(First version posted on 01/05/23)
How Neurons Create Knowledge
Some people say, "How can you live without knowing?" I do not know what they mean. I always live without knowing. That is easy. How you get to know is what I want to know. - Richard Feynman - The Meaning of it All (1999)
Once you start thinking about this new definition of knowledge, there are many answers to the above question, but none of them is a requirement to understanding the neuron’s gnostic nature. This may disappoint you but I’m not going to answer the above question directly or in technical detail. I will however describe in broad terms a few ways that neurons might create knowledge as I’ve newly defined it. Who knows, some of them may even be correct. In any case, all of that can be sorted out later using more rigorous science. The important thing at this point is to get a feel for the idea.
Even though the topic is where I’ve spent the bulk of my time both before and after realizing that neurons actually do create knowledge, the ultimate answer would also be both nebulous and incomplete. Though quite technical in an analogical sort of way, because knowledge is the basis for most of evolution’s tricks, the answers would not only fill a book by itself, but perhaps a whole shelf of books, a wing in a library, or all the libraries that have ever been built. And burned. As I’ve defined knowledge, the answer to this question is essentially the whole history of animal evolution and all of its consequences. Since knowledge was the key to animal life evolving and surviving, the how of it is literally half of biology and every discipline that supports it, no matter how remotely. Knowledge is everything that isn’t material, including consciousness, a topic we’ll avoid for the present. Therefore, anything I might present would be by definition, incomplete. I’ll just lump all knowledge into philosophy, and find peace not knowing all possible answers. But we can explore a few if we’re careful not to nail them down too tightly at this early stage of understanding.
Virtually everything I present from this point on will be hypothetical, as in less than a theory. I’m going to at first set the bar for these answers so low that they can’t fail. Later they can be refined or have completely new answers replace them. After all, that’s how science works before it actually becomes science - it’s speculation. For example, any non-random relationship between things in the world and the firing of a neuron could be defined as knowledge. After that, it's just a matter of quality. Non-random is a good standard to start with.
I will try to use my most obvious examples. What I mean by this is, by the time I got around to figuring out how neurons might create knowledge, I’d already roughed out at least a dozen different ways. I just needed this fresh perspective to understand what it really meant to create knowledge. I needed to frame the question in a less rigid fashion - analogically. Once you entertain the idea that neurons do create knowledge, you realize evolution’s already figured out the “how” of knowledge. All we need to do is figure out how evolution solved the problem. And since evolution’s not trying to hide its accomplishments, they are there in plain sight only needing to be characterized in a bit more formal fashion.
By the way, I stole this approach to solving this problem from a few lines of dialog from one of my favorite movies, “Hunt for Red October”. It was from the shower scene aboard the aircraft carrier where the Jack Ryan character is talking to himself trying to solve a riddle. Like his approach, I don’t have to figure out how to create knowledge. Evolution’s already done that. Knowledge exists. I need only understand how evolution solved the problem, a much easier challenge. Once I began to characterize what the nature of knowledge might be, examples started appearing everywhere I looked.
Once you begin to consider the idea that neurons might be firing because they have detected some significant real relationship between things in the world and that the result of that firing is ethereal knowledge, de Carte’s dualism comes immediately to mind, and so many other pieces also fall into place. Spiritualism is ethereal and perhaps our most precious form of knowledge. Which is probably why it finds form in consciousness.
Even if you doubt the nebulous nature of knowledge and that neurons create it, you’ll have to admit that knowledge certainly exists within the human skull, as we can express knowledge using our minds to control our voices to share this knowledge. And unless you assert that this knowledge came from eating an apple or some other more spiritual source, then understanding how evolution performs this amazing trick is almost certainly on the path to understanding what the brain does. How the brain does it is secondary and has multiple solutions, and many subtle sub-versions, some even becoming subversions.
Since the challenge doesn’t have just one answer, I can go after the low-hanging fruit first. More significantly, if you’ve been paying attention to my Zen maneuvers, you will realize by now that any one answer need not preclude another. Or in some cases, it may. But only for a while. Most of these riddles I will leave for the next generation to consider. But don’t worry, I won’t leave you hanging. I’ll at the very least present my simplest solution which may likely be just an approximation. It will still need to be challenged. How can I fail?
Since I no longer have anything to prove, I’m going to present my answers in a very unscientific way - by telling a few stories over and over in different ways with different characters, but with increasing complexity for each example. Each will demonstrate how evolution MIGHT have evolved a new way to evolve - by creating an ethereal thing called knowledge. This may allow you to get a feel for the process and find the conviction that you need to accept that neurons really DO create knowledge, which is my prime objective in any case. The rest is mere detail to be worked out in time by people who do not yet realize the nature of the challenge.
After all, we have to start somewhere, and so far, I haven’t found a better approach.
Venus Flytrap
My first example will also be my first exception to animals being the only source of knowledge. Does a venus flytrap create knowledge? And what’s a plant have to do with neurons? Exactly. I use this example to demonstrate that knowledge is not limited to the animal kingdom, or likely, even Earth.
Is the venus flytrap a parallel evolution of animal life? Almost certainly. Even though it can’t relocate, it’s a plant that moves quickly in order to acquire nutrition and energy, a rare quality for a plant. This exception from the plant kingdom will help demonstrate how life is a reflection of the world that drove its evolution, animal or plant. Exceptions or not. But let’s get back to our fun first example.
If you haven’t played with a venus fly trap you’ve missed out on one of nature’s most interesting “creatures”, and I push the definition of a creature here for a reason. A venus flytrap seems like an animal, at least some of the time, and in multiple ways.
As its name implies, this plant traps flies (and other bugs). It’s not alone. There are a whole collection of plant species that capture bugs to eat and digest, but this one is special. Its trick is to close its jaw-like leaves over its prey so quickly that they don’t have time to fly away. And if you’ve ever tried to catch a fly in your fist, you know how quick a venus flytrap has to be. Quicker than me. Almost certainly quicker than you.
Surprisingly, that’s not this plant’s most interesting quality. Only its most vivid. Its real trick is knowing WHEN to close. And why. If these special leaves sense any movement then were to immediately close (an impressive trick on its own), they would risk wasting all their energy trapping bits of lint and dirt blown in by the wind, not a very appetizing meal.
The venus flytrap needs to know the difference between the living and the non-living if its meal is going to provide the best nutrients. Since life itself is a collection of the stuff life needed to prosper, even this plant’s objective is a bit of knowledge about the advantages of eating a living, versus nonliving meal. That's valuable knowledge.
The venus flytrap uses a clever two-stage sensor to improve the odds. A fly lands on the first sensor, but nothing happens. If the fly moves enough to activate a second sensor AFTER a set length of time, only then does the trap snap shut. This two-stage timing and the distance between sensors are significant. This timing exclusion window is enough to capture the asynchronous behavior of insect movement and the sensor spacing almost certainly reflects the distance between a typical fly’s legs in some way. I learned about the first motion sensor as a child, but the second only a few years ago.
Though the test is crude, much of the time the flytrap gets it right. Or at least often enough to yield a net gain of energy in calories. This temporal timing window can be thought of as creating a crucial bit of knowledge about the difference between the living and not-living, and is so important it remains differentiated and lateralized in human brains, so has obviously evolved at least twice. Discriminating for life is so important that the flytrap’s speedy trick won’t work without it. At least not very well. This asynchronous bit of knowledge matters. That is why. Now for the nature of this knowledge.
The most obvious differentiator for animal life is movement, and this plant has evolved to detect it. You can think of it this way - insect movement is part of the relationship between the fly and the plant. It’s a very important bit of knowledge as far as the plant is concerned. Not only is this movement critical to creating this knowledge, movement, in general, is critical to creating much of primal knowledge as I’ll shortly present.
The Venus Fly Trap solves the problem using a time delay of at least 20 seconds, and it likely uses some form of ionic signaling to achieve this result. The first movement primes for the second, and could even be described as a type of “memory”. I’ll leave the details to a botanist, but not only does this plant know a bug is present, it knows this by sensing that bug’s presence over a time span to trigger the capture. Such temporal knowledge could be described as a detector of asynchronicity - two things happening at two separate points in time - a very highly evolved plant indeed!
In “Insectivorous Plants” Darwin noted that, “When the leaves are irritated, the current is disturbed in the same manner as takes place during the contraction of the muscle of an animal.”
Darwin is to be excused for describing this plant in electrical terms, after all, he didn’t yet know the difference between electronics and ionics. Since we’re avoiding the details of triggering for now, we can leave these investigations for others, but in summary, I’d like to suggest another very important question:
Does a venus flytrap actually “know” something in the way you understand knowledge? I’m sure there are many times when a flytrap closes on a second twig blown by the wind within the appropriate timing window. That’s a fail. The venus flytrap’s discrimination about life is not perfect. Neither is knowledge. But the flytrap’s temporal test works often enough to survive and even prosper. The venus flytrap certainly seems to know when to have lunch. This life-detecting knowledge defined by both movement and delayed time is quite impressive for a plant that doesn’t even have a neuron.
As I conclude each of these examples, I’ll try to remember to also document why and when neurons fire. In the case of this plant, it doesn’t bother with a signal like neurons do. It goes straight to movement at the right moment. The why is nourishment, and the when is to detect life, an indicator of the quality of the food at hand.
By the way, whatever trick the venus flytrap uses to acquire this knowledge is not in our skull as this “creature” is not on our evolutionary proto-path. But this exception nicely demonstrates that knowledge (and even temporal “memory”) is not the sole province of the animal kingdom, or likely even Earth.
How Venus flytraps store short-term ‘memories’ of prey
Action potentials induce biomagnetic fields in carnivorous Venus flytrap plants
This Is a Lie
The title of this section demonstrates how easy it is to create a paradox using words. If “this is a lie” is true, it makes “this” the truth, which makes it a lie, which must be the truth. But that’s not the worst of it. For my next example, I’m going to tell you a lie, but not completely. A complete lie would have nearly the same utility as telling the truth. Paradox aside, there’s a whole subfield of philosophy that ironically deals with the quality of truth. Truth by degrees is itself an invalidation. I’ve also presented its lack of utility in defining knowledge. So the lie I’m about to tell might even be true.
This approach to veracity is actually an idea I stole from Bizarro Superman. It goes to the core of Bizarro World philosophy. Bizarro Superman would typically say the opposite of the truth, and even present the opposite of logic. But not always. And not exactly. Therein lies the challenge - knowing which parts are true and which are false, and to what degree. It’s how we sort knowledge into two sets of mostly true and maybe false. Set theory needs fuzzy edges. Since I’ve framed this next example as the truth, I may as well make it a whopper.
When I was a kid I asked a lot of questions, and each answer would beg another question. This is a common behavior from curious children of a certain age. I was no different. I did this at home. I did this at school. I did this at church. Most adults would simply ignore me after a while. My grandmother was pretty good at this game. She had lots of answers, but not all of them. When I reached her limit, she would simply leave me with a quixotic smile, which I remember well. This smile meant, “I don’t know, but perhaps you can find out.” In this way, instead of leaving me frustrated, she would encourage me to seek the answer somewhere else, and so send me off on my quest.
In the Baptist Sunday school which I attended, they also tried to answer my questions. It got to the point that the pastor of the church asked my grandmother not to bring me back to Sunday School. I was being too disruptive, which leads us to the topic of God. Don’t worry. This is not the beginning of a sermon. Far from it. Indeed, it’s almost the opposite. I neither believe nor deny anyone else’s idea of God, but I did notice that when I asked a question that no one had a good answer for, the topic of God would ultimately come up. It got to the point that I began to equate the word God with, “I don’t really know”. God is a pretty good stand-in for the “true” part of knowledge, and its opposite.
The Territorial Imperative of Early Life
A billion years ago survival was more challenging than it is today. Darwinian evolution ruled the planet. Billions of plant cells had to die for each new mutation to replicate and prosper. Improving life was all about dying. The only life that existed on Earth at the time was very small plant cells floating in water. But animals were about to literally and physically emerge using two tricks that likely co-evolved during the same event. These two were sense and movement. Along with these new tricks came the need to know WHEN to move, not unlike the Venus Flytrap above. It’s certainly possible that sense and movement evolved separately but less likely because a third non-material aspect required the interaction of both - the creation of knowledge.
About the time I first started programming microcomputers, I came across a computer game called “Life”. This was not the old board game of the same name, it was something entirely different. This “Life” was based on some very simple rules documented in Martin Gardner’s column from Scientific American in 1970. I didn’t get a chance to read the column until years later, but the game presented in computer code was fascinating.
This “Life” is a zero-player game - very Zen. You’d set up initial conditions graphically in a program then press “start” to see what happened. Requiring only very modest computer resources, this game was a natural for the limited memory and displays of the earliest versions of microcomputers. Such vivid "graphical" applications were rare at the time even if these "graphics" were just ASCII characters. Experimenting with “Life”, and the “gliders” were a lot of fun, but what mostly caught my attention were the visual graphical patterns that caused a “lifeform” to prosper and stabilize - the trick was having not too many neighbors, and not too few. There was a Goldilocks zone.
Explore the game if you haven’t. For me, it was one possible model for a primal plant, or perhaps even, animal life. It inspired me to explore how plants were different from animals and why - movement was the key! Modern (and perhaps ancient) amoebas seem to live on this emergent boundary between plants and animals. How did plant cells come to move at all, and how could they best apply these new and amazing evolutionary tricks? I now realize that signaling ethereal knowledge is the key. And that knowledge has profound implications as I’m about to present. The title of this section which I took from a book I read back in the 1960s is a decursive hint.
Hemo
Before we proceed, I want to take you back to one of my 7th-grade experiences:
When I first saw this film as a child, it set off a whole series of possibilities in my mind. Note how the brain is presented as a telephone system. This was high-tech at the time. Computers did not yet dominate our cultural modeling of the brain circa 1959. Watching it decades later, I see the decursive nature of multi-celled specialization and chemical signaling in its various forms - neurons included. In spite of its now-dated content, some of which are in doubt, the movie has many useful concepts. I won’t do a detailed review but it’s worth watching, if nothing else to understand how I came to understand the challenge of evolution at the time I was about to begin learning about electronics, logic, and later, computers.
Staying with my theme, this film honors the difference between science and art which may have inspired its production. I was also fascinated by the idea that blood was similar to ancient seawater as it might have existed about a billion years ago. Decades later I became especially interested in the ratio of potassium to sodium in modern seawater, and even more significantly, contrasting those very ratios in biology between what’s inside the cells, and what’s outside.
I won’t dive deep into chemistry here but a quick review of certain aspects will be useful at this point. Various salts in dry form are made up of two different atoms in a stable crystal configuration, but when you put them in water something very interesting happens - they dissolve in solution. The most common example is table salt. When sodium chloride is dropped into water it will dissociate into separate sodium and chloride ions, each with opposite ionic charges. The same thing happens with potassium chloride. All of these ions float around freely in seawater most of the time.
What’s really interesting is that there are about three potassium ions for every sodium ion withIN most plant and animal cells, but that ratio is reversed and varies by almost an order of magnitude in the saline fluid outside of the cell. There are 27 sodium ions for every potassium in typical seawater, perhaps less in ancient seawater. You might have noticed how they tried to explain this difference in the above movie but mostly failed.
Still, the difference in these ratios led me to explore the topic once I got into college chemistry. It’s where my first doubts about the electrical and logical nature of neurons began. The closer I looked, the more I realized that internally, neurons are ionic, not electronic. And outside of the neuron, they were more chemical in nature than anything having to do with electrical or even ionic charge. This now sets the stage for the next example.
Hillock, the Proto-protozoan 1.0
My second example will be the very first animal, a proto-protozoan, perhaps born of a proto-amoeba. The meaning of the words amoeba and protozoa have been so abused by reclassification as to be almost meaningless, but not quite. All protozoa are animals. Some protozoa are one-celled, others, multi-celled. They range from simple to complex in structure and function. In a similar respect, amoebas cover an informal classification including fungi, algae, and of course, protozoa. Our very first animal is of the very simplest type, a single-celled proto-amoeba, proto-protozoan, proto-neuron - but with no dendrites and no axon. We’ll call him Hillock to note his most significant structure and function.
This first hypothetical beast (and his immediate progeny) are so simple that they only have some standard replication and energy conversion stuff from the plant world. Hillock does not yet sense anything. It does not yet move on its own. It knows nothing, and never has. But that’s about to change.
Like some modern amoebas, Hillock has a leaky membrane. Also like some modern amoebas, this one has become somewhat structurally differentiated in the strength and nature of this membrane - asymmetrically. On the left side of the cell body (to have a reference) is an area of the membrane that is more likely to capture potassium ions (along with food) when encountered externally. This spot will ultimately evolve into a dendrite. On the right side is its hillock. In the future, it will protrude to become a proto-axon, again much later, so not today. For now both sides present with relative weakness and strength in the membrane surface as to changes in osmotic stability, asymmetrically left and right.
This particular Hillock has evolved a very interesting characteristic. Its proto-structure acts like a Zener diode from the tech world. Using the migration of potassium ions instead of electrons, Hillock allows a free flow inward but stops the exit of potassium ions until an inflection point is reached. For the techs among you, think in terms of the easy flow of potassium only in one direction (inward) until a certain threshold of ionic potential is reached at which point, the migration dramatically reverses direction, resetting the internal ionic balance to a more stable configuration. This hasn’t happened yet in all of evolutionary history, but it’s about to. The point is, the membrane at this hillock is about to do something similar with ionic charge that a Zener diode does with an electronic charge. This dynamic membrane has long let potassium ions enter this proto-amoeba but has so far never reached this inflection point to let the potassium ions all go at once, or at least a lot of them, but it hasn’t happened quite yet. Patience.
I’m now going to describe this event in the simplest terms I can. Science will ultimately need to characterize this structure in detailed biology but for now, I will take some liberty. As mentioned, the ratio of sodium to potassium in seawater is 27 to 1. You are 27 times more likely to encounter a sodium ion in seawater than you are to encounter a single potassium ion, yet plants and animals have roughly three times more potassium ions within their cell membranes than sodium ions. This means that there’s a difference in concentration of 81 times between the outside and the inside of any living cell as to the ratio of these two different types of ions. Since this ratio roughly holds for both plants and animals, detecting potassium ions can be thought of as a chemical indicator of life, or at least it’s 81 times more likely to be the case, which is a far more effective test for life than the Venus Flytrap’s motion detector, and so the knowledge it yields more valuable, by degrees.
If all this biology and ionics is becoming a blur, that’s fine. I’m just being technical in a very fluffy way for those who have never played with the details. What I’m describing is almost certainly wrong in many possible respects, except for the final result. It’s quite likely this final result happened in some fashion if not the one I’m actually describing. The details are not as important as the outcome, which is actually quite profound. I include these details to stir the imagination. Challenging them may help some to understand the dynamics of the first creation of knowledge by this proto-amoeba. Hillock is about to cross that line from being a plant to become an animal.
The Nature of Boundaries
It’s a billion years ago, plus or minus a couple hundred million years. Now imagine a totally barren desert landscape next to a large body of water. By totally barren I mean exactly that - just rock and sand above the water - nothing else. Plants won’t find their way onto land for several hundred million years. Imagine no visible life at all in this desert - no lichen on the rocks, no algae slime in the water. If you were searching for life you’d need a microscope and some luck. Life at this point was so rare as to not produce any obvious visual indication even when you looked closely. All you could see would be sand and water, and only feel the air if the wind was blowing.
At a place in the shallows was a point where the sea, sky, and land came together to form a spatial triple boundary. There was the surface between the water and the air, another surface between the air and sand, and of course a third between sand and water. These surfaces meet at a dynamic and ever-changing line we call a shore. This sand, water, and air could also be described as volumes. Even surfaces have area, but shores form a dynamic line where all three meet. I’ll here suggest that most of the really fun stuff in nature happens at boundaries, from the nano context to the macro, and even mega. Keep an eye out for them and think about their nature. As we progress, the knowledge you might imagine emerging from these boundaries will be useful in this exercise.
The water in our story might well have been an ocean filling what is now called North America’s Great Basin, at a place not far from present-day Reno, Nevada. I’ve hiked the mountains and valleys that make up this ground for literally thousands of miles. I’ve come to know the dynamic sky that covers it, and have even enjoyed the remaining bodies of water making up these boundaries as described.
This hypothetical event happened long before the Sierra Nevada mountains as we currently know them even existed, but dry land matters little to this second story, and the sky, not at all. The sand at least provided a shore and shelter from the waves and small currents for the creature I’m about to describe. Land and air will matter much more for other creatures in a few hundred million years. We can ignore them for now.
Until the time I’m about to describe, knowledge did not exist on planet Earth. Nor had it ever existed. The prefix “proto” means “first” for a reason. Single-celled plants were relatively rare but still common enough to be found with a microscope and indeed were the stock from which a new way of dealing with the world was about to evolve. Knowledge was about to become evolution’s newest trick, and the very basis for evolving a new way to evolve. We have a front-row seat thanks to our mind being able to imagine this setting and a single creature which was also the very epitome of the word “singular” in its ability to create that very first bit of knowledge.
What generally separates plants from animals? Movement, of course, even just seemingly random movement. This morning for the first time there would be meaningful movement. What makes movement meaningful? Knowledge. What makes knowledge meaningful? Movement. This critter was about to become the first expression of the mind-body aspect of evolution in a very Zen fashion. Now back to Hillock.
Hysteresis - All or Nothing
As various potassium ions make their way through the left membrane of our proto-neuron over time, they tend to increase the ionic charge within this cell compared to the saline solution outside of this cell. This is best described as a type of ionic priming, the build-up of total ionic charge within the membrane. For now, we can also think of it as “sensing” for the accumulated ionic tension reflects the density of potassium captured by the membrane and so also reflects the recent density of potassium ions outside the cell. At least on average. Some ions slowly leak back out over time, thus decreasing this ionic tension or priming but mostly they are contained and accumulate. How much priming can our cell take? That’s where hysteresis comes in. This particular cell, this particular morning, had a weakness that became its strength.
Let’s get over to the right side of the cell again. This is where that weak spot evolved an interesting characteristic. The ionic tension of potassium is additive up to a critical and consistent inflection point, reflecting the physics of biology. Here I have to make a careful distinction. The ionic priming is not a function of potassium density, but there is a relationship, yielding a mathematical relation. The reason for this function/relation distinction is that there’s a random aspect to potassium density and membrane weakness. It’s not determinant. Our left-brain would like to think it’s determinant as that would make for a more accurate prediction, but we don’t need to project perfect accuracy into a story where there isn’t any to be had.
So reflecting average density, potassium ions would build up in concentration or slowly leak back out, but if (and when) this potassium ion concentration reached a critical inflection point in a given period of time something very interesting happened. These ions diffused across the cell creating a gradient of ionic potential in the process. This particular spot in the cell membrane weakened but did not actually rupture as membranes are flexible. This weak spot was about to release its potassium ions all at once hysterically, as in hysteresis.
Ionically (and ironically), hysteresis is best understood and characterized in how magnets change states. This sudden release might create a chemical signal of sorts when this group of ions flooded out without bursting the membrane.
If you’re not familiar with magnetic hysteresis, think toggle switch. If you haven’t noted the difference, toggle switches are not like most light switches found in houses. Toggle switches were once more common but are now typically found in old World War II airplane dashboards and other specialized equipment. Toggle switches have a rounded-off shiny metal button-shaped lever instead of the more squared-off plastic version found in common house switches today. They also have another VERY important aspect best characterized by how they turn off and on. It’s called “all or nothing”.
You can push a toggle switch very carefully past its center point until it goes just barely beyond, then it flips over completely without applying any more force. Next, you can do the very same thing in the opposite direction to turn it off. Toggle switches always switch when moved just past center. They were an early attempt to avoid electrical glitches. Toggle switches are a mechanical version of magnetic hysteresis, but critically, neurons do not remain in their switched state. They immediately switch back to their resting condition, producing a signal but not a change in state, a weakness of this particular metaphor, like the weakness in Hillock’s membrane.
Magnets, toggle switches, neurons, and yes, humans in the macro context all exhibit hysteresis. Observe that humans in relationships with other humans maintain a type of dynamic power balance, right up until they don’t. A failing marriage will have a building tension right up until the point of divorce. And then hysteresis. Few marriages survive the conviction of divorce. Decursively.
Something similar to hysteresis evolved in Hillock’s membrane - it gained an ionic inflection point. Feel free to apply any metaphor that helps as I describe this asymmetric ionic weakness. Some of the biologists among you will recognize this weak spot as the beginning of a “protein channel” using positive feedback. As ions accumulated, tension built until it gave up most of its potassium ions all at once before resetting to its normal resting condition ionically.
It’s also a mechanical inflection point that allows a gun to fire. Yes, one more and perhaps better metaphor. Once gunpowder begins to burn. It’s all or nothing. This is one reason a neuron’s firing is called a triggering event. In other words, impact ignites gun power, then it’s all or nothing. Something similar happened with Hillock this particular morning, knowledge was about to come into existence. And then be gone.
I Don’t Know, But the Neuron Does!
See what I did with the above title? I made the distinction between subjective and objective knowledge. As your narrator, I don’t objectively know when the tension gets too high for the membrane to “hold”, but Hillock’s membrane literally DOES subjectively know. And that’s when it fires. The sovereignty of the decision lies with Hillock. "You can lead a horse to water, but it's up to the horse if it wants to drink." That's because it's the same with the horse's neurons. the sovereignty of knowledge lies with the neuron.
Finally, the ionic tension became too much for Hillock’s membrane and a large number of potassium ions burst forth from the right side of our proto-neuron which caused Hillock to move a tiny amount in a random direction somewhat away from this cloud of potassium ions, and at the same time creating a localized exception to entropy in the form of potassium density.
Interestingly, the new locations of these potassium ions could be described as an expanding probability cloud around the location from which they were released. This would become a useful localizing beacon for later progeny, a sign of other lifeforms in the area.
Though only an approximation, this bit of imperfect knowledge about the relationship between itself and other lifeforms in the immediate surrounding area was just enough to make a difference in the probability of survival somewhat like that game of “Life” described above. If this was not the case, the next steps in evolution would have been far less likely.
If we characterize this bloom of potassium ions as a chemical signal, even though it only lasted for an instant, it was still enough of a signal about the proximity of other lifeforms to gain an advantage over the other plant cells at that moment. It was just enough that a new classification kingdom was born - animals. Life on Earth would never be the same. When you disturb a random process, you create a type of order, in this case, a bias of displacement yielding a chemical signal.
A Plant Becomes an Animal
At that very moment, in this cell, floating in the Great Basin of North America, knowledge was created for the very first time, and then immediately was gone. No one noticed. This was because other animal life that might be able to sense such an event did not yet exist on Earth, but shortly would, once Hillock replicated. It’s only our imagination that provides a view of this very special moment. So back to the details of our very first proto-neuron. What exactly was being detected? Why does it matter? And what does it “mean”?
Was the creation of this knowledge worth the energy expended in managing the ionic balance? The answer is likely yes, or this particular trick would not be with us today even though it’s now a far more refined and energy-efficient version.
What was being detected of course, like the Venus Flytrap, was a chemical signal of significant life in the area, or at least the odds were 81 to 1 that life was being detected, and those were pretty good odds, considering. What did Hillock do with this ethereal knowledge? Two things. As this specific type of ionic release occurred, the body of this cell moved a very small amount in a random direction allowing itself to optimize its relative location for survival somewhat like Brownian motion.
The second thing that occurred is that a concentrated bit of chemistry (potassium ions in this case) being released into the seawater would provide a type of signaling for itself and other future progeny. It would tend to keep randomly moving until potassium concentration decreased to give it space from its neighboring lifeforms. This would have the result of producing the inverse of entropy, at least in a very limited and localized context. Such knowledge would become more significant over time as this new species evolved. This cell not only improved the odds of survival for itself, it literally became “Maxwell’s Demon” for concentrating potassium ions.
Carnot’s Second Law of Thermodynamics may be challenged by the above concept, but only locally. Though entropy on a macro scale mostly dominates, there are many hyper-localized exceptions. How else could life ever become increasingly complex? Even evolution itself could be described as a form of extropy.
Before you get excited about negative entropy or signaling progeny, what I’m describing is only hypothetical. This event may have occurred in a completely different way, but at some point, the first animal cell DID appear. And because of replication (the more significant half of evolution as Richard Dawkins noted in, “The Selfish Gene”). Some form of selection for the creation of knowledge occurred at some point millions of years ago. Existing animal life is proof.
Now you may consider this example of questionable value in understanding the creation of knowledge, but that’s the point. It barely qualifies as an example at all, which was my objective in this flight of fancy. I may be completely wrong in this description, but it doesn’t matter greatly. If it didn’t happen exactly in this way, the important thing was, it happened. We have the result to study. Somehow evolution got from plant to proto-animal and this description is as good as any for now. It’s enough to move us along in our quest to understand knowledge and the neuron.
You might not think that the ratio between sodium and potassium within a cell compared to that outside the cell is very useful knowledge, but when you consider these ratios as an indicator for lifeforms, and thus the probability of encountering food or becoming food, the significance for survival increases dramatically and disproportionately. I honestly don’t know if this particular bit of knowledge was the first to evolve, but it likely occurred early in the evolutionary process, almost certainly before sodium-potassium pumps evolved. In any case, I was looking for one of the most primal examples of knowledge generation and this is my favorite so far. In the greater scheme of things, the accuracy of the example is far less important than the concept itself. If you have a better candidate for the very first genesis of knowledge, let me know.
You may also wonder why I chose such a primal description of this first animal life. It’s specifically because this critter predates ion pumps, synapses, dendrites, and even axons, yet still has all the critical functions to be a proto-neuron. All of these other things would emerge in due course. Evolution had to start somewhere. The simplest version is the most likely.
The point of this example is that a lifeform was able to produce a chemical signal that reflected the ionic and mathematical relationship, in the form of a probability ratio between sodium and potassium ions. I could have described this mathematical relation as objective information except that it is not material nor consistent, exists for only a very small part of a second, and is not stored anywhere. But knowledge of this relationship is similar to what I’ve described in the last post. It was ephemeral, ethereal, and not very accurate, but it still beats the odds for survival so it had utility and conviction approximating Plato’s definition of knowledge, perhaps better. Well, except for missing the truth, which is why I describe it as a useful lie.
Finally, a reasonable person might not consider what was created when this first proto-neuron fires as ethereal knowledge at all. Said possible knowledge was not required for the said critter to move, and perhaps even evolve. Where exactly is the knowledge? And why does it matter? The answers to these questions are subjective and relative to the critter in question. Not us. Did it survive? Yes. Or perhaps it was a cousin. It doesn’t matter much which. What matters is that some proto-animal found that useful moment to fire, perfect or not. As you’ll see the quality of knowledge will improve, but it will take time. Now onward.
Proto-protozoan, Version 2.0 - Friend or Foe?
It’s ten million years later, or perhaps a hundred million. It’s hard to know. Evolution moves in fits and starts, of which the Cambrian explosion was only the most dramatic example. Knowledge was fragile and quite rare at first. The early tricks were sometimes the most difficult to get right. Evolving a new way to evolve was not easy, nor probable. But it did happen for it exists now. New developments were disproportionate, even extremely disproportionate. The start was slow, but the acceleration, amazingly fast as evolution tends to reapply these tricks over and over, and quite effectively.
At long last, our first animal has finally specialized into having four cells, each now differentiated from later plants which have even more structure. There were two sensor neurons, an interneuron, and a muscle cell. This multi-celled protozoan has a primitive neural net with its signals converging on that muscle cell. The interneuron has actual dendrites and each of the neurons have useful ion pumps, a modest proto-axon bump, and most importantly, synapses for both input and output connections. We can now better describe how a proper neuron might create some practical knowledge. Yes, I’ve jumped over an amazing amount of evolutionary detail. Someone else will have to fill it in.
This second animal example in many ways does the same thing as the first, just more effectively. That 81-to-one ratio between the probability of encountering sodium and potassium ions is now part of our newly minted neuron’s basic machinery and will forever be part of basic biology for all neurons. One of the sensor neurons in this pair still detects potassium ions and can be considered the ancestor of all neurons in various respects. One of its progeny will take up residence in our tongue and evolve to become our taste receptor for “salt”, the sodium kind.
The prime difference of movement between plants and animals is also set, with a few exceptions here and there as noted. You can now think of these two kingdoms as two logical sets in this ionic ocean.
Something else which is very important has happened in the last few million years - the very first xenophobia, and the need for discrimination. Such discrimination between one thing or another is the very essence of what neurons do in the process of creating knowledge.
Because of their general inability to move, plant cells have to compete with other plant cells for light to create and store energy. Our new animals have the ability to move and so manage their territory more effectively. This movement is so important to our newly-minted proto-animals that the act of movement, along with where and when to move has become the reason for our brand-new upgraded neurons to exist. Where and when are forms of knowledge, but you won’t find this knowledge in any physical form except for the occasional firing of these neurons. This ephemeral knowledge is mostly a reflection of the challenges the world presents to these proto-creatures in the moment.
Plants and animals have had a schism and will never again be united as one. They no longer share the same agenda. Plants passively replicate and focus on converting sunlight into energy. Our new Proto-protozoan has become a mobile vegetarian. It finds surviving more effective by simply eating plant cells than to convert energy from the sun on its own. This newly defined relationship between plants and animals has made them competitors of sorts. So far, Proto-protozoan, Version 2.0 is not a cannibal. That too will later change. For now, its agenda is knowing the difference between plants and animals, reflecting the relationship between the two kingdoms.
Even though other animals are not the best of friends, there is an advantage to being part of a herd of its own kind. Fortunately, this new protozoan version 2.0 has evolved a way to recognize its food - the very first sense of smell, which brings us to our new sensor neuron. I hope you will forgive me for not describing it as a neuron; its dendrites do not have any synapses. Instead, they have the ability to chemically smell plant cells as differentiated from other animal cells. This is the quality, (and the knowledge), that it communicates to its adjacent interneuron.
So how does our new sensor neuron tell the difference between a plant cell and another protozoan? It could be as simple as being able to detect chlorophyll in the water, or perhaps some other molecular marker now lost to history. In any case, this “odor” allows our sensor neuron to fire when this marker is in the water causing its interneuron to trigger movement in the adjacent muscle cell, and as we’ve already noted, seemingly “random” movement may actually have greater than random value for survival. These two sensors can be thought of as an analogical “OR” from the interneuron’s subjective perspective. The logical “OR” is fairly obvious as there are now two reasons to move. The “ana” part will be explained in due course.
Follow the Mosquito
Again, the mosquito obviously isn’t on the human proto-path of evolution, or even close. But we may share a homing trick so distant in the past that it existed long before the mosquito or even insects in general. Here’s where taking decursion in the opposite direction can be of help in understanding the nature of Proto-protozoa Version-2.0. We’ll start with how a modern mosquito finds human blood for food.
Among other methods, mosquitos tend to move in a random direction for a random distance using a type of biological random number generator. They also have a neural sensor that fires when it detects carbon dioxide. The firing of this neuron, (and the bit of knowledge it represents), inhibits the mosquito’s random direction generator and so the path continues straight forward until no more carbon dioxide is detected.
If we return to that model of the probability cloud of the drunk and the lamppost, this resetting of the random number generator establishes a new “lamppost” nearer the source of possible human carbon dioxide. Note that this is not determinant machinery. Nor logical. It’s all about probability in several different ways, and the trick is about beating the odds which is all that’s required to get a fresh meal of blood. Probability is also what makes knowledge different from information as I’ve described in the last post. In any case, this is one possible method for our Proto-protozoan, Version 2.0 to locate food using a different marker to help create this important knowledge.
The four cells making up our new animal can now herd with others of its kind as friends, yet also focus on eating plants instead of random cannibalism. If you haven’t noticed, Version 2.0’s inherited ability to sense ion ratios has divided its world into living and non-living proximity, and now its new ability to smell plants has further divided the set of living into either plants or animals. The intersection of these four mathematical sets could be described as a new more complex type of knowledge that allows our critter to know the difference between eating dirt, yet not being a cannibal. Knowledge is becoming more abstract. And more useful.
For you techy types you can think of this set discrimination as a type of binary search, a very logical process that can yield prosperity, analogically. And it does. I hope it is clear how the firing of these two specialized neurons creates different types of knowledge to yield a third type of knowledge allowing for movement and increasing the probability of survival and replication. So far evolution has converted the relationship between the living and the non-living and also the relationship between plants and animals into very specific, though often, inaccurate knowledge.
Again, I’ve likely got many of the details of our Proto-protozoa Version-2.0 wrong, but it matters little. They obviously evolved in some fashion or they wouldn’t be here now. What’s more important is the concept of them using a type of very primal knowledge “to understand” the relationship between themselves and the nature of the things around them. It used this knowledge to time movement, survive and replicate. Knowledge is the key to survival, and neurons create it. Complexity just makes knowledge more specific as our next example will demonstrate.
Continued:
