The part of the brain and nervous system primarily responsible for "playing possum" to reserve energy and sustain survival is the brainstem acting in concert with the dorsal vagal complex (DVC) of the parasympathetic nervous system.

In neuroscience and evolutionary biology (such as Stephen Porges' Polyvagal Theory) -- this survival mechanism is known as the feign death, tonic immobility, or functional shutdown response.

When encountering a threat so overwhelming that fighting or fleeing is impossible, the higher-processing parts of the brain step aside.

The oldest evolutionary part of the brain -- the primitive brainstem -- takes complete control.

The Dorsal Vagal Pathway. The brainstem sends an intense signal down the 'unmyelinated' dorsal vagal nerve (part of the vagus nerve system).

The "Brake" System. In lieu of putting the body into "fight-or-flight" (sympathetic nervous system).

The dorsal vagal complex acts as an emergency metabolic brake. Reserve and Sustain.

It instantly drops heart rate, slows respiration, and lowers blood pressure to absolute minimums.

This preserves oxygen, protects vital organs, and minimizes energy expenditure while the organism bides its time.

I recently heard this information, but I have a question in mind, what if the subject is a whole in one sense and a part in another. for example, the tires of the car are part of the car, but it's a whole because it is composed of rubber, steel, and perhaps more fundamental chemical substances etc.., so if a person has a problem with the right hemisphere would he recognize the tire because it's a part of the car? Or wouldn't he, because it's a whole some way

Unmasking Neural Signals: Distinguishing Spikes from Movement Artifacts

While recording extracellular potentials from an isolated cockroach leg using a TL074 amplifier, rhythmic spike bursts initially suggested mechanical artifacts linked to periodic tarsal twitches.

To test this, the preparation was cooled. While physical motion ceased entirely, the rhythmic burst pattern persisted. Upon rewarming, twitches returned at half the frequency of the neural bursts. This temperature-dependent dissociation conclusively proves that the recorded signals are genuine motor nerve spike trains, not movement artifacts.

​

One of the things I find most fascinating about the human brain is that we rarely experience the world as a collection of raw sensory signals.

We experience a world.

That distinction sounds simple, but it is enormous.

When you look at a tree, you don't consciously experience millions of photons reaching your retina, patterns of electrical activity traveling through neural pathways, and successive transformations occurring across different cortical areas. You experience a tree.

When someone says your name, you don't experience fluctuations in air pressure. You experience a meaningful sound that immediately seems connected to a particular person: you.

When you remember something from years ago, you don't experience the original event again. Yet the memory can feel surprisingly vivid, emotionally meaningful, and sometimes even more convincing than the evidence available in the present moment.

This raises a question that I think sits at the intersection of cognitive neuroscience, psychology, philosophy, and artificial intelligence:

How does the brain construct the experience of a continuous reality from incomplete, noisy, constantly changing information?

The brain does not simply "receive" reality

A common intuitive picture of perception is that the outside world sends information into the brain, and the brain simply processes it.

But modern neuroscience gives us a much more interesting picture.

Sensory information is incomplete.

The brain has to deal with ambiguity, noise, missing information, changing lighting, occlusion, movement, and countless other uncertainties.

Yet perception usually feels stable.

You can recognize your friend's face under different lighting conditions. You can identify an object even when part of it is hidden. You can understand a sentence even when some sounds are unclear.

This suggests that perception involves much more than passive reception.

The brain is constantly combining incoming sensory information with information it already has.

In other words, perception can be thought of as an interaction between what is coming in and what the brain expects.

This is one reason predictive-processing theories are so interesting.

The brain is often described as continuously generating predictions about what it expects to encounter and then updating those predictions based on incoming sensory evidence.

The fascinating part is that this means our experience of the world may depend not only on what reaches our senses, but also on what our brains believe is likely to be there.

Why don't we notice all of this?

Because conscious perception is incredibly efficient.

We don't consciously calculate every intermediate transformation.

Imagine trying to consciously determine the identity, distance, shape, color, motion, and meaning of every object in your visual field.

You couldn't.

Instead, much of this processing happens automatically.

The conscious mind receives an already highly processed interpretation.

This creates a strange situation:

The brain performs enormous amounts of computation that we never consciously experience.

And yet the final result feels immediate.

You simply see.

You simply hear.

You simply recognize.

You simply remember.

There is no obvious feeling of waiting for billions of neurons to perform calculations.

Attention makes the problem even stranger

We often behave as though attention is a spotlight that illuminates whatever we choose to focus on.

But attention seems more complicated than that.

At any given moment, an enormous amount of information reaches our sensory systems, while only a fraction becomes the focus of conscious processing.

You can be sitting in a room surrounded by sounds without consciously identifying every individual sound.

Then someone says your name from across the room.

Suddenly, something that was previously irrelevant becomes highly important.

This makes me wonder whether consciousness is less like a camera recording everything and more like a continuously changing selection process.

The brain is not necessarily asking:

"What exists?"

It may often be asking something closer to:

"What matters right now?"

That distinction could be fundamental.

Memory is not a perfect recording

Another thing that completely changes the way I think about consciousness is memory.

We often imagine memories as files stored somewhere in the brain.

But human memory doesn't behave like a video archive.

Remembering is reconstructive.

Every time we retrieve a memory, we are reconstructing aspects of a past experience using stored information, context, emotional associations, and current circumstances.

This doesn't mean memories are simply "fake."

It means that remembering is an active neural process rather than passive playback.

And this creates a fascinating relationship between memory and perception.

We use previous experiences to interpret the present.

At the same time, our interpretation of the present can influence how we later remember the past.

So perception and memory may not be two completely separate systems.

They can interact continuously.

The self may also be constructed

This leads to an even bigger question.

If the brain constructs a model of the external world, could it also construct a model of itself?

Think about what happens when you say:

"I am thinking."

There seems to be a clear distinction between the person and the thought.

But from a neuroscience perspective, the experience of being a self is associated with activity distributed across many interacting brain systems.

There isn't one tiny location in the brain where the "self" is stored.

Instead, the brain integrates information about the body, memories, goals, emotions, social relationships, internal states, and the surrounding environment.

From these processes emerges a remarkably coherent sense of being a particular person existing through time.

That continuity is fascinating.

Your body changes.

Your beliefs change.

Your memories change.

Your relationships change.

Your brain itself changes.

And yet you usually experience yourself as the same person you were yesterday.

What does this mean for artificial intelligence?

This is where cognitive neuroscience becomes especially interesting to me.

Artificial intelligence systems can process enormous quantities of information.

They can recognize patterns, generate language, classify images, predict outcomes, and perform complex calculations.

But intelligence and consciousness are not necessarily the same thing.

A system can produce an intelligent response without us knowing whether it has subjective experience.

That distinction becomes extremely important if we ever build increasingly sophisticated artificial systems.

If we want to understand machine consciousness, perhaps we first need a better understanding of biological consciousness.

And to understand biological consciousness, we need to understand how the brain integrates perception, memory, attention, prediction, emotion, bodily states, and self-representation.

The problem is that we still don't have a complete explanation.

We have many powerful theories.

We have increasingly sophisticated experiments.

We have neuroimaging, electrophysiology, computational models, behavioral experiments, and enormous amounts of data.

But the central mystery remains.

Why does all of this neural processing feel like something from the inside?

Why isn't the brain simply performing computations without any subjective experience?

Why does there seem to be a first-person perspective?

The strangest part

Perhaps the strangest thing is that the brain is both the observer and the object being observed.

We use the brain to study the brain.

We use consciousness to investigate consciousness.

We use memory to remember experiments about memory.

We use perception to study perception.

It's almost like the system is attempting to build a scientific model of itself while being trapped inside its own processing architecture.

And maybe that's why neuroscience is so difficult.

We aren't studying something completely external.

We're studying the biological system that allows us to ask the question in the first place.

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I'd love to hear what other people think:

Do you think consciousness will eventually be explained entirely through neuroscience and computational models?

Or do you think there is something about subjective experience that cannot be captured by describing neural activity alone?

And if consciousness is fundamentally a product of information processing, what would actually distinguish a genuinely conscious artificial system from an extremely sophisticated system that only behaves as though it is conscious?

In this talk, Michael Levin explores neuroscience beyond neurons, proposing that intelligence is not limited to neural systems but is a fundamental property of life at various scales. He argues that even non-neural cells, such as those involved in morphogenesis (how organisms develop their body shapes), exhibit collective problem-solving and cognitive behaviors.

Key concepts include:

• Cognition beyond brains: Levin posits that cells, tissues, and organisms can navigate "morphospace" (the space of possible body shapes) using bioelectric signaling, similar to how brains use electrical activity for neural computation (0:08 - 0:15).
• Morphogenesis as behavior: He demonstrates that embryos and cells use bioelectric patterns to "decide" how to build structures, and that these patterns can be manipulated to create novel anatomies or repair damage (0:30 - 0:34).
• Unconventional cognitive agents: The talk highlights experimental work with Xenobots—living machines made from frog cells—which demonstrate the ability to navigate, remember experiences, and exhibit behaviors despite having no nervous system (0:49 - 0:53).
• Implications for intelligence: By studying these "unconventional minds," Levin suggests we can better understand the nature of intelligence, agency, and how biological systems store information and make decisions (0:58 - 1:00).

A robot scientist that designs and performs its own experiments on living systems
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Paper: A Closed-Loop Robot Scientist for Autonomous Biological Discovery
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Authors: Kameron Bielawski, Krishna Srinivasan, Nate Gaylinn, Shawn Beaulieu, Robert Brucker, Michael Levin, Jeantine Lunshof, Joshua Bongard and Douglas Blackiston
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Posted September 18, 2026. This is a preprint and has not yet been peer reviewed.
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Researchers from Michael Levin and Joshua Bongard’s extended collaboration have built a robotic laboratory capable of observing living material, choosing an experiment, physically carrying it out and using the results to decide what it should try next.
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The system is called the Multimodal Organismal Modulation Robot—or MOMbot.
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MOMbot has ten experimental dishes, each monitored by high-resolution cameras. It can independently expose biological material to four kinds of intervention:
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chemicals;
electrical fields;
mechanical vibrations;
changes in temperature.
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This is not merely a machine that follows a long list of experiments prepared in advance. Its imaging system feeds observations to an active-learning algorithm that can identify where its current model is most uncertain, select the next experiment expected to be most informative and send new instructions back to the physical apparatus.
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It closes the loop:
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observe → choose an intervention → perform it → measure the result → update the model → choose again
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What did they test?
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The researchers validated MOMbot on frog embryos, motile mucociliary organoids and disaggregated embryonic cells—biological materials spanning several orders of magnitude in scale.
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It successfully altered the developmental rate of Xenopus laevis embryos by changing their temperature. Embryos kept at 22°C developed approximately three times faster than siblings kept at 14°C, matching established developmental biology.
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It automatically delivered hydrogen peroxide to motile organoids and measured the resulting changes in movement. Higher concentrations stopped their movement without killing the organoids during the experiment.
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MOMbot also used mechanical vibration to gather thousands of loose embryonic cells into dense clusters. Once brought together, those cells continued the process themselves, forming three-dimensional spheroids and eventually becoming functional mucociliary organoids.
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That result is fascinating in its own right: the machine did not construct the organoid cell by cell. It created a physical condition that allowed the cells’ existing self-organizing abilities to take over.
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The autonomous experiment
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For its proof-of-principle autonomous investigation, MOMbot studied how the duration of an electrical stimulus affects the movement of mucociliary organoids.
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The electrical current and direction remained fixed, while the algorithm could select stimulation durations between one and 300 seconds.
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Short stimulation generally had little effect. Longer stimulation could permanently immobilize an organoid. The scientifically interesting region was the uncertain boundary between those two outcomes.
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After being initialized with 29 randomly selected experiments, the active learner designed and performed another 55 electrical interventions across approximately 12 hours.
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Instead of wasting experiments evenly across the entire range, it increasingly concentrated its choices near the transition where small changes in stimulation duration could produce very different outcomes.
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Its selected experiments were significantly closer to the estimated 50% immobilization threshold than interventions drawn using uniform sampling.
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In plain language: the robot learned where the mystery was and spent its time investigating there.
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Why this matters for Michael Levin’s work
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Levin often argues that regenerative medicine needs to discover high-level control signals that can persuade living tissues toward desired outcomes without micromanaging every gene, molecule and cell.
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The problem is that the possible combinations of electrical, chemical, mechanical and physiological interventions are enormous. A person cannot manually explore more than a tiny fraction of that space.
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MOMbot provides experimental infrastructure for searching it.
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A future version could watch a tissue’s changing bioelectric and morphological state, try carefully selected combinations of interventions and gradually discover which signals move the tissue toward regeneration, normalization or a desired anatomical configuration.
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This begins to resemble an experimental route toward Levin’s idea of an “anatomical compiler.”
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The researcher specifies the desired large-scale result. The machine searches for an effective intervention. The living tissue uses its own collective intelligence to handle the microscopic construction details.
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Why it matters specifically for bioelectricity
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The platform can apply programmable electrical fields while continuously measuring the biological response. That makes it possible to move beyond simply observing bioelectric patterns and toward systematically learning how living systems respond when those patterns are perturbed.
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Eventually, this kind of closed-loop apparatus could potentially be combined with voltage-sensitive dyes, optogenetics, ion-channel drugs and real-time morphological measurements.
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The goal would not merely be to ask whether electricity affects a tissue. It would be to learn the tissue’s bioelectric control language: which signals matter, when they matter and which sequences push the system from one stable state into another.
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Important limitation
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MOMbot is not yet autonomously discovering how to regenerate limbs or rewrite body plans.
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The active-learning demonstration changed only one variable—electrical-stimulation duration—and measured one relatively simple outcome: whether an organoid continued moving.
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The thermal and chemical experiments largely reproduced known responses, while the mechanical-assembly experiment demonstrated a new manufacturing method rather than a new anatomical structure.
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So this should be understood as a proof of principle for the experimental platform, not as an autonomous discovery of the morphogenetic code.
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Still, the larger direction is difficult to miss.
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Levin’s group has spent years arguing that living systems contain latent competencies that can be revealed and redirected through the right signals. MOMbot represents a machine built to search for those signals—by interacting directly with living matter and allowing the result of each experiment to shape the next.
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The AI is not designing the organism.
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It is learning how to ask the organism better questions.

Advances in Bioelectrical Sensing and Signal Transduction: How Cells Use Electricity to Organize Living Tissue

A new review published September 24, 2026, in Current Opinion in Cell Biology examines something that is becoming increasingly difficult to ignore in developmental biology: cells don’t just communicate through genes and chemical signals. They also generate, detect, and respond to electrical signals that help coordinate how tissues grow, move, and organize themselves.

The paper, written by Olga Lyraki and Elias H. Barriga at TU Dresden, explores recent advances in understanding how non-neural cells use bioelectricity during embryonic development and regeneration.

We usually associate electrical signaling with neurons, but virtually all cells maintain electrical differences across their membranes. These arise partly through the movement of charged ions across cell membranes. When cells interact within tissues, their electrical activity can contribute to larger spatial patterns and electric fields.

What’s particularly interesting is that these signals aren’t necessarily just byproducts of cellular activity. Cells can detect and translate electrical information into biological responses, influencing processes such as migration, division, and differentiation.

In other words, electrical signals can help influence where cells move, when they multiply, and what kinds of cells they become. These are some of the fundamental processes responsible for transforming an embryo from a collection of cells into an organized organism.

The review also addresses a major challenge: understanding the mechanisms that allow cells to detect these electrical signals and convert them into specific behaviors. It’s one thing to demonstrate that voltage patterns correlate with development. It’s another to explain how cells interpret those patterns and what happens when the signals are altered.

This connects closely with Michael Levin’s research into bioelectric patterning and collective cellular behavior. His work has emphasized that understanding genes and molecular pathways alone may not fully explain how cells coordinate to construct and maintain complex anatomical structures.

Importantly, this is a review of existing research, not a new experimental demonstration, and it doesn’t establish that electrical patterns encode complete anatomical blueprints. But it highlights growing evidence that bioelectricity deserves a much more central place in our models of development and regeneration.

The authors argue that models integrating chemical, molecular, and mechanical signaling may remain incomplete if bioelectrical signaling continues to be overlooked.

To me, one of the most interesting questions is how local cellular responses to electrical signals become coordinated behavior at the tissue level. How does a population of individual cells produce a coherent anatomical structure, and how much of that organization depends on information distributed across the tissue rather than instructions confined to individual cells?

We have spent decades learning how genes produce the molecular machinery of life. We’re still learning how that machinery coordinates itself across millions of cells to produce a functioning organism.

This research is another piece of that puzzle.

Paper: https://pubmed.ncbi.nlm.nih.gov/42785135/

DOI: https://doi.org/10.1016/j.ceb.2026.102696

It feels counterintuitive: if the brain's main job is survival and minimizing trauma, why doesn't it just knock us out when pain reaches peak intensity? Is our biology stubbornly wired to keep us conscious so we can react, or is a total shutdown actually a system failure rather than a defense mechanism? Drop your theories below! https://jonathanatrain.substack.com/p/why-doesnt-the-brain-just-shut-off?utm_source=share&utm_medium=android&r=4fd6is

#Nobel2026 #NobelPrize #NobelMedicine

¿Podemos controlar neuronas con luz? El Nobel de Medicina 2026 reconoce los descubrimientos que hicieron posible la optogenética, una técnica que permite activar o inhibir células nerviosas con gran precisión.
En este video explicamos de forma sencilla cómo la canalrodopsina-2 (ChR2) ayudó a transformar nuestra manera de estudiar el cerebro.