are the sum of very many independent random variables and, as such, they have been equated with pure independence–the antithesis of interactivity. By contrast, the signature of interactivity is likely to be found in distributions reflecting multiplicative random processes, that is, the successive multiplication of potentially interdependent random variables.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 8.


“Multiplicative random processes might be simple to define and simulate, but they are difficult to diagnose empirically. Such processes can generate distributions following the power law and its close cousin the lognormal.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 9.


“Whereas white noise results from taking the derivative of an ordinary diffusion time series whose standard deviation increases with the square root of time, pink noise is the derivative of a different diffusion time series with faster, power-law growth of standard deviation. Empirical evidence of power-law and lognormal distributions is consistent with the hypothesis of multiplicative random processes and interactivity. However, it is not conclusive proof of multiplicative random processes.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 9.


“Essentially, mathematical cascades are abstractions of natural cascades describing how probability distributions evolve across scales. They may spread from relatively dense aggregates at the largest scales to relatively sparse patches at the smaller scales, and they may also describe the congealing of small-scale probability distributions together at progressively larger scales. They are an origin story for how measured fluctuations come to be as variable as we find them in our experiments.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 12.


“Perfectly homogeneous cascades are rare outside abstract mathematical modeling. It is more common for natural cascade phenomena proportions to be fractal, that is, to exhibit singularities with fractional (i.e., noninteger) singularity strengths. Fractional singularity strengths can occur in mathematical cascades when the proportions are perturbed slightly with each step. This perturbation brings us to describing random cascades. In random cascades, an aggregate distribution (i.e., containing 100% of a given measure) is split into progressively smaller samples, but whereas the splitting of proportions in these samples is perfectly even in homogeneous cascades, random cascades involve a random perturbation to the proportions in all samples within each new generation.” Kelty-Stephen, Damian G., Kinga Palatinus, Elliot Saltzman & James A. Dixon. 2013. “A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time series in Ecological Science.” Ecological Psychology. 25:1-62. 10.1080/10407413.2013.753804. [4] p. 15.


“Cognitive performance reveals an interesting mix of stability and instability. For example, cognitive structures, such as those involved in memory and categorization, are conventionally defined by their temporal stability. However, sufficiently detailed measurements show that cognitive performance fluctuates, from memory retrieval and reaction times to syllable durations, acoustical power of vocalizations, and movements of hand and eye.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 52.


“Rather than exemplifying additive white noise, fluctuations in cognitive performance are often closer to ‘pink’ noise. Whereas white noise reflects equally sized fluctuations at all time scales, pink noise consists of a fractal decay of fluctuation size with scale: systematically larger fluctuations at longer time scales and smaller fluctuations at shorter time scales.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 53.


“Thus, a considerable body of work spanning various disciplines (e.g., chemistry, physics, and geosciences) has investigated changes in power-law relationships. For example, injecting unpredictability into a task can weaken pink-noise signals, that is, weaken the relationship between fluctuation size and time scale so that it more closely resembles evidence of additive white noise. This ‘whitening’ of a pink-noise signal may reflect weakened interactivity between the cognitive system and the task environment. Comparable examples of changes in power-law relationships can be found in physiological development over the longer term. For instance, pink noise in the timing of strides in gait will whiten with age and with the development of neurological disorders such as Huntington’s disorder.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 54.


“Consistent with an interaction-dominant view, the distributions of gaze steps (Euclidean distances between consecutive gaze positions) in language comprehension and visual cognition tasks are best fit by power-law-like distributions.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 55.


“If interactions dominate the cognitive system, then the emergence of a new cognitive structure should have the properties of a phase transition, a sudden qualitative change in the organization of the system that arises from a critical instability, the breaking and reforming of componential constraints on the system.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 57.


“Most important, participants who discovered alternation [simple pattern of alternating circular direction among a series of interlocked gears in a perception test on gear charts] showed a peak and subsequent drop in their power-law exponents just prior to discovery. Participants who did not discover alternation showed no significant change in their power-law exponents.... Regardless of whether the time series was obtained from the hand or the eyes [by motion tracking devices for very fine movements], the transition to a new cognitive structure was predicted by a peak and subsequent drop in the power-law exponent.... More important, participants who discovered alternation showed a peak and subsequent drop in their power-law exponents just prior to discovery. Participants who did not discover alternation showed no significant change in their power-law exponents.... Regardless of whether the time series was obtained from the hand or the eyes, the transition to a new cognitive structure was predicted by a peak and subsequent drop in the power-law exponent.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] pp. 57-8.


“The minimal crucial difference between component-dominant and interaction-dominant systems is that in an interaction-dominant system the internal functioning of each component is dependent on the functioning of other components. Behavior in an interaction-dominant system is a macroscopic phenomenon emerging from the interactions among all the components. Given that behavior is softly and temporarily assembled in interaction-dominant systems, stability is a phenomenon of interest, just as change is.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 58.


“Because the collective interactions across the components give rise to emergent behavior, as opposed to the activity of any one component, the notion of information exchange is poorly suited to interaction-dominant systems.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 59.


“Importantly, diffusion only occurs when there is a gradient of energy or matter within the medium. Traditionally, models of diffusion have begun at the level of the motion of a single particle. This motion is quantified in terms of the mean squared distance (MSD) covered by the particle as a function of time. In ordinary Newtonian models of diffusion, average squared distance increases as a linear function of time. Power-law relationships emerge when MSD increases faster than a linear function of time but not faster than a quadratic function of time. Because of the fractional exponent on time, between the whole numbers 1 and 2 is often called fractal diffusion. Fractal diffusion (i.e., diffusion in the power-law range) occurs in complex physical media in which the gradients of energy and matter are heterogeneous.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 59.


“What does diffusion have to do with cognition and behavior? Cognition, because it involves the activity of physical components, must consume energy. Energy consumption will change the local gradients of energy and matter, and therefore the speed at which energy flows through the system, that is, the rate of diffusion. Thus, the activity that entails cognition must change the rate of diffusion in the complex physical materials in which it occurs.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] p. 59.


“Regardless of whether we view behavior as intrinsically meshed with cognition or just tightly time-locked to cognition, it follows that fine-grained measurements of behavior carry information about the diffusion rates of the structures generating it. The fluctuations emphasized above are the source of this information about diffusion rates. Fluctuations in macroscopic behavior are the aggregated, gradient-dependent movements of material across many scales of the cognitive system that support the behavior. The power-law exponent relating the magnitude of fluctuations to the time scale quantifies the rate of diffusion.

“In very complex material, such as biological tissue, diffusion is likely to occur at different rates across different scales. Here, a single power-law exponent will not suffice to describe energy flow through the system. Rather, we need a range of power-law exponents to describe the spectrum local rates of energy flow within the system. Whereas diffusion depending on a single fractional exponent relating MSD to time is fractal, diffusion that depends on multiple fractional exponents relating MSD to time is multiply fractal, or more simply termed multifractal.” Dixon, James A., John G. Holden, Daniel Mirman & Damian G. Stephen. 2012. “Multifractal Dynamics in the Emergence of Cognitive Structure.” Topics in Cognitive Science. 4:51-62. 10.1111/j.1756-8765.2011.01162.x. [4] pp. 59, 60.


“Although we are accustomed to thinking that the forebrain orchestrates most human behaviors, many complex responses, such as feeding–the coordination of chewing, licking, and swallowing–are actually made up of relatively simple, stereotypic motor responses governed by ensembles of neurons in the brain stem.

“The importance of this pattern of organization in human behavior is clear from observing infants born without a forebrain (hydranencephaly). Hydranencephalic infants are surprisingly difficult to distinguish from normal babies. They cry, smile, suckle, and move their eyes, face, arms, and legs. As these sad cases illustrate, the brain stem can organize virtually all of the behavior of the newborn.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Clifford B. Saper & Joel K. Elmquist, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 981.


“Unlike most of the brain, which is protected by a blood-brain barrier, the area postrema contains fenestrated capillaries that allow its neurons to sample the contents of the blood stream. These neurons, when they detect a toxin, activate a pool of neurons in the ventrolateral medulla that control a pattern of responses that clears the digestive tract of any poisonous substances. These responses include reversal of peristalsis in the stomach and esophagus, increased abdominal muscle contraction, and activation of the same motor patterns used in the gag reflex to clear the oropharynx of unwanted material.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael N. Shadlen & Eric R. Kandel, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 994.


“Neurons in this group [dorsal respiratory group] receive respiratory sensory input, including afferents from stretch receptors in the lungs and peripheral chemoreceptors, and participate in such reflex actions as limitation of lung inflation at high volume and the ventilatory response to low oxygen (hypoxia). The ventral respiratory group, a column of neurons in and around the nucleus ambiguus, coordinates respiratory motor output. Some of these neurons are motor neurons with axons that leave the brain through the vagus nerve and innervate accessory muscles of respiration or premotor neurons that innervate the phrenic motor nucleus, whereas others form a pattern generator, the pre-Boetzinger complex, that generates respiratory rhythm.

“The intrinsic rhythmicity of the pre-Boetzinger complex is so resilient that, even in ... able independently to generate a respiratory rhythm....” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael N. Shadlen & Eric R. Kandel, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 995.


“Other inputs to the respiratory pattern generator come from the circuitry mediating particular behaviors, as breathing must be coordinated with many motor actions that share the same muscles. To accomplish this coordination, respiratory neurons in the medulla receive input from neuronal networks concerned with vocalization, swallowing, sniffing, vomiting, and pain....

“Voluntary motor pathways can take over the control of breathing during talking, eating, singing, swimming, or playing a wind instrument. Descending inputs cause hyperventilation at the onset of exercise, in anticipation of an increase in oxygen demand. In fact, this leads to a sustained drop in blood CO2 during exercise–the opposite of what would be expected for a negative feedback control system....

“Thus, the respiratory control system is a fascinating example of a brain stem pattern generator that must be sufficiently stable to ensure survival yet flexible enough to accommodate a wide variety of behaviors.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Michael N. Shadlen & Eric R. Kandel, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. pp. 997, 998.


“Although largely involuntary, autonomic and neuroendocrine responses are tightly integrated with voluntary behavior executed by the somatic motor system. Running, climbing, and lifting exemplify voluntary actions that have metabolic, cardiovascular, and thermoregulatory consequences. These needs are automatically met by the autonomic and neuroendocrine systems through changes in cardiorespiratory drive, cardiac output, regional blood flow, heat dissipation, and fuel mobilization. Such compensatory changes are implemented primarily by feedforward central commands, supplemented by reflexes activated by sensory feedback....” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1011.


“Most physiologists today have adopted a ‘distributed settling point’ model [in place of the traditional set point idea of a thermostat] that incorporates strong feedback control of multiple sensory/effector loops. With body temperature, for example, there is no single specific set point and no location in the brain where a single set point is encoded and error detection takes place; in short, there is no thermostat. Instead, there are multiple temperature detectors located in different sites (skin, core, and brain), and each is coupled through neuronal pathways that traverse the preoptic area on their way to different body temperature effectors (cutaneous blood vessels, sweat glands, brown fat metabolism, shivering, and behavioral pathways). When engaged, each of these effectors impact body temperature. The apparent set point for body temperature is in fact the emergent settling point that results from the combined activities of the multiple feedback-informed afferent/efferent loops. As we will see later, this nuanced model also applies to regulation of blood pressure, blood osmolarity, and body fat.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1013.


“The autonomic system is divided into three divisions: sympathetic, parasympathetic, and enteric. All neurons in sympathetic and parasympathetic ganglia are controlled by preganglionic neurons whose cell bodies are located in the spinal cord and brain stem. The preganglionic neurons synthesize and release the neurotransmitter acetylcholine (ACh), which acts on nicotinic ACh receptors on postganglionic neurons, producing fast excitatory postsynaptic potentials and initiating action potentials that propagate to synapses with effector cells in end organs. The sympathetic and parasympathetic systems are differentiated by five criteria:

“1. The segmental organization of their preganglionic neurons in the spinal cord and brain stem
“2. The peripheral locations of their ganglia
“3. The types and locations of end organs they innervate’
“4. The effects they produce on end organs
“5. The neurotransmitters employed by their postganglionic neurons.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1016.


“The entire gastrointestinal tract, from the esophagus to the rectum–and including the pancreas and gallbladder–is controlled by the system of enteric ganglia. This system, by far the largest and most complex division of the autonomic nervous system, contains as many as 100 million neurons.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1019.


“Walter Cannon, in addition to introducing the concept of homeostasis, also appreciated that this fight-or-flight response is a critical sympathetic function.

“Two important ideas underlie this insight. First, the sympathetic and parasympathetic systems play complementary, even antagonistic, roles; the sympathetic system promotes arousal, defense, and escape, whereas the parasympathetic system promotes eating and procreation. Second, actions of the sympathetic system are relatively diffuse; they influence all parts of the body and once turned on can persist for some time. These ideas are behind the popular notion of the ‘adrenaline rush’ produced by excitement, as by a roller coaster ride.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1022.


“Similarly, the sympathetic and parasympathetic systems are often partners in the regulation of end organs. In most cases, ranging from the simplest reflexes to more complex behaviors, all three peripheral divisions of the autonomic system work together.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1022.


“The baroreceptor reflex is one of the simplest mechanisms for regulating blood pressure and further illustrates coordinated homeostatic control by antagonist sympathetic and parasympathetic pathways. It prevents orthostatic hypotension and fainting by compensating for rapid hydrostatic effects produced by changes in posture....

“When neurons in the ventrolateral medulla detect the decrease in afferent baroreceptor activity produced by low blood pressure, they produce a reflexive suppression of parasympathetic activity to the heart and stimulation of sympathetic activity to the heart and vascular system. These changes in autonomic tone restore blood pressure by increasing heart rate, the strength of cardiac contractions, and the overall vascular resistance to blood flow through arterial vasoconstriction.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1023.


“Because the intracellular content of osmotically active molecules is relatively fixed over the short term, increases in blood osmolarity cause cells to shrink, and conversely, decreases cause cells to swell. This is particularly dangerous for the brain because it is encased by the rigid skull. with extreme hyperosmolarity (too little water), the brain shrinks, pulling away from the skull and tearing blood vessels. With hypo-osmolarity (too much water), the brain swells, causing cerebral edema, seizures, and coma. To prevent such incidents, the brain acts to maintain normal osmolarity. It does this by detecting changes in osmolarity and then regulating the motivation to drink (thirst) and the kidney’s capacity to excrete water.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1031.


“In a simplified stimulus-response view of behavior, one might assume that neural detection of water or energy deficiency (the stimulus) is hardwired to motor pathways for drinking or eating (the response), and thus analogous to the knee-jerk stretch reflex. However, this cannot be the case because the response that can be employed to obtain food, all motivated by the deficiency stimulus, are remarkably varied and complex–to such a degree that they could not be hardwired. Indeed, animals can complete an infinite number of complex operant learning tasks to obtain water or food rewards.

“The challenge to understanding motivational drive is to devise a model that accounts for the ability of deprivation states to induce behavior that is remarkably varied and complex, while remaining completely specific for one goal. Two compelling theories are relevant. According to incentive motivation theory, deficiency increases the reward value of food and water. Drive reduction theory posits that deficiency generates an aversive state, the resolution of which is thought to motivate behavior. Notably, these two views are not mutually exclusive and may in some ways be two sides of the same coin.” Kandel, Eric R., John D. Koester, Sarah H. Mack & Steven A. Siegelbaum (eds.) [Bradford B. Lowell, Larry W. Swanson & John P. Horn, section authors] 2021. Principles of Neural Science, 6th Edition. NY: McGraw Hill. p. 1038.


“Translating cutting-edge climate science into laws, regulations, and engineering guidelines takes people, money, and political will, and a vast majority of jurisdictions are largely flying blind.” Keenan, Jesse M. 2025. North: The Future of Post-Climate America. Oxford UP. p. 33.