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Sunday, June 05, 2022

The Gnostic Neuron - A Simple Model of a Complex Brain - Part 1


 Part 1

<Originally posted July 17, 2020>


"If you can't explain it simply, you don't understand it well enough." - Albert Einstein

 

I know why neurons fire, and I understand it well enough to explain in a relatively simple fashion, especially for such a complex topic. I’m serious. Researching the nature of neural connection and the concept of “knowledge” lead me to a startling conclusion based on a single radical, yet simple idea:


Neurons create knowledge.


More specifically, neurons literally create and define knowledge at the instant that they fire. What does this even mean? How can biology create something as abstract as knowledge, let alone define it? Most knowledge is not exactly what you've been led to believe. Knowledge is the ethereal relationship between things. Knowledge is organic proto-information. Information is the refined REpresentation of some knowledge fixed in some medium in the real world. Most knowledge occurs far more often, and with far less quality than generally assumed. The trick is in how we define and think about knowledge. If we relax its definition in a very specific way, some fairly magical things happen in modeling the neuron, the brain, and our world in general. The key is to understand the actual nature of knowledge. And how neurons create it. This assertion begs a detailed clarification, which I’ll provide in due course, but here’s a quick overview: 


It's widely assumed that knowledge and information are the same, or at least very similar things. They are not. Knowledge is pervasive proto-information. Most knowledge generation is inherently biological, and there's far too much of it to even think about most of the time. Information is an abstracted and relatively tiny subset of knowledge managed consciously in a physical form, such as words as sounds or written text. Information can be sent as a signal if both ends agree upon its meaning typically represented by a state in some medium such as this text. This is not true of most knowledge. The knowledge signaled by a neuron is far more dynamic, and far less consistent. Agreement as to its meaning is not required between neurons to make them effective. They only aspire to achieve consistency and agreement.


Information is defined by objective consensus typically represented in some medium outside the skull. These "states" only change for logical reasons. In contrast, neuronal knowledge starts from within and is inherently subjective, analog, and ephemeral. It's also often surprisingly incorrect, and even illogical. Each bit of knowledge is the product of a specific neuron, at a specific moment, and only exists for that moment, useful or not. Knowledge is far more pervasive but far less reliable than information.



Knowledge Cues Scripts 


Most significantly, knowledge is not stored as a fixed “state” in the neuron, or anywhere else in the brain. Instead of storing states, neurons evolve a very specific “sensitivity” to each experience much like an immune cell becomes sensitive to a specific pathogen, except more flexible and adaptable, making it much less "stately" than even an immune response. When a similar circumstance reoccurs, that neuron may fire again in recognition of that specific bit of what is best described as approximate biological knowledge, and then adjusts its sensitivity to be a more effective cue for this particular bit of knowledge at the next opportunity. Again, this is similar to what happens when the body re-encounters a pathogen, just more flexible. An immune response is driven by a type of hyper-specific knowledge used to help keep our bodies alive, healthy, and reproducing. So is knowledge.


A neuron’s knowledge has a utility that is quite different from that of information, but no less significant. As other neurons fire, their specific knowledge joins in a convergent and cascading but sparse map of semiotic simulation that has evolved to create more abstract meaning from any particular experience. Each neuron knows something different, but it only knows that thing for the instant that it fires and then prepares to know that thing even better the next time it occurs. Neurons only fire when they are cued by that thing from reality or imagination, and that thing is best described as ethereal knowledge.


An ionically mediated chemical signal representing this knowledge also diverges out to any other neurons that might find it useful. Ultimately, these somewhat divergent, but mostly convergent and hierarchically organized experience nets both compete and cooperate to form cues that drive scripts of muscle movement known as behavior. Each movement we make is informed by a crescendo of convergent knowledge. How is this knowledge encoded? Mostly, it's not. At least not like information is encoded in a computer. Knowledge is constantly changing, much like the reality we encounter daily in our lives.


In the temporal background, typically out of the critical path, the cortex creates models of the world using a form of this stateless, signal-based simulation expressed as chemical feelings from both sides of the brain. We call these predictions emotions. Through the trick of priming, they increase the probability of physical movement we call behavior as the word e-motion implies. Processing thoughts in our left brain, and envisioning solutions in our right, are both higher-order forms of this emotionally driven effort. Emotions make our imagination "real" so that we'll respond in a way similar to a stimulus from the world, only next time hopefully before it happens, yielding prediction.


Dreams are the off-line version of this type of semiotic stateless simulation, a type of practice run for the next real-world encounter, sorting out what we learned from forming fresh neural connections the day before, all while keeping our muscles carefully inhibited, but the emotions active. Dreams help to hone and firm up this stateless "memory" at night as a follow-up to the real-world sensitivity adjustments that have occurred during the day. This process is known as up and downregulation of neural connection, a form of biological normalization, somewhat similar to what we do with an information database. But also quite different. The result ranges from primal proto-knowledge joining together to drive increasing abstraction all the way up to information, and ultimately something approaching the truth. But by degrees. And over time.


The key to understanding the brain is this fresh perspective that neurons create knowledge, and that most knowledge is created by neurons. It’s only the quality and character of this knowledge that varies, and varies widely. Once we begin to focus on what each neuron knows, and how knowledge dynamically changes, can we begin to build a simple model of a complex brain.



The Neurophilosophy of Language


If you’ve spent any time studying neuroscience or human behavior, this idea of neurons creating and defining knowledge may at first seem comical, radical, bizarre, or worse - meaningless. My first reaction was to laugh out loud. My second was, could it be this simple? I couldn’t look away. 

As I worked with the details of neuronal communication I soon discovered that the macro consequences of this gnostic model were so dramatic and answered so many questions about human behavior that my macro experience began to eclipse the work I was doing in the nano context with the synapses. This neo-gnostic model of neurons ultimately changed how I understand the world and even philosophy itself, which is of course the appreciation of such knowledge.


It's now hard to see neurons as anything other than creators of knowledge. And that’s just the beginning. The concept changes not just how I see neurons and the brain, but also how I understand human behavior. I now see adaptive knowledge behind the actions of everyone I meet. This model is dramatically shifting my perspective of everything. Like green letters dropping down the screens from the movie, “The Matrix”, I see bits of primal knowledge coming together in life to form effective behavior and ultimately emergent insight about everything I experience. This transformation is what I wish to share, but I'm torn between continuing to explore this model and describing its nature in this blog post. I'll try to do both in hopes that each will inform the other.


Am I delusional? Perhaps. But with a clear understanding of this first principle

of the neuron, the brain begins to make a lot more sense. The trick is to

generalize and broaden the concept of knowledge while recognizing its

genesis. Once I understood that neurons literally created and defined knowledge,

figuring out how this happened became a lot easier and revealed the brain’s

multifaceted architecture, yielding a map of astounding complexity largely based

on this one simple principle.


Even more surprisingly, the concept illuminates language as a Rosetta Stone

of brain architecture hiding in plain sight. The connectome of the brain is

ultimately reflected in our language and culture, but by degrees. This

evolutionary trick has evolved to yield knowledge, information, and ultimately,

wisdom.


Words are literally the expression of this knowledge in the process of becoming information. When pre-motor neurons fire, they cue a script of choreographed muscle movements in the diaphragm, throat, tongue, and lips to create sounds. Or in the fingers to produce writing. Words are the result of an expression of knowledge. So is virtually every other form of expression from dance to mathematics.


What I’m about to present is not merely the redefinition of the "word" knowledge. It’s a radically different understanding of what it means to define all words which are only a very modest subset of all knowledge. Knowledge is also likely the basis for all thought and imagination. From this perspective, etymology sheds light on the hard problem of the brain. But it will be easier to address the simple version first. Later we can speculate about consciousness.



Scripts Both Compete and Cooperate to Yield a Multifaceted Brain 


In due course, I’ll describe a collection of tricks that evolution has used to evolve a new way to evolve. (Well, knowledge is only about a billion years old, so fairly new.) It yields a very different, yet powerful way of thinking about the brain. And reality. No, I don’t understand all the tricks of the brain, only a relative few. But these tricks are applied disproportionately yielding a shadow of an overview that has for me become a simple model of the brain. Needless to say, understanding the nature of this biology-based knowledge has extraordinary application in our everyday interactions with the world, from science to art, and especially, philosophy. It informs everything you can imagine. And many things you can't.


Yes, I realize how audacious this claim is, probably better than most. I’ve been casually working on this problem for decades, but more intensely over the last few years. I’ve collected well over a thousand pages of technical descriptions, alternate versions, notes, and references, but all of that detail would only distract us at this point.


A comprehensive model of anything needs to account for all known observations. This of course is currently impractical in the case of the brain. There’s simply too much data to even review, let alone validate (at least by any one person). We need a simple model of the brain first. That starts with a framework, or better yet, an overview of a model. We can fill in the details as our understanding evolves.


Whether we realize it or not, we each manage a default model of the brain along with a model for human behavior. We use it daily in various ways. It's just how the mind works. Being part of nature itself, the brain too abhors a vacuum. If your exposure to our technical media about the brain is typical, your personal brain model likely involves electrical metaphors, computers, and processing your thoughts in a sequential fashion. After that, the details are likely lost in shadow, because most of that model is simply wrong. But not completely.


Many think of neurons as logic devices or memory elements (which can be derived from electronic logic). For decades, so did I. But neurons have far more in contrast than in common with such metaphors. If you're like me, you may have a feeling that there's just something about this tech approach that doesn't seem quite right.


We each know different things about the brain depending upon our own individual research and experience. Striving for a fresh approach, here's how I manage my model of the brain - start from the most general and work in new detail as I validate each observation. But it helps greatly to have that first principle understood - that neurons create knowledge.


Here's a fun game: each time you use the word "know" or "knowledge", look outward into the world and think about how you came to know this thing and what your level of conviction is. Question everything. So, what do you know?


After that, the challenge is to generalize in a way that incorporates what we know, yet keep those generalizations broad enough to account for all the detail we’ve yet to discover. A fool’s errand? Perhaps, but here's the hyper-simplified model of the brain I now use to understand this challenging mystery.


A Simple Model of a Complex Brain


The body delivers millions of neural signals to the brain, each of which represents a bit of knowledge about the world in chemical form. These signals are best understood as theatrical cues which both compete and cooperate in a converging and increasingly abstract fashion to drive scripts of muscle movement known as behavior, which in turn sometimes affects the world, which can once again be sensed. This process happens in a continuous loop with that world. Or these cues may signal glands to release internal chemistry which interacts with these chemical signals in a similar fashion also forming a dynamic loop within the body and especially the brain. In both cases, these two macro loops help to refine and normalize interactions with each real-world encounter. Or internal emotional feelings. In the process, both ionic signals and their chemistry refine and validate the accumulated knowledge of that experience in the form of adjusted sensitivity. Or not. The exceptions can be critical.


In their competition and cooperation, these cues and scripts of neural connection have formed in layers within each side of a single verticle division, left and right, providing for necessary isolation to create the multifaceted nature of the brain. These sides and layers are best imagined as creatures from our evolutionary past. Each of these critters has many different ways of dealing with the world. As you come to know how each creature net is cued, you’ll begin to better understand your own behavior. A thousand critters each apply one of their thousand tricks to yield a million survival solutions. There are obviously too many to keep in mind. Fortunately, their application is disproportionate, even extremely disproportionate. But understanding even a few of these tricks can be quite useful in understanding the brain, and ourselves.

 

For instance, think of the cues that drive human competition, consumption, and reproduction. There are many, but only a relative few dominate most of the results in a form best described as sparse signaling creating a map of your body and the world in general. If the “executive” in your mind can intercept and redirect even a few of these more common cues, it can change your life dramatically. There are many self-help books that apply these techniques without ever understanding the neural details of how they work. OK, the above may be a bit too complex for now. Ignore these last three paragraphs. If you can.



An even simpler summary of a simple model of the brain:

- Neurons sense the world to biologically create primal knowledge.


- Chemical signals converge to create even more abstract knowledge.


- This knowledge cues scrips of muscle movement known as behavior.


- Behavior affects the world and body, and in turn is affected by the world and body, forming dynamic loops with reality, normalizing, refining, and validating neuronal knowledge with each repetition.


- This knowledge is published widely by the neuron's axon delivering chemical signals to form a type of stateless, semiotic simulation using sparsely coded maps of reality that help increase the probability of survival and reproduction.


- Our conscious worldmapculus is one such map becoming both the source and the object of this ultimate expression, in a Zen fashion. It's a simulation using dynamically looped signals to create an ethereal representation of reality in our skull, paradoxically.


Still too complex?


How about this:


Neurons create knowledge which is used to cue scripts of muscle movement we describe as behavior. These cues and scripts of neural nets both compete and cooperate to yield a multifaceted brain needed to survive in a complex world.


We are each a thousand creatures that have evolved a million tricks over a billion years.


or even more simply:


Neurons create knowledge yielding a skull full of cues and scripts that help us survive and replicate.


That's about as simple as I can manage for now. Just think of your brain as a collection of competing and cooperating theatrical cues and scripts. Explore the interactions of these cues and scripts introspectively. It may provide a better understanding of how you deal with the world. Like mindfulness (closely related to knowing), this neo-gnostic approach will begin to make more sense and yield more useful results.


If you’ve read this post more than once, it may seem to have changed. That’s because it probably did. I used to have a section here about assertion salad which I broke out as a separate post I now use as a summary. What’s useful today may not be useful tomorrow, or worse, may even become distracting. If I'm correct about this prime assertion, the consequences are as cosmic as the brain itself. It informs all of human knowledge, science, philosophy, and art. I want to keep my thinking flexible and plan to treat this content as a dynamic document much like a gated Wiki which will evolve as I get useful feedback. Initially, it will be progressively published here as a series of dynamic blog posts. Feel free to follow or link, and share as you will. Check back later for new versions.


If the above summary about the brain speaks to you in any way, you’ve likely spent a great deal of time thinking about philosophy, the brain, and/or human behavior. I hope I can help you along your path, and you, along mine. If you’re purely a spectator, that’s fine for now. But I hope you’ll get involved in this effort to understand the brain. Perhaps I should clarify who I am, and who you are as my audience. My work history is steeped in computers, business, and technology, not biology. To a significant degree, I'm writing these blog posts for myself, and to myself. I read them often. But I also need to include you as a critical element in this exercise.


You are likely very interested in the topic or you wouldn't have read this far. I'm sure most will bail within the first few paragraphs. But those who truly understand the nature of this challenge will likely entertain even crazy ideas if it helps them in any way to understand the brain. That makes you more like me in your imagination and conviction regarding this quest. To be candid, I’m making much of this up as I go along, so I need your feedback. Here’s how I hope to inspire it:


I’ll start with an important question to help frame the problem which has been informed by this neo-gnostic model. The exploration of this question will be followed by some unlearning critical to finding a fresh start. Then I'll describe why and how neurons create knowledge and actually define it. Next, we'll take a trip starting with the first animal and then forward through history to imagine how evolution might have created this amazing result. I’ll present an evolving description of the brain starting with a single neuron and ending with a simple model of the human brain. If using this simple model itself to inform a fresh thesis seems like circular reasoning, it’s not. It’s merely a circular presentation. Modeling the brain ultimately starts with the neuron. So will I.


I’ll describe the ideas that informed this model in the way I came to know them over my lifetime of subjective experience, especially the parts I had to unlearn. That’s the reason some of this presentation will be a memoir. Here’s a sample:







Flying



My very first memory was from when I was about three years old and sitting on a rock wall in front of my grandmother’s house where I lived. Above is a current photo. This wall was already falling down 67 years ago. Most of the rocks have now been used for other projects, but at the time I was straddling not only the wall but also that remaining concrete post that originally held a gate. At the time of this memory, all that was left of this gate was a single board of the frame held by one bolt at its center. Now only the bolt-hole remains. I don’t know what happened to the gate or the other bolts, but the remaining one allowed this board to rotate about the face of the post to a horizontal position. As a typical three-year-old fascinated by airplanes, I’d put my feet on this board which became a wing. I could bank left or right. This seat, post, and board became my airplane, not unlike Snoopy’s doghouse which I discovered years later. I recall flying my "airplane" and going to many places in my mind. I remember it well. Or do I?


A couple of years later my father took me on a real airplane ride with a friend of his. As a five-year-old, I had to sit in my dad's lap, but I got to fly a real airplane for a few minutes. Thirteen years later I had my pilot’s license, followed later by an instrument rating. Flying for me has always been a joy, inspiring an immense sense of freedom.


I’ve since wondered many times about this first “memory” of "flying" and how it was stored in my brain. Did my later aviation ambitions affect the content or recollection? Decades later my grandmother told me I’d spent hours on that rock wall as a child. Did my memory simply come from hers? Or did I modify the genesis of my own memory? Are memories real? Or ethe-real?


It's unlikely she would have known about the dynamics of that board, nor did she mention it at the time, yet that aspect remains vivid, leading me to think the memory was mine. Or was this memory created anew at the moment before I typed this sentence into this blog post? A bit of both I suspect.


As we proceed, I will mostly ignore genetics, imaging, brain waves, and the rest of the more recent technical fields, especially anything having to do with the electron (once I carefully dismiss it). What’s left? Chemistry, connection, and the concept of knowledge. Oh, and a bit of theory about evolution informed by the practices of Tao and Zen. But first I need to challenge some common assumptions with a very important question, then plant a seed of doubt about the limits of information theory, and even science itself.


One last thing before you proceed. I may be wrong about neurons creating knowledge as a first principle, but if I AM wrong, what IS the first principle of the neuron? What exactly does its signal mean? And how can we build a model of the brain if we don't clearly understand this first principle? Finally, if not neurons, from where does knowledge spring? Whatever your perspective and convictions about the brain, these questions need to be asked. And answered. While you consider them, here's that first important question to be addressed in the next post:


How can the most profound and studied object in the world be so poorly understood?



<continued in the blog post below>


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