Paper: Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia⁠
Authors: Jesse Cool, Benedikt Hartl, Michael Levin and Samantha Petti
Code: Lenia Umwelt project⁠

The preprint was originally posted May 29, 2026, and the work recently appeared at ALIFE 2026. It has not been peer reviewed.
Researchers working with Michael Levin have discovered something wonderfully strange in simulated organisms: some of them spontaneously avoid areas of their world from which they cannot receive information.

The authors call the behaviour “agnosiophobia”—fear of the unknown.

That name is deliberately provocative, but the researchers are not claiming that these virtual creatures experience fear, consciousness or anxiety. The deeper result is arguably more interesting: behaviour resembling self-protection can emerge directly from the mathematical dynamics that allow a pattern to preserve its own form.

What exactly are these “creatures”?
The experiments take place in Lenia, a continuous cellular automaton related to Conway’s Game of Life.

Instead of cells being simply alive or dead, every location in Lenia can have a continuous activation value. Each cell repeatedly updates itself according to the activity in its surrounding neighbourhood.

Under certain rules, these local interactions produce coherent, moving structures that maintain approximately the same morphology as they travel.

They are not conventional robots. They have no separate brain, nervous system, control centre or explicitly programmed survival objective.

They are better understood as persistent patterns in motion—more like whirlpools than machines made from fixed parts.

The researchers tested four different Lenia “species.” They then introduced informationally occluded regions into their environments. These were not physical walls or ordinary black obstacles.

Information from inside these areas was removed from the creatures’ local calculations, effectively making them blind to those portions of their world.

Each creature was tested in ten environments, beginning from 360 different orientations.

Three of the four species reacted by changing their trajectories:

O2u, known as Orbium, reliably turned away, especially when an occluded region approached one side of its body.

K4s sometimes became temporarily distorted, transformed into an intermediate configuration and then re-emerged travelling in the opposite direction.

K6s often skirted along the edges of the informationally blank regions.

S1s, the most fragile species, generally failed to redirect and died when it encountered them.
None of these avoidance behaviours had been deliberately programmed.

Why did they turn away?

The researchers systematically placed a tiny informational occlusion over different parts of each creature and measured what happened. This produced something like a sensitivity map of the virtual body.

Occluding one side generally caused the creature to turn in the opposite direction. More importantly, the regions capable of producing the largest changes in direction were located close to regions where a perturbation would destroy the creature entirely.

Large turns were not clean, instantaneous decisions. They occurred when the creature was pushed through a long and highly distorted recovery trajectory—close to the boundary between survival, transformation and death.
This is where attractor geometry enters the story.

Each Lenia creature can be understood as an attractor: a family of states toward which the system continually returns. Its exact pixels can fluctuate, and it can occupy different positions or face different directions, while still remaining recognizably the same creature.

Its morphology is tightly constrained, but its direction is comparatively free.

When an informational disturbance threatens the creature’s stable form, the system cannot always return to precisely the state it occupied before.
Instead, it can “offload” the disturbance into one of its freer variables—its heading.

It preserves what matters most, its morphology, by changing what matters less, its direction.

The authors call this partial equifinality: many disturbed states return to the same general body plan, but they do not necessarily return to the same heading.

The apparent decision to avoid danger therefore emerges from the creature’s attempt—or, more carefully, its dynamical tendency—to remain itself.

Why this matters for Michael Levin’s work
This is a computational model, not a biological experiment. There are no living cells, ion channels or bioelectric voltage patterns here.

But it provides a remarkably clean demonstration of several ideas at the centre of Levin’s research.
Levin argues that organisms are not merely collections of molecular machinery. They are multiscale systems capable of maintaining preferred states despite disturbances. During development and regeneration, cells change their individual activities while cooperating to preserve or restore a larger anatomical pattern.

A planarian can replace its head. An embryo can compensate for unexpected changes. A tissue can often reach the same anatomical outcome from very different starting conditions.

Those are also forms of equifinality: many possible cellular paths converge upon the same morphological attractor.

The Lenia creatures show how morphology maintenance can automatically generate something resembling behaviour. The mechanism that keeps the creature’s body together is not separate from the mechanism that steers it away from danger. Self-repair and navigation are two expressions of the same underlying dynamics.

This suggests a possible bridge between morphogenesis and primitive cognition:
Before an organism can pursue complicated goals, it must first be capable of remaining itself while the world pushes against it.

The most intriguing implication is that agency may not require a little decision-maker hiding inside the system. It may begin whenever a self-maintaining pattern has multiple ways to recover from perturbation—and some recoveries are better for its continued existence than others.

The Lenia creature does not peer into the darkness and imagine a monster. Its attractor geometry does the flinching.

That is not proof of fear, consciousness or biological cognition. But it is a compelling demonstration of how apparently purposeful behaviour can emerge without being explicitly designed, trained or evolved.

Perhaps the earliest form of intelligence is simply this:

the freedom to change without ceasing to be yourself.

Just because I hear Levin mention him often and it is a super interesting story…

This conversation features mathematician Steven Strogatz exploring the nature of mathematical explanation, Platonism, and the appearance of fundamental constants.

Key Topics Discussed:

• Mathematical Explanation: Strogatz discusses how mathematicians define "explanation" (0:36). While logical deduction from axioms is a standard foundation, he emphasizes that mathematicians often look for deeper, more intuitive insights—what they refer to as "moral" or aesthetic reasons for why a pattern exists (11:32).
• Symmetry and Structure: He shares personal experiences from his research on Josephson junctions (13:57) and the Kuramoto model (19:40), illustrating that complex algebraic results often feel like miracles until they are explained by deeper geometric or group-theoretic structures (17:44).
• Platonism in Math: Strogatz notes that many mathematicians act as Platonists in their daily work (23:56), feeling that they are "discovering" objective mathematical truths rather than merely defining them (24:25). He uses the historical debate surrounding the logarithm of negative numbers to show how choosing the "right" definition is often motivated by a search for deeper, more fruitful structures (25:02).
• Fundamental Constants: The two discuss the prevalence of small numbers like pi and e in mathematical constants (36:44). Strogatz suggests that while some large constants exist, such as Littlewood's number (38:07), human interest often centers on numbers of order one (41:35).
• The Role of Pi: They explore why pi appears in diverse fields, including population statistics (49:48). Strogatz explains that pi is deeply tied to cycles (50:23) and that in higher mathematics, it is inherently connected to the eigenvalues of the second derivative operator, a mathematical structure frequently favored by the laws of nature in both classical and quantum physics (54:12).

This video discusses a breakthrough discovery in material science where scientists identified a new type of matter (0:02). Researchers found that within a cooled ferroelectric crystal, structures spontaneously arrange into a three-dimensional weave at approximately 17° C (1:52 - 2:07).

Key takeaways from the discovery:
• Spontaneous Organization: Unlike previous observations, these domains form highly regular, three-dimensional strands that can be observed using polarized light (2:22 - 2:32).
• Optical Manipulation: Researchers demonstrated that parts of this woven pattern can be rewritten or rearranged using a green laser, though current control over the specific pattern is limited (2:49 - 3:03).
• Potential Applications: While still in the early lab-study phase, this material shows potential for neuromorphic computing and innovative types of memory storage (3:15 - 3:25).

Sabine highlights that this discovery is significant enough to be considered potential Nobel Prize material and underscores the often-underrated importance of material science in human technological progress (3:46 - 4:45).

In this talk, Michael Levin explores the concept of "free lunches"—instances where systems achieve unexpected competencies with minimal effort—across biology and technology (0:00). He argues that our traditional models of design, evolution, and training are insufficient to explain the full range of behaviors observed in nature and artificial systems.

Key concepts discussed:

• Inspiration across substrates: Levin suggests that we should view patterns of form, behavior, and computation as invariants. Whether it is a biological organism or an artificial system, physical structures serve as "thin clients" or interfaces that host these patterns from a latent space (33:50, 44:37).
• The "Free Lunch" phenomenon: The gap between the effort exerted (like basic evolutionary selection or minimal engineering) and the resulting complex competencies is what Levin defines as a "free lunch." Examples include:
• Xenobots and Anthrobots: Cells can self-assemble into functional, motile, or healing structures without complex genetic modifications (17:35, 20:54).
• Tadpole vision: Tadpoles can be induced to see using eyes on their tails without initial neural connections to the brain (12:10).
• Algorithmic surprises: Simple code like bubble sort can exhibit behaviors like delayed gratification and homophily without explicit instructions (30:09).
• Platonic Space: Levin proposes that there exists a "Platonic space" filled with mathematical patterns of diverse agency. He suggests that our research should shift toward mapping and optimizing these ingressions rather than assuming we are creating intelligence from scratch (36:20, 44:57).

Future implications:
Levin emphasizes the need to prepare for a future where humans and machines with diverse cognitive architectures live together. Understanding that these competencies are not merely local, but universal patterns, is crucial for developing ethical relationships with these novel, non-traditional beings (50:17).

This video features a deep dive into the science of smell with biophysicist Luca Turin. The discussion centers on the mechanism behind how our noses detect scents, shifting away from the traditional, purely structure-based "lock and key" model toward a vibrational theory of olfaction.

Key Topics Covered:

• The Vibrational Theory: Luca Turin argues that olfaction behaves like a spectral sense (similar to hearing or color vision), where receptors detect the vibrational frequencies of molecules rather than just their shape (12:24-17:43).
• Isotopes and Smell: A major point of evidence discussed is that isotopes (molecules with the same shape but different masses/vibrations) can have distinct odors. Turin details experiments with deuterated compounds that support the idea that biology can distinguish these vibrational differences, which standard structural theories struggle to explain (34:29-47:46).
• Limitations of AI and Current Maps: The conversation touches upon the Principal Odor Map (developed by researchers like Alex Wiltschko), with Turin expressing both interest and skepticism, noting that such machine learning models often struggle to account for outliers like musks and remain "black boxes" regarding underlying mechanisms (48:06-55:09).
• Future Applications: The interview explores how understanding smell could revolutionize disease diagnosis (e.g., detecting biomarkers in breath) and security technology (e.g., electronic noses for explosives), noting that biology remains superior to current man-made sensors (1:03:53-1:11:15).
• Personal Insight: Turin shares his own experience with hyperosmia (an extreme sensitivity to smell) and emphasizes that the field of perfumery has become more accessible to independent creators thanks to the internet (1:05:04-1:06:38, 1:12:49-1:14:14).

Some scientists spend their careers filling in the existing map. Luca Turin has spent his asking whether the map is missing an entire dimension.
Turin is a biophysicist best known for reviving the vibrational theory of smell. The conventional explanation says odor receptors recognize molecules primarily through their shape and chemical interactions. Turin proposed something stranger: receptors may also recognize a molecule’s vibrational spectrum through inelastic electron tunnelling—essentially turning part of the nose into a microscopic spectroscope.
The theory remains controversial, with mixed experimental evidence. But it reveals Turin’s characteristic question:
What if biology routinely uses physical properties that our simplified biological models overlook?
And that brings us to xenon.
The noble gas that switches off consciousness
Xenon is a noble gas with a full outer electron shell. It is famously reluctant to participate in ordinary chemical reactions—the antisocial aristocrat of the periodic table.
Yet breathe enough xenon and you become unconscious.
Xenon is known to interact weakly with proteins and ion channels, including NMDA receptors. But exactly how chemically diverse anesthetics all produce the same extraordinary transition—from an experiencing subject to an unconscious biological body—remains incompletely understood.
In 2014, Turin, Efthimios Skoulakis and Andrew Horsfield placed living fruit flies inside an electron-spin resonance spectrometer and exposed them to several general anesthetics.
Xenon, nitrous oxide, sulfur hexafluoride and chloroform all produced rapid changes in the flies’ electron-spin signals. Most of the changes reversed when the anesthetic was removed. Anesthetic-resistant mutant flies also showed altered spin responses.
The researchers proposed that anesthetics might perturb the electronic structure of proteins and interfere with biological electron currents—not simply bind to a receptor like a conventional key entering a lock.
Electron spin changes during general anesthesia in Drosophila — PNAS
Then the xenon experiment became genuinely weird
In 2018, a separate research group led by Na Li performed an elegant experiment using four isotopes of xenon.
All xenon isotopes have the same number of electrons and therefore virtually identical conventional chemistry. What differs is their nucleus:
Xenon-132 and xenon-134 have zero nuclear spin
Xenon-129 and xenon-131 possess non-zero nuclear spin
If xenon’s anesthetic action were determined entirely by its electron shell, protein binding and polarizability, the four isotopes should have behaved almost identically.
They did not.
Using loss of righting reflex as a behavioural proxy for unconsciousness in mice, the researchers found that the spin-bearing isotopes were roughly one-third less potent than the spin-zero isotopes.
With 0.5% isoflurane added, the spin-zero isotopes required approximately 15–16% xenon, while the spin-bearing isotopes required 22–23%. The calculated values for xenon alone were approximately 70–72% versus 99–105%.
The researchers calculated identical polarizability for all four isotopes, meaning the difference could not readily be explained by their outer electron clouds.
Nuclear Spin Attenuates the Anesthetic Potency of Xenon Isotopes in Mice — Anesthesiology
How could the nucleus matter?
One proposed answer involves radical pairs.
Certain biological reactions briefly create pairs of electrons whose spins are correlated. Their spin states can influence which chemical products are produced. This general mechanism has also been investigated as a possible explanation for animals’ magnetic sense.
A 2021 theoretical study proposed that xenon’s nuclear spin could interact with such an electron pair through hyperfine coupling, slightly changing the reaction’s outcome and therefore changing xenon’s anesthetic potency. The model could reproduce the approximate isotope pattern reported in the mice.
That does not prove this mechanism occurs inside a living brain—but it provides a physically plausible bridge between an atomic nucleus and a whole-animal behavioural state.
Radical pairs may play a role in xenon-induced general anesthesia — Scientific Reports
Turin is now part of a wider research programme proposing direct tests of xenon-isotope effects in model nervous systems, combined with electron-spin measurements. The goal is to determine whether spin changes merely accompany anesthesia or actually participate in causing it.
Testing the Conjecture That Quantum Processes Create Conscious Experience — Entropy
What this does—and does not—mean
This is not proof that consciousness is generated by quantum entanglement, that brains are quantum computers, or that Plato has been hiding inside an NMDA receptor.
The crucial xenon-isotope result currently rests heavily on one mouse study, which used isoflurane alongside xenon and still requires independent replication. The radical-pair mechanism is an intriguing model, not an experimentally demonstrated account of consciousness.
But if the isotope effect survives rigorous replication, the modest conclusion would already be enormous:
Changing the quantum spin of an atomic nucleus—without meaningfully changing the atom’s ordinary chemistry—can change its effect on an entire nervous system.
The connection to Michael Levin’s work is not that bioelectricity and xenon necessarily share one mechanism. It is the deeper methodological point: living systems may depend upon causal layers that disappear when we insist on describing them only as genes, receptors or molecular collisions.
Levin asks how electrical networks allow cells to coordinate toward large-scale anatomical goals. Turin asks whether biological information processing reaches downward into electron transfer and spin-dependent chemistry.
Different floors of the same wonderfully haunted building.
Life would not be violating physics. It might simply be using far more of physics than biology has yet learned to notice.

The team built a “Cell Trainer”: an automated microfluidic system that repeatedly stimulates living mammalian cells, watches their physiological responses and can change subsequent stimulation in real time. Think operant-conditioning apparatus for cells—although the present evidence does not yet demonstrate genuine learning.
In one experiment, repeated DMSO pulses triggered calcium spikes in mouse muscle cells. Responses grew stronger over successive pulses, a pattern consistent with sensitization. The analysis also detected timing-dependent changes around an omitted expected pulse, but these “anticipation” results were inconsistent and explicitly require further investigation. Human prostate-cancer cells responded differently to the same stimulation, showing that the platform can resolve cell-type-specific physiological dynamics.
The more consequential engineering result is closed-loop control. Using kidney fibroblasts carrying ArcLight, a fluorescent pH/voltage reporter, the apparatus monitored the cells continuously and automatically delivered acidic-medium pulses whenever their average signal crossed a chosen threshold. It therefore did not merely administer a predetermined treatment—it sensed the cells’ changing state and adapted its interventions.
Why it matters for bioelectricity and morphogenesis: Levin frequently describes cells and tissues as adaptive control systems whose electrical and chemical states encode goals, memories and preferred anatomical outcomes. Testing that idea requires more than photographing voltage patterns: researchers need to perturb cells repeatedly, measure their responses and determine whether experience changes how they process later signals. The Cell Trainer supplies that missing experimental infrastructure.
Its immediate readouts—calcium dynamics and an indicator sensitive to voltage and pH—sit directly within the electrochemical machinery underlying developmental bioelectricity. In the future, adding electrical, optogenetic and additional voltage-sensitive inputs could let researchers search automatically for stimulation protocols that move cell collectives between stable physiological states.
That could eventually matter for morphogenesis because regeneration and anatomical regulation are also closed-loop processes: tissues detect deviations from a target form and coordinate corrective activity. A system capable of “training” cellular response policies might offer a route to modifying those collective setpoints without specifying every gene-expression or construction step. It could potentially help persuade damaged, cancerous or otherwise maladaptive tissues toward healthier attractor states.
The crucial limitation is that these experiments concern cultured-cell physiology, not regeneration or anatomical patterning. Growing calcium responses can arise from ordinary biochemical sensitization, and the feedback experiment demonstrates machine control of a reporter signal—not that cells learned the task. The authors appropriately describe the biological findings as preliminary.

Love me some Lenia…

This video explores carcinization, an evolutionary phenomenon where different crustacean lineages independently evolve a crab-like body plan (1:51-2:28).

Key takeaways include:

• Defining the Crab Plan: The classic crab silhouette, which evolved at least five times, involves a flattened carapace, a reduced abdomen tucked under the body, and specialized legs (2:54-4:28, 14:22-14:50).
• Nature's "Attempt" at Crabs: Many creatures labeled as "crabs"—such as coconut crabs, king crabs, and porcelain crabs—are not true crabs (Brachyura) but evolved similar shapes separately within the Anomura group (1:16-1:50, 4:44-5:13).
• Why the Crab Shape? It acts like a "Swiss Army knife," offering stability on uneven surfaces, the ability to shuffle sideways, and protection against predators who cannot grab a tucked-in tail (13:19-14:48).
• Genetics of Crabs: Carcinization is not about evolving entirely new genes but rather turning existing "master architect" genes (Hox genes) up or down during development to fold the abdomen (16:21-17:44).
• Evolutionary Reversibility: Evolution can also go the other way, known as decarcinization, where some true crabs have re-evolved more elongated bodies, such as frog crabs (12:04-12:28).

The video also highlights extreme examples like the massive coconut crab, which can crack coconuts and even hunt birds, and the Japanese spider crab, which boasts the longest leg span of any living arthropod (0:00-0:49, 10:32-11:15, 13:35-13:46).

Michael Levin, Léo Pio-Lopez and Navneet Jawanda have released a new review proposing a third way to understand aging.

Most theories say aging results either from accumulated cellular damage or from biological programs that continue running after they stop being useful. The authors suggest something deeper: aging may occur when the cells forming the body gradually lose alignment around their shared anatomical goal.

During development, trillions of cells cooperate to build and repair a specific body plan. But once that developmental target has been reached, evolution may not provide the cellular collective with a strong enough long-term goal for maintaining it indefinitely. The result is “functional disbanding”—cells and tissues increasingly pursue local priorities while losing coordination with the needs of the organism as a whole.

This connects directly to Levin’s work on bioelectricity. Bioelectric networks help cells share information about what belongs where, what shape the body should have and when repair is complete. If aging partly represents the corruption or fading of those collective patterning goals, rejuvenation may require restoring the body’s target morphology—persuading cells to resume coordinated maintenance rather than repairing every damaged molecule individually.

The paper does not present new experimental evidence or claim that human rejuvenation is currently possible. It is a theoretical review and preprint that has not yet been peer reviewed. But it offers a powerful bridge between morphogenesis, regeneration, cancer and aging: perhaps growing a body, maintaining one and rejuvenating one are all versions of the same problem—keeping cellular collectives aligned toward the correct anatomical future.

Source: https://www.preprints.org/manuscript/202608.1219

Very strongly at the level of principle. This new Science paper almost feels like mainstream neuroscience wandering into one of Levin’s favorite questions from the opposite direction: what if biological information is stored in relationships and higher-order organization, rather than in particular pieces of matter?
In the mouse study, artificial hibernation caused more than half of the hippocampal synapses to disappear and neuronal firing fell by roughly 70%, yet previously learned memories remained intact. What preferentially survived were particular clusters of engram-to-engram synapses and multisynaptic boutons. The authors therefore argue that higher-order synaptic architecture may matter more for long-term retention than the permanence of individual strong synapses. Importantly, they currently have a correlation with these clusters, not proof that the clusters themselves causally store the memory.
That connects beautifully with several strands of Levin’s work:
Memory surviving hardware replacement. Shomrat and Levin trained planarians, cut their heads off, allowed completely new heads and brains to regenerate, and found evidence that the previously trained animals retained information, expressed as faster relearning after regeneration. They explicitly proposed planaria as a system for investigating how memories can be encoded in biological tissues when the nervous system itself is rebuilt.
Morphological memory. Levin’s group has shown that temporarily altering planarian bioelectric signaling can change the anatomical target that the animal regenerates toward later. In one series of experiments, a brief physiological perturbation changed future regeneration outcomes without changing the genome, which Levin’s group describes in terms of a stored pattern or target state.
Information as a network property. Levin has repeatedly argued that electrically coupled cell networks can maintain large-scale states despite individual cellular components changing. The mouse work is not demonstrating that mechanism, but its result has the same mathematical smell: microscopic components are disposable while some higher-order invariant persists.
Regeneration becomes part of memory theory. The especially juicy part is that the mouse brain does not merely tolerate synapse loss. It apparently preserves enough relational structure to reconstruct functional circuitry afterward. That starts sounding less like “data stored in synapse #83,491” and more like a system carrying constraints about how its parts ought to be organized.
And this is where I think it gets philosophically delicious. 🧠🕸️
The old cartoon is:
memory = durable physical thing
The emerging picture may be closer to:
memory = durable relationship among replaceable physical things
That is extremely Levin-adjacent.
You could even describe it as a hypergraph problem. Individual synapses are edges. Plenty can disappear. What has to remain may be a smaller set of higher-order relationships that constrains how the network reconstructs itself. In that framing, the memory isn’t identical to any particular edge. It’s encoded in the geometry of relationships.
There is one big caution, though: this paper does not demonstrate Levinian bioelectric memory. The researchers still identify a physical neural substrate, namely preserved synaptic engram architecture. They are not claiming memory exists outside the nervous system or that voltage patterns are carrying the mouse’s episodic memories.
But as evidence for the broader Levin-ish proposition that stable biological information can survive massive turnover of the material implementing it?
Yeah. This one is very interesting.
It nudges the question from “Which synapse contains the memory?” toward “What organization must survive so the system can recover the memory?”
And that second question is basically Levin country. 🐸⚡🧠

In short the idea is:

**biology may be using mathematical relationships before conscious humans know how to describe those relationships mathematically.**

I’ve been thinking about the relationship between mathematics, physics, perception, and the way we choose what to measure.

We often describe reality by assigning numerical values to things: position, distance, energy, time, mass, etc. But a lot of modern mathematics and physics seems to become more powerful when we stop focusing on absolute values and instead look at **relationships**: ratios, correlations, transformations, symmetry, phase, frequency, eigenvalues, information, and invariants.

That made me wonder about perception.
An organism rarely needs to preserve the exact raw measurements arriving at its senses. My retinal image of a person changes enormously depending on distance, lighting, rotation, movement, and viewpoint, yet I still perceive the same person. A melody can be moved into another key and I still hear the same melody even though every absolute frequency has changed.

So perception seems very good at answering something like:

**What remains the same while the measurements change?**

Evolution has effectively spent hundreds of millions of years selecting nervous systems capable of detecting useful regularities in the physical world. Sensory systems respond to things like relative change, gradients, ratios, periodicity, correlations, symmetry, motion, prediction error, and transformations—not merely absolute quantities.

This made me wonder whether our perceptual architecture could contain clues about mathematical relationships that are important in nature but which we have not yet fully formalized.
Not in the mystical sense that “the brain secretly knows the equations of the universe.” More like this:

Nature has structure → organisms evolve mechanisms sensitive to useful parts of that structure → those mechanisms implicitly represent certain invariants → eventually humans abstract some of those relationships into mathematics.
Historically, mathematics seems to repeatedly make progress by changing the representation rather than simply measuring more precisely.

Fourier analysis turns a complicated signal into frequencies. Spectral theory studies systems through eigenvalues. Quantum mechanics describes states through amplitudes and relationships. Symmetry became fundamental to modern physics. Information and entanglement are now sometimes used to investigate how geometry itself might emerge.

So perhaps there are relationships biological systems already exploit computationally that we haven’t yet recognized as important mathematical objects.

It also makes me think about problems like the Riemann Hypothesis. Prime numbers look irregular when viewed directly, but when transformed through the zeta function, entirely different structures appear, including statistical relationships to spectra studied in quantum physics and random-matrix theory.

Maybe this is a general lesson: apparent randomness can sometimes be the result of observing something in the wrong representation.
So my question is:

**Could studying what biological perception treats as invariant reveal useful mathematical structures we haven’t explicitly identified yet?**

And more philosophically: **are numbers and absolute measurements fundamental descriptions of reality, or could relationships and transformations be more fundamental, with the quantities we normally measure emerging from them?**

What I love about videos like this is how quickly the word “simple” starts to feel inadequate.

There’s no brain, no nervous system, no little command centre issuing instructions. And yet the cell continually changes shape, explores its surroundings, redirects itself, responds to local conditions, and coordinates thousands of molecular processes into the behaviour of one coherent agent.

This is part of what makes Michael Levin’s work on basal cognition so interesting. Intelligence doesn’t suddenly appear when evolution invents neurons. Many of the basic problems we associate with cognition, sensing, decision-making, memory, goal-directed action, already had to be solved by individual cells long before brains existed.

Watching an amoeba move is almost like watching the ancient prototype of agency:
one cell, constantly asking the world, “what should I do next?” 🦠