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The conceptual architecture
Each concept has one primary home. Cross-links preserve the connections that a hierarchy alone cannot show. Select a concept to see its status, sources and relationships.
The source collection
The backbone outline, supplied papers and recorded talks. Source passages use document paragraphs, PDF file pages or video timestamps, so every reference can be located.
All 16 papers and the Inspiration talk transcript have been read in full. Source notes record the scope and limits of that review. Cited underlying studies were not independently audited.
Source coverage
Counts refer to source units: one nonempty paragraph in the outline, one PDF file page, or one minute of a talk transcript. A direct reference identifies a cited unit; it does not establish that every claim on that page has been audited.
Source unit status
Units
Questions about mind life and agency
Seven entry points into Michael Levin’s framework. Read straight through or choose a question. The answers distinguish experimental examples, explanatory models and proposals, and link to the corresponding concepts in the map.
Levin’s starting point is to investigate capacities rather than sort systems by how human they look. Following William James, intelligence means reaching a goal by different means, especially when obstacles make a familiar route unavailable. Learning, compensation and flexible goal pursuit can be investigated experimentally across different systems. Exploratory play raises a further question that this definition does not yet quantify. TAME organizes these investigations through empirically useful ways of predicting, communicating with and intervening in a system.
The relevant behavior can occur in physiological or anatomical space as well as ordinary movement. A tissue repairing its shape and an animal finding a route home can be compared at the level of flexible goal pursuit, while their mechanisms and capacities remain different. The spectrum of persuadability asks which interaction works: physical manipulation, changing a setpoint, training or something more reciprocal.
Cognition and consciousness are distinct questions. Cognition and intelligence concern observable competencies; how consciousness relates to those competencies remains unresolved. TAME by itself makes no predictions about consciousness. Levin proposes asking what kind and how much, rather than treating either intelligence or consciousness as a simple yes-or-no property.
The framework considers several sources and scales of goals. Physiological systems maintain viable conditions; cell collectives can regulate larger anatomical outcomes; evolution and development shape the architectures through which those outcomes become possible. A goal representation influences present activity without requiring a future event to act backward in time.
A major open problem is how local concerns combine into a new collective goal. Shared stress, physiological coupling and common memories are proposed mechanisms. In stress sharing, one component’s error can affect neighboring components, making resolution of that problem relevant to their own regulation. Assembling competent parts does not yet let us predict every goal of the resulting whole.
The Platonic-space proposal supplies a further, metaphysical answer: embodiments may express patterns drawn from a structured non-physical space. That interpretation is a distinct hypothesis, with a proposed research program. Unexpected behavior alone cannot establish it, and the operational study of goals does not require accepting it.
A Self is treated as a goal-directed unit with an effective boundary, capacities for sensing and acting, and a range of concerns. Its cognitive light cone describes the spatial and temporal scope of its goals, which can extend beyond what it currently senses or controls. A collective can therefore have concerns extending beyond those of any one component; whether it does requires investigation.
Bioelectric coupling is one proposed form of cognitive glue. Shared physiological signals can coordinate cells around tissue-level outcomes. Memory anonymization proposes that a trace exchanged without an ownership label can become part of a collective history. These mechanisms may help explain how a larger goal-directed unit is constructed, rather than assuming that a familiar body automatically supplies its boundary. The proposal requires selective sharing: differentiated parts and layers still matter, so greater connectivity alone does not guarantee a more capable Self.
Selves may be nested, overlapping or changeable. Regeneration, chimerism and the continued viability of cells after an organism dies make these questions experimentally concrete. Functional integration still leaves the distinct question of how, or whether, smaller experiences relate to a larger subject’s experience. Survival of an organism’s cells does not by itself show that its former conscious subject survives.
The Platonic-space proposal offers an interpretation of consciousness as the experience of a pattern ingressing through a physical embodiment. In this account, the combination problem connects collective intelligence in the embodiment with patterns of different cognitive scale. This is a proposed account to investigate, distinct from the experiments establishing physiological coordination.
The memory bowtie describes experience compressed into a limited trace and later reconstructed by an agent that may now have a different body, context or set of needs. The trace helps the current Self construct a model for future action. The framework connects this architecture in cognition with development through an egg: in each case, a compressed starting point is interpreted into a much richer outcome.
Metamorphosis and brain regeneration motivate the question of how learned information remains useful when the machinery that interprets it changes. Levin emphasizes continuity of what matters to the agent, including remapping information to a changed body. The selflet perspective treats memory as communication between temporal versions of a being. The framework also considers memory transfer between organisms and the possibility that neural networks interpret information stored elsewhere in the body. These examples raise separate questions about storage, transfer, interpretation and reconstruction.
The active-pattern proposal places fleeting thoughts, persistent thoughts, personality fragments and full personalities on a continuum of patterns in a cognitive medium. A host helps constitute its thoughts, while those patterns can help sustain or change the host. Their degree of cognitive competence is a question for experiment. This proposal is distinct from the evidence for reconstructive recall and the further question of experience.
First determine what the tissue regulates and which signals alter its behavior. If morphogenesis uses feedback to reduce the difference between present anatomy and a target, an effective intervention may change a setpoint or physiological state that many cells then act on together. The TOTE model makes the loop explicit: compare, act, compare again and stop when the discrepancy is resolved.
Non-neural bioelectric networks provide one experimentally accessible interface through ion channels, membrane potentials and gap junctions. The supplied papers describe effects on growth, patterning and persistent regenerative outcomes. The same signal can have different effects in different tissues, so context and interpretation matter. Decoding the bioelectric code means learning which distributed states correspond to which anatomical outcomes; a general-purpose decoder remains a research goal.
The body’s own responses to unfamiliar stressors can guide intervention design: cells, organoids and biobots can serve as agential sensors. A related proposal treats some persistent disease states as self-reinforcing physiological patterns. Such patterns may become targets for interventions aimed at changing tissue models, learned expectations or regulatory states. This motivates somatic psychiatry as a research agenda.
An anatomical compiler would translate a desired organ-level outcome into instructions that competent tissue can use. Tissue training, bioprompting and somatic psychiatry are related research directions. Model-system findings motivate them, but they do not establish general clinical treatments for cancer, aging or human limb regeneration. The next question is which specific system, signal and outcome can be tested reliably.
Novel organisms and hybrid systems reveal capacities beyond familiar anatomy. Xenobots made from frog cells and Anthrobots made from adult human cells let researchers study beings whose form and capacities were not directly selected for, designed or trained. Chimeras and cyborgs extend this exploration through combinations of evolved and engineered components. The latent-space proposal emphasizes that the same cellular hardware can support many forms and behaviors; a genome does not specify every outcome available to a cellular collective.
Darwin’s agential materials asks how developmental competence changes evolution itself. Error-correcting morphogenesis can buffer mutations and make workable intermediate forms easier to reach. The proposed intelligence ratchet describes feedback between this competence and selection: when development compensates for variation, selection can increasingly act on the capacity to repair and adapt. High-level control variables, including bioelectric goal states, may make anatomical change more tractable.
Engineering with competent materials means allowing components to solve some construction and adaptation problems themselves. It also means investigating the finished system: knowing its assembly history does not reveal every capacity or goal. Polycomputing extends that lesson by asking what additional functions the same activity supports for different observers. These must be usable functions: one material example carries AND and XOR operations through different vibration frequencies, with a readout distinguishing them.
AI belongs within this wider field of diverse intelligence. Levin proposes investigating potentially alien minds rather than assuming that humans provide the appropriate comparison. Questions about creation, trust, values, identity and responsibility also arise in parenthood and biology; AI makes these older questions more immediate. The ingression framework offers a further interpretation of bodies and algorithms as interfaces through which diverse patterns become embodied.
The Inspiration talk links discovery, biology and engineering through the difference between invested effort and acquired competence. A free lunch is competence not paid for through the expected work of designing, evolving or training a controller. The proposal extends across substrates. It asks researchers to state expectations before observing results and to favor models that help discover the next capability, rather than explaining away surprises afterward. Inspiration is linked in the map to machines, model limits and Platonic space.
Creating a potentially significant agent brings responsibilities for its welfare, relationships and environment. Levin asks how to help unfamiliar or partly unfamiliar beings have a good life, including what mental health care and psychoanalysis could mean for novel minds. These responsibilities extend to biological, artificial and hybrid beings.
The ethical ambition includes addressing the medical suffering that already exists. Attention to the risks of new interventions should also account for the opportunity cost of inaction. Morphological freedom concerns the ability to change a constraining embodiment: communicating new anatomical goals to cell collectives could expand the bodies in which a person can live.
Synthbiosis names the aspiration for biological, artificial and hybrid beings to flourish in interdependence. It connects communication with unfamiliar minds to expanding understanding, capability and compassion. Freedom as long-term self-transformation concerns sustained practices that change future capacities, preferences and likely thoughts toward the kind of being one hopes to become.
Reading note: this editorial synthesis follows a complete reading of the supplied outline, 16 papers and the Inspiration talk transcript. Experimental examples, theoretical interpretations and ethical proposals carry different kinds of support. The papers’ cited studies were not independently audited; automatic captions may contain errors, and the talk’s slides were not reviewed.
Michael Levin · Terms and definitions
Glossary
Definitions in Michael Levin’s own words, collected from the Levin Lab FAQ.
My take on existing terms
How I use familiar terms in studying minds, life, and agency.
How I use definitions: context, scale, and decision-making
I think each of these is a metaphor, like all scientific concepts - a package of methods and relationships between ideas that offers ways to think. It is a lens through which we can choose to view a particular context. All of these terms are observer-dependent and relative to a reference frame (a problem space), and each one has advantages and limitations. The quality of each lens is determined by how much insight they enable in a specific context (not by philosophical pre-commitments). In a science/engineering setting, this means, how much prediction, control, and discovery they facilitate - ability to drive novel questions and research progress. In other contexts, it may mean how much each definition contributes to an ethical, mutually-beneficial relationship with some system or being. Clarity on what one means by these is essential when embarking on a specific discussion about some system; but I see this task as less about demarcating precise formal definitions and more about specifying the operational properties of the term - what is it supposed to do for us, how does it work, how do we recognize when it's happening, how to measure it, and how to use it to drive discovery forward.
A scale of analysis should be made explicit in all definitions. For example, many attempts to define “decision-making” break down because of an implicit focus on molecular events (at which point, anything like “making a decision” slips through our fingers and disappears). But this is not the only level of analysis and may be sub-optimal in many circumstances. For example, the degree to which an event is a "decision" has to be judged with respect to how much the optimal understanding and control of that event by an observer (e.g., scientist, or other biological system) will require knowledge of the large-scale goals, adaptive cycles, reward functions, and context - it is only defined within a context that may include evolutionary or engineering cycles. Specifically, the degree to which a process is a decision is proportional to the size of the informational light cone of spatiotemporal events that need to be considered for optimal understanding and management of that event. Very mechanical behaviors can be well-captured by the immediate, local pushes and pulls occurring to an object. In contrast, to properly understand the events happening in a complex agent need to consider events in the distant past (due to memory), the distant future (due to predictive capacity), and in other locations (due to integrated information across space). Similarly, the closely-related concept of free will cannot be sought in molecular events (where only mechanical necessity and quantum randomness can be found) but in the large-scale behavioral functions that are best understood as a cognitive system curating its own structure and future possibilities by rich chains of action that take place over time (niche construction, for and of, the future self).
Agent
a system that executes a perceptual control loop, in which it takes measurements, compares to setpoints, and tries to minimize error with respect to its expectations about its own state and those of the outside world.
Agents are demarcated by a cognitive light cone set by the spatiotemporal size of the goals it can pursue. Agents sense, act, and make models of the world around them (and themselves). Agents can be described at multiple scales, including the microscopic scale that emphasizes determinism and backwards-looking explanations for events. What all significant minds have in common, regardless of composition or origin story, include: behavior that requires taking its perspective to predict and communicate with it, responsibility for setting (and managing) a boundary between itself and the outside world (at multiple scales), responsibility for interpreting its own memories (behavioral, genetic, etc.), and responsibility for hacking its parts toward larger-scale goals. Use of the word "Agent" refers to a frame that emphasizes:
3rd person (external) recognition of a system with competency to navigate problem spaces. Low-agency tools (the tools of physics) are not good at detecting agency (this is why they see chemistry, not minds). Agency is best detected by another agent's cognitive processes in (i.e., external observers such as scientists, parasites, and conspecifics), via perturbational experiments). Detection of Agency is fallible, observer-dependent (not objective), and empirical.
2nd person perspective - attempts to control other agents in a communication/instruction mode - tools along the Continuum of Persuadability (again, dependent on the skill of the observer to deploy the correct tools to exert control and hack the agent to desired outcomes). Control doesn’t mean unidirectional exertion of will on another - control is also a part of successful communication for example, where one tries to construct messages that will have desired impact on the course of another mind’s activity.
1st person perspective - significant agents require us to take its perspective to optimally predict its behavior or communicate with it.
Aging
a systemic, time-dependent loss of competency on all scales of the body's operations.
See here for the intersection of the aging and longevity field and diverse intelligence.
Bioelectricity
refers to the set of phenomena mediated by voltage gradients, ion fluxes, and electric fields in living tissue, especially focused on non-neural contexts such as embryonic morphogenesis, regeneration, cancer, etc. Bioelectricity is the evolutionary precursor to the brain's electrophysiology, using the same molecular machinery and executing many similar functions to store, process, and integrate information across levels. More deeply, our work suggests that bioelectricity: a) is the cognitive glue that enables individual cells to work together as a collective intelligence that flexibly implements setpoints in anatomical space, b) is a modality that enables a bowtie-like architecture that abstracts over chemical details of ions into system-level voltage patterns, c) is a set of mechanisms that enable reprogramming of outcomes in ways quite distinct from the genetic hardware defaults, and d) is a powerful interface by which cells talk to each other and which we as bioengineers can exploit to communicate novel goals to cell groups in regenerative medicine contexts. The bioelectric code is the language of the somatic internal monologue - its (non-linguistic, as far as we know) representation of its current and future morphological states, which we try to crack for biomedical and bioengineering applications.
(of cells and tissues, or other unconventional agents) - a degree of ability of a system (such as a group of cells) to solve a problem in anatomical, physiological, metabolic, or transcriptional space (see reviews here or here). This is a generalization of the narrower term in developmental biology. Here, competency refers to the efficacy with which a system navigates that space despite interventions and novel perturbations. It is not the same definition of “competency” as used in developmental biology, but there is overlap. For example, when using molecular-genetic prompts such as the “master” eye gene Pax6, the frog embryo looks to only be competent to form eyes in the anterior neurectoderm. But if one uses bioelectric prompts, we find out that in fact almost any tissue in the embryo can make an eye (even gut or tail). Thus, the degree of competency (in the traditional narrow developmental sense, or in my expanded basal-intelligence sense) that is revealed is a function of how one poses the problem and makes observations. In other words, it is easy to under-estimate the competency of a system if we, as observers, are limited in what prompts and behaviors we know how to recognize and deploy. This observer-dependence is critical: competency does not have some unique, objective, correct value, but rather reflects the agency that some observer has been able to detect - it's a model that an observer (or indeed, the system itself) has about that system's ability to solve problems in some specific space.
the kind of information I'm interested in is meaningful, interpreted information: salience that is compressed and generalized (and then remembered, and recalled by being continuously transformed) from signals by a receiver/observer and integrated into their future actions.
a term that emphasizes 1st-person inner perspective consisting of valence, attention, and decision-making. Selves are owners of goals, preferences, and memories that do not belong to any of their parts - an emergent whole implemented by an interlocked triad of: an option space within which it operates, a cognitive light cone demarcating the size of goals in that space which the system can pursue, and a set of cognitive processes that allow the system to navigate the space with some degree of competency). Selves live forward in time, being essentially decision-making systems that cannot be effectively circumscribed by deterministic backwards-looking causal stories. Selves arise when some collection of parts all buy in to the same shared story (model) of themselves and their environment (e.g., when cells become coupled by gap junctions, which allows them to share physiological engrams of experiences and thus build up the same internal models/memories, become a coherent emergent morphogenetic agent like an embryo). A defining feature of significant Selves is that they do not try to observe reality "as it is" (preserving the fidelity of information about the environment's microstates) but instead compress and coarse-grain it into memories that will allow their future Selflets to preserve salience via improvisation, not detail. Selves interpret and reinterpret their memory engrams as they do with any input or signal, not being tied to how the sender (including their own past Selflet) meant it but doing the best it can to extract adaptive meaning in current internal and external circumstances (this is an example of Polycomputing). Selves are primarily sense-making systems, which reinforce their own reality via models of the world in which they exist.
Goal
A goal exists whenever using the tools of goal-based frameworks (e.g., cybernetics) give engineers good traction on the system (prediction and control). This is not the same thing as “purpose” (high-level goals where an agent has the meta-cognition to think about having goals and what they might be). And, I think even bowling balls and such have the tiniest, most basic, nano-goals because they follow least-action laws. The word “goal” is useful to indicate situations where an engineer does not need to micromanage some aspects of the system because they can offload that onto the system itself (when it has some ability to pursue goals without you controlling each step). A goal-directed system lets you, as the engineer, trust it to do stuff when you’re not around. So, does a bowling ball have such a property? Yes. Imagine you’re an engineer building a Roller Coaster at an amusement park. What you have to do is engineer a way for it to get up the hill. But you don’t have to worry about getting it down the hill - it already does that, autonomously. Following energy gradients is the simplest, most basic goal there is, and thus it’s not 0 on the scale of goal-directedness. Sure, it’s incredibly minimal and doesn’t quite look like the goals smart beings have (although some human psychological phenomena can be cashed out as a system following an energy landscape in terms of basic drives and rewards etc.), but I think it’s not 0. Crucially, thinking of goals this way (as a continuum) avoids pseudoproblems brought on by binary frameworks, such as the following. Do single cell organisms have goals? If so, then little bags of chemical networks have goals. If not, then given that we all formed from single cells (fertilized egg), when during the gradual, extended process of development, did goal-directedness show up?
Intelligence
a degree of capacity for problem-solving, also known as competently navigating a problem space to reach specific goals despite barriers and novel circumstances. Intelligence is an estimate made of a system by some other observer (i.e., it's in the eye of the beholder and relative to that observer's skill in formulating hypotheses about problem spaces, goals, and competencies and then using perturbative experiments to test them). Intelligence is but a subset of cognitive repertoire more broadly, because there are other components in some systems like exploration, play, ability to change goals (2nd order intelligence), ability to commit to become better at meeting goals and setting goals (3rd order) etc.
that are not included in this simple definition.
Embodiment
I think embodiment is very important for the development of intelligence but it doesn’t have to be an obvious body that moves in 3D space, like a conventional motile animal or robot. Biology implements embodiment in many different spaces, many of which are hard for us to visualize: physiological state space, metabolic space, transcriptional (gene expression) space, anatomical morphospace, linguistic space, etc. The key thing about embodiment is bi-directional engagement with a space in which the perception-prediction-action loop can be executed. Moreover, the body should offer constraints (akin to the laws of physics in familiar 3D space), and, enablements resulting in the offloading of cognitive tasks to the environment as studied in morphological computation (in other words, the environment can be exploited for affordances as well as providing constraints that offer innumerable problems to be navigated).
Life
I actually don't worry much about the definition of "life" (I'm much more interested in the spectrum of "cognition", which I think is a broader category than life), but if we wanted to define it, I think we call life those cognitive systems that a) self-assemble under metabolic constraints (scarcity of time and energy, which demands essential coarse-graining and agential world-models, as well as memory reinterpretation that commits to sense-making over fidelity), b) significantly scales up several nested levels of components’ competencies to larger cognitive light cones in new problem spaces, and c) do all this in a semi-persistent physical body, whose defense over multiple time scales gives rise to differential replication and what we see as evolution. Life is a vulnerable system fundamentally oriented in forward time, and the need to choose the next action to maintain a coherent physical presence (remaining observable to others in physical space). Life forms face existential uncertainty about the meaning of their own memories and the world around them, keeping their parts aligned toward goals and in spaces they cannot perceive. Key aspects of life are steermanship, leadership, uncertainty, creativity, projection into new spaces (searching for new problems, not just solutions), and the "strange loop" (in Hofstadter's sense) of giving its own memories, structure, and actions meanings (being its own observer with a nontrivial inner perspective or point of view). Of course the more conventional definitions (around thermodynamics, evolutionary change, etc.) are also relevant aspects.
Consciousness
[ Note that 99% of our work isn't about consciousness, and all the claims about diverse intelligence have no commitment to any view on consciousness and can be adjudicated in purely 3rd-person objective experimentation ] I think there are 2 key aspects not often mentioned in the field of consciousness studies (although ). One is the experience of being responsible for interpreting your own memories and thus continuously improvising your world-story and self-story. It is what it feels like to be a particular kind of agent, exerting continuous effort of propagating a story to your subsystems (along the thought→thinker spectrum) to keep them aligned and thus keep yourself in physical existence. Another way to think about consciousness is as the view (perspective) of a pattern within the Platonic Space looking outward into the physical world. I think a theory of consciousness should enable us to cross the bridge from "is" to "ought" - it should convert facts about a system's properties to prescriptive ethical claims about how much and what kind of compassion we owe it (and what it owes others).
Terms I’ve introduced or developed with others
New words and specialized uses of familiar terms. The original coining and collaboration credits are retained below.
the subject of engineering (by evolution or by human engineers or by cells or whatever) which is not passive (can only be expected to hold its structural properties) and not even just active matter or computational materials, but has a significant degree of autonomy - an agenda, perhaps homeostatic capacity or higher, which it will execute independently, and which serves as the target of behavior-shaping interventions (not micromanagement) in optimal control (by engineers, but also by evolution, parasites, etc.). As an engineer, the degree to which your material is agential is, practically, the degree to which you need to account for, and can exploit, its autonomy; it’s the degree to which engineering is really reverse-engineering, and the degree to which the result of a collaboration with the material. Living matter is a multi-scale agential material, with learning capacity and goal-seeking competencies at every scale, which has massive implications for evolution, bioengineering, and biomedicine. But even non-living, minimal constructs can exhibit some of this.
Anatomical compiler
a future system representing the long-term endgame of the science of morphogenesis, that reminds us how far away from true understanding we are, and serves as a compass for where we are going. Someday, we will be able to sit in front of this combination of software and robot scientist platform, specify the shape of an animal, plant, organ, or biobot that we want, and it will convert that shape specification into a set of stimuli that will be given to cells to get them to build exactly that shape (no matter how novel - total control over growth and form). Critically, the anatomical compiler is not a 3D printer; instead of micromanaging the structure of the target construct, it exploits the agential medium of cells with competencies. It is in effect a communications device for translating goals between the user's brain and that of the collective intelligence of other cells (transferring our preferred anatomical outcome into navigation policies by which the cellular collective will traverse anatomical space). And, it is not simply a passive interface but is itself an agent exploring the space of possible form and function.
Anthrobot
a living construct that self-assembles from normal (un-edited, genetically wild-type) adult human airway epithelial cells, having a novel form, new behaviors (including healing capability for nearby neural cells), radically altered transcriptome, and other features illustrating the plasticity of morphogenesis and the potential utility of biobots (for example, in-the-body repair without need for immune suppression). More information on Anthrobots can be found here.
Axis of persuadability
a spectrum containing different kinds of systems (from mechanical clocks to humans and beyond) that organizes them with respect to what kind of interventions (rewiring, setpoint editing, training, logical arguments, etc.) are optimal for prediction and control of that system. The question is, what kind of approach is needed to persuade the system to do what you want it to do. It's an engineering take on the question of agency, designed to bring deep philosophical questions into tight contact with experimental science and discovery. See Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and minds.
Bioprompting
the ways in which biological systems signal each other in ways that hack, manipulate, or otherwise exploit the competencies of the receiver to induce complex outcomes with simple signals. For example, the signals that a wasp embryo uses to get plant leaf cells to build a complex gall, which is very different from the native morphology of the leaf, is an example. Some of our bioelectrical perturbations that induce formation of whole organs are also examples of this. I think that just as AI prompt engineers craft inputs to exploit the intelligence of things like large language models, bioengineers will be crafting prompts (not micromanaging the molecular details) to get cells and tissues to reach complex system-level outcomes.
Cognitive Light Cone
the outer boundary, in space and time, of the largest goal a given system can work towards. Not the distance their senses and effectors can reach, but specifically the size (in some problem space) of the goal states they can pursue. This is my attempt to formalize what all agents have in common, no matter their make-up or origin: animals, aliens, AI, swarms, etc. can all be placed on a chart showing the scale of the goals they are capable of pursuing. See The Computational Boundary of a 'Self': Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition.
Ionoceutical
a biomedical intervention that specifically targets the bioelectric interface exposed by cells - ion channels, gap junctions, and similar machinery that control how cells and tissues process information and regulate each others' behavior (individually, and in collectives such as tissues and organs).
Morphoceutical
a biomedical treatment (drug, device, and/or set of stimuli) that exerts its function by re-setting the anatomical setpoint (encoded target morphology) in a patient's tissues and thus leveraging the homeostatic mechanisms which will autonomously implement it (in contrast to current approaches which seek to specifically implement a physiological state directly).
Platonic space
a non-physical latent space of patterns (such as observed in mathematical objects) which, while not determined by evolutionary history or properties of the physical world, affects physical events and in particular is heavily exploited by evolution (i.e., physics is constrained by these patterns, while biology is what we call the study of systems that are enabled by them). "Platonic space" as a term is used not to necessarily hew close to the views of Plato, Pythagoras, and others who thought about this issue, but to make a link to the project of Platonist Mathematicians who see themselves as not creating novel structures but discovering pre-existing patterns in an ordered space. I envision a broader latent space which contains not only the (seemingly) low-agency facts of mathematics but more complex, higher-agency patterns we recognize as behavioral competencies (a.k.a., kinds of minds). The causal influence of mathematical facts on the physical world suggests a kind of interactionism: that the mind:brain relationship is symmetrical to the relationship between mathematical objects and physical ones. I propose a research program using synthetic interfaces to those patterns (including biobots and hybrids that do not have a specific evolutionary history to explain their form and behavior) to work out the structure of the space and the mapping between the causal architecture and other properties of physical embodiments (cells, machines, embryos, etc.) and the patterns that ingress into the physical world through them. As a corollary to this view, we can define mathematics as the branch of cognitive science that studies the behavior of those Platonic space denizens whose behavior can be precisely and sharply defined, while computer science and biology study the behavioral properties of more agential, intelligent patterns as observed through specific kinds of dynamic and living interfaces respectively.
Polycomputing
this idea refers to a view of computation (developed by Josh Bongard and I) as fundamentally observer-driven: what computation a given machine (living or otherwise) is doing depends on which computational lens some particular observer (including the system itself) is using to understand and manipulate a process. Given that, the same physical set of processes might be simultaneously implementing multiple different computations, which has many implications for cognitive science, AI, and evolution. This idea is explored here.
Selflet
a Selflet refers to a thin temporal slice of a cognitive being (in a human, it would be measured in hundreds of milliseconds) across time, as in the "space-time bread loaf" of Special Relativity. It emphasizes that cognitive agents like us are not static, perduring entities but a dynamic pattern that has to re-construct itself and its past from the currently-available memory engrams in its brain, body, and environment. Selflets are snapshots of a mind's "now" moment, and some large number of Selflets integrate into what looks, to observers (and to itself), as an entire agent lasting through time. This model facilitates thinking about memories as messages from your past selves (written stigmergically in the medium of your cells), and actions as constraining and enabling your future selves by deforming the energy landscape of the options available to you in the future (via actions that change environmental features and your own structure and information content). This model thus exploits similarities between lateral interactions between agents and "vertical" interactions between a single agent's past and future selves.
Synthbiosis
this one was invented for me by GPT-4, to capture the idea of a hopeful symbiosis of evolved and engineered material in novel chimeric configurations (e.g., cyborg, hybrot, and other novel composite beings at the level of cells, organisms, and societies). In its own words: "this new word is derived from the Greek word "σύνθεσις" (synthesis), meaning "putting together," and "βίος" (bios), meaning "life." The -sis ending is also used in other terms denoting interaction or association, like "symbiosis." Synthbiosis signifies the flourishing relationship between living and artificial or engineered forms, portraying an image of different entities coming together to create something new and beneficial. This term emphasizes the interdependence and co-prosperity that arises from this unique interconnection, reflecting the concept of "thriving together."
Target Morphology
the anatomical pattern to which a cellular collective will work, and the pattern that, once achieved, causes the proliferation and remodeling to stop. The ability to reach a specific target morphology is the competency of cellular collectives to navigate morphospace, and can (as we have shown) be edited in the absence of any genetic change.
Teleophobia
the unwarranted fear of erring on the side of hypothesizing too much agency in explaining or predicting the behavior of a system, and the usage of conceptual tools and strategies that are best suited for the most mechanical side of the spectrum (coupled with insufficient concern about incorrectly attributing too little). This limiting perspective usually arises from a mistaken belief that agentic properties in a system can be adjudicated linguistically or philosophically, or that agentic properties are still mysterian, and from a failure to embrace the tools that have been available for decades to rigorously study and empirically discover tractable levels of competency in any given system (evolved or engineered).
Xenobot
a self-organizing proto-organism discovered by the team at ICDO which forms when frog embryonic skin cells are liberated from the influence of the other cells and allowed to reboot their multicellularity. Xenobots move around on their own and have a number of other amazing behaviors, serving as a biorobotics platform and helping us to understand the plasticity of life (see full discussion in Biological Robots: Perspectives on an Emerging Interdisciplinary Field). A better definition considers the Xenobot to be not just the proto-organism itself, but the whole multi-scale system comprised of the bots, the AI used in their design, and the human scientists who are using this platform to explore the possibility space of form and function. This expanded view sees Xenobots as a platform with which to understand the latent space of biology.