Can A Single Neuron Understand the Whole Brain?

Neuroscientists are beginning to ask themselves: Can a human brain understand the human brain? The sheer amount of descriptive data on the brain being generated in neuroscience labs is terribly daunting — and is being described as “an existential crisis” in the field of cognitive science.

Can a Neuron Understand the Human Brain?

No, it isn’t meant to. But for humans to begin to understand what the human brain can do, it helps to better understand the complexity of the individual neuron.

Neurons are pretty smart — a lot smarter than we thought they were. Each neuron in the brain has thousands of synaptic connections with neighbors, but neurons also communicate with each other electrically via gap junctions and via direct transfer of messenger RNA packets from one neuron to another. We are still learning new ways in which individual neurons perform complex computations and how small local groups of neurons work together for more demanding work.

What can an individual neuron learn? Apparently individual neurons in the human medial temporal lobe (MTL) can quickly learn to distinguish a novel stimulus from a familiar stimulus.

We found that the average single-trial classification performance increases from 67% correct for one neuron to 93% when six simultaneously recorded neurons are considered __ Individual Neurons Learn Distinctions from Single Trials

Such learning by individual neurons is not representative of how the higher-level brain works overall. But watching such very low level learning taking place in real time is an awe-inspiring experience for many individual human observers of the human brain.

Non-Synaptic Coordination

Regional neurons may coordinate their activity under the influence of a “chemical bath” such as brain-wide release of hormones or in the context of an “electric field effect.”

… a neural network can give rise to sustained self‐propagating waves by ephaptic coupling, suggesting a novel propagation mechanism for neural activity under normal physiological conditions.

The mechanism most consistent with the data is ephaptic coupling whereby a group of neurons generates an electric field capable of activating the neighbouring neurons (Zhang et al. 2014). __ The Journal of Physiology Oct 2018

Researchers are just beginning to understand the implications of this non-synaptic “electric field effect.”

… a group of neural waves, either physiological or pathological, share the same speed of ∼0.1 m/s without synaptic transmission or gap junctions, and this speed is not consistent with axonal conduction or ionic diffusion. The only explanation left is an electrical field effect. __

Just as the complexity of individual neurons is not well appreciated, the complexity of regional groupings of neurons — which can vary as to specific members, from one time to the next — is far from being understood.

Why Should Young Neuroscientists Expect to Understand the Brain?

The unexpected levels of complexity that are encountered by researchers at the neuronal and micro-regional levels, are reflected by unexpected levels of complexity at regional and inter-regional levels of the brain. At every level of brain function, new levels of complexity are being discovered. So why are we likely to keep hearing cries of despair such as this:

The question of how we might begin to grasp the entirety of the organ that generates our minds has been pressing me for a while. Like most neuroscientists, I’ve had to cultivate two clashing ideas: striving to understand the brain and knowing that’s likely an impossible task. __ Existential Crisis in Neuroscience?

But perhaps we should put our “comprehensive understanding of the human brain” on hold for a while, as we continue to try to understand a tiny fraction of how our mammalian brains work? After all, our brains are  roughly as complex as the patterns of matter in the entire cosmic web!

Based on the latest analysis of the connectivity of the brain network, independent studies have concluded that the total memory capacity of the adult human brain should be around 2.5 petabytes, not far from the 1-10 petabyte range estimated for the cosmic web!

Roughly speaking, this similarity in memory capacity means that the entire body of information that is stored in a human brain (for instance, the entire life experience of a person) can also be encoded into the distribution of galaxies in our universe. Or, conversely, that a computing device with the memory capacity of the human brain can reproduce the complexity displayed by the universe at its largest scales. __ Strange Similarity of Brain and Cosmic Networks

Understanding the Level of Resolution in the Brain May be Helpful

It is likely that moment-to-moment human consciousness occupies a much lower level of resolution than many cognitive scientists comprehend. If researchers focus on the “nanolevel of consciousness” they are apt to be led somewhat astray, and will likely waste a great deal of time and resources — if understanding consciousness is what they are after.

Subconscious vs Conscious Bandwidth
Figure 2. Sensory bandwidths reaching sub-conscious and conscious mind, from Tor Norretranders’ The User Illusion. Visualisation from Stephen Few’s Information Dashboard Design
Image Source

Many years ago, “The User Illusion” by Tor Norretranders helped to expose the sparse levels of bandwidth displayed by conscious mental processes of the human brain. Human consciousness is much slower and coarser than lower level brain activities tend to be. This suggests corresponding brain processes that are much slower and coarser than the processes at the neuronal and neuronal group levels.

“Rhythms of the Brain” by Gyorgy Buzsaki provides us with a rough sketch of what such slower, coarser macro-brain processes may look like.

Basic Visual Encodings
Figure 7. Relative accuracy of comparison using different basic visual features, from Cleveland and McGill. Visualisation from Alberto Cairo’s The Functional Art
Image Source

We can try a number of tricks to “stretch” our bandwidth of consciousness — especially for fast visual processing — but there are limits. Whether those limits can in turn be stretched is a question for the future.

Daniel Kahnemann, in Thinking, Fast and Slow, introduces the terms System 1 and System 2 to differentiate between the information processing that occurs in our sub-conscious and conscious minds respectively. The former encapsulates the functions that are uncontrolled, always-on and effortless, while the latter refers to those functions that are controlled but require effort to engage.

… While the activities of visual working memory and long-term memory occur largely in System 2, the rapid, automatic and massively parallel bottom-up processing is entirely System 1.

… Therefore, to maximise the power and efficacy of a visualisation, we should seek to encode as much information as possible in the pre-attentive features perceived during bottom-up processing.

Jacques Bertin set out basic rules for intuitive, accurate and universal encoding of data as abstract shapes in his 1967 book, Semiologie Graphique. Subsequent derivative work has further refined our understanding of these rules and our associated perceptions, and has clarified how they relate specifically to data visualisation. __ Source

Understanding the Brain

The brain must be understood on multiple levels, simultaneously. But not in real time, of course. Things happen much too quickly on the levels of molecules, neuronal subcellular components, neurons, and neuron groups. Our speed and resolution of thought is too slow and coarse — and so we must “compress” our comprehension using the tools of thought which we possess.

First, it is important to understand that our brains are not designed for technical work, or for thinking as we typically understand the word. Our brains are designed to “see” and to “move.” It is best to understand how so much of our other thinking is shoe-horned into those functions.

Not designed to remember large amounts of information in fine detail:

In the modern world, information bombards us constantly. And if we rely on our … capacity to memorize as much as possible, the only way to make it fit is to store it at a low resolution. When we come to review what we’ve learned, we’re dismayed to find only ‘blurred’ information and vague approximations of what was so clear when we experienced it. __

Not designed to multitask:

Here’s practical advice from a neuroscientist: Don’t try to multitask. It ruins productivity, causes mistakes, and impedes creative thought. Many of you are probably thinking, “But I’m good at it!” Sadly, that’s an illusion. As humans, we have a very limited capacity for simultaneous thought — we can only hold a little bit of information in the mind at any single moment. You don’t actually multitask, you task-switch. This wastes time, makes you error-prone and decreases your ability to be creative. __

Brains are designed to acquire “skills” that help us survive and propagate. The skills we learn today are somewhat different from those our ancestors used.

Our brains have constructed unique skill patterns for each activity we repeatedly engaged in, such as reading, speaking, cooking, golfing, driving, bike riding, algebra problem-solving, violin playing, etc. Every skill, worldview and ability we have was “self-constructed” from countless hours of experience, observation and practice- by carefully applying knowledge, methods and techniques- repeating actions over and over again until we developed progressively higher levels of comprehension and skill mastery.

As you might imagine, this understanding has important implications for learning and education. __ Source

The skills our brains are best constructing involve the visual system, the system of body movement and body perception, and the hearing system.

But there are ways that we can learn to use our brains as if they were designed to think. In today’s world, even the roughest tradesman or blue-collar worker needs to solve difficult problems and learn technical skills. And for today’s leading-edge scientists, engineers, and technical thinkers? Let’s just say that there is a reason why so many members of that group are taking nootropics.

What does it take to solve hard problems:

There are 3 things essential to problem solving.

  1. The right paradigm
  2. Pattern recognition
  3. Insight
  • The right paradigm: This is the most important part about problem solving. You need to approach the problems the right way. Even if you are intrinsically motivated and want to study because you’re interested make sure you don’t despise problem solving. Many smart people do this. When people sit to solve problems, they want to just get through because they want to improve in their domain. Make sure that you enjoy the process, that’s the most effortless and the most effective way to get better at problem solving.
  • Pattern recognition: Make the gears mesh. I’ll take an analogy and compare problem solving to a system. In order to understand a system perfectly, you need to know how things fit in with each other (meshing of gears). The other important thing is you need to see the big picture. The one thing that separates a genius from his peer is that he has the ability to see things in problems that others don’t. He forms the not so obvious connections in problems in order to crack them. He does not do it because he has some innate power to solve problems.

    Here’s what geniuses do: They ask questions. It’s not that whenever you see a problem your brain starts popping up everything useful that is essential to solve a problem. Questions are like cues in a scavenger hunt, they head you in the right direction. There is no other way to do it whatsoever. The genius does this over and over again till it becomes subconscious. It’s only a matter of a couple of seconds before he solves a problem that takes a day or two for an average person, it’s these abilities that make it so. I will elaborate more on the asking questions and methods to solve questions later in the answer.

  • Insight: Insight is just the next level of pattern recognition. It is pattern recognition done subconsciously.

__ How to solve Hard Problems

Our mammalian brains evolved over tens of millions of years, assuming a hodge-podge of functions and inclinations — all oriented around the imperative to survive and procreate. We evolved to sense, to move, and to keep good enough account of what we have sensed and where we have moved to create maps and schemas for where we can profitably navigate, and which of our possible actions are skillful and well-done at any particular moment.

But some human brains discovered ways to think logically, mathematically, scientifically — methodically. Systems of thought can count for more than sheer brilliance of individual thought. Ancient China, for example, boasted many original inventions. But the scientific and industrial revolutions had to wait for a European revolution of higher mathematics, systematic thinking, and closely recorded experimental observations.

The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century. It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; experimental and measurement-based testing of deductions drawn from the hypotheses; and refinement (or elimination) of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises. __

Until humans adopted the useful crutch of systematic thinking, they could only depend on “hit or miss” discoveries based on individual brilliance, which generally faded away after several generations and loss of interest by the political classes.

We Must Develop More Useful Crutches

In order to understand how our brains work — and how they could work better — we will need to develop some very useful systems of thinking. Or perhaps, systems of brain crutches. The 1962 essay by Douglas Englebart, Augmenting Human Intellect, was written in the dawn of modern computing. It is an attempt to create a systematic approach to raising human intellect in such a way that humans will be able to continuously solve new problems faster than they create ever newer problems.

Augmenting humans for complex problem-solving is a different approach to this problem than the contrary desire to turn over all our problems to super-intelligent machines. Decades ago, any alternative to making humans more capable and more masterful would have been unthinkable. It is only in the past 20 years or so that the pseudo-intellectual factions of humankind have become convinced that the human race is irredeemably beyond repair.

It is this recent simultaneous rise of both the extinctionist left and post-humanism, that make a better understanding of the human mind and brain so crucial to the ultimate human attainment of “the next level.”

Eventually, we will understand the human brain very well. But we will gain that understanding in fits and starts, struggling against it every step of the way.

It is never too late for a Dangerous Childhood © . (Remember that Dangerous Children tend to make the best Post-Apocalyptologists, and that the pathway to The Next Level is apt to follow a path best trodden by expert post-apocalyptologists.)


More on solving hard problems

More on augmentation —

Individuals who operate effectively in our culture have already been considerably “augmented.” Basic human capabilities for sensing stimuli, performing numerous mental operations, and for communicating with the outside world, are put to work in our society within a system—an H-LAM/T system—the individual augmented by the language, artifacts, and methodology in which he is trained. Furthermore, we suspect that improving the effectiveness of the individual as he operates in our society should be approached as a system-engineering problem—that is, the H-LAM/T system should be studied as an interacting whole from a synthesis-oriented approach.
___ Augmenting Human Intellect

Deep Work — an important skill

The Neuroscience of Intelligence by Richard Haier (free book download from in multiple formats)

Brief scientific review of the neuroscientific underpinnings of intelligence

Edward de Bono’s Thinking Course (Video 1/3)

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2 Responses to Can A Single Neuron Understand the Whole Brain?

  1. bob sykes says:

    Once upon a time, I read somewhere that there is a fairly constant 40 Hz electrical wave in the brain. Do I misremember? What would be the purpose of such a wave?

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