Finding Paths of Return in the Brainforest
The Search for Neural Correlates Was Always a Dead End
When I read Francis Crick’s popular book on neuroscience - The Astonishing Hypothesis - I brought with me a conceptual tool that I had in my ‘back pocket’ since art school. I used it to help me make sense of what Crick revealed about the workings of the brain. Unexpectedly, that tool showed me certain contradictions in Crick’s perspective that turned out to be connected to a massive paradigm shift that was emerging at that time in the field of neuroscience.
I call that tool the Continuity Principle. It is based on a simple conceptual equation:
Meaning = Continuity
Crick presented a strong case for his main hypothesis - that everything you consider to be You, your thoughts, memories, feelings, and even free will, are ‘no more than the behaviour of a vast assembly of nerve cells and their associated molecules’. But when he wrote this, the field of neuroscience was on the cusp of a massive paradigm shift. The evidence is in that book.
He successfully debunks the common notion that human perception, awareness and consciousness arise from an inner projection screen in the brain watched by some mysterious inner being. He does this by showing how the brain dismantles sensory inputs into countless separate features such as colour, edges, shape, motion, etc.
But he then poses the question of how the brain puts all these diverse features back together into a unified awareness of objects and experience. He never answers that question.
Throughout the book he refers to the search for ‘neural correlates’ - persistent neural patterns that represent the things we encounter in the outside world. For example, Crick uses the term ‘awareness neurons’ and even ‘the neural correlate for consciousness’.
In part one of this article, I talked about some of the problems with that approach. The main problem is that a neural correlate implies a dead end to neural processing. And that contradicts the fact that all neurons have outputs - so the processing must continue, somewhere.
Looking through my Continuity Principle lens, it didn’t make sense to describe the brain as a system of connections and continuities and then rely on a dead-end concept, such as neural correlates, to ‘explain’ awareness. I wanted to know what happens to that flow after the correlate? But there was nothing in Crick’s book that really answered the question. It was like an elaborate joke without a punchline.
I puzzled about that for a decade or two, wishing I knew an actual neuroscientist to ask them “Did I miss something here?”
The Revolution in Neuroscience
Somewhere around 2015, I stumbled across a book by György Buzsáki called ‘Rhythms of the Brain’. Being a dancer, this title felt like an old friend. So I bought the book immediately.
But it was sooo technical. I have zero experience with chemistry, biology, and neuroscience (other than the Crick book). The book sat on my shelf for years. Until Covid.
When we took a holiday to Cuba in early 2020, there was a possibility of getting stranded there if the virus thing got worse. So I decided I should take a book that would keep me occupied (on the beach) for a few weeks. I took ‘Rhythms of the Brain’.
As it happened we returned home just days before the declaration of a worldwide pandemic. Then everything shut down, especially dance clubs. So I still had lots of time for reading. Just no beach.
It took me a year to read that book. When I had almost finished, I felt I had missed some important pieces. I needed a second pass to understand it better. So I started again, from the beginning. Another year later I finished it, finally.
From the outset, Buzsáki puts everything about brain function into a time perspective. He points out that nature is full of examples of cycles of activity occurring over time. He refers to the continuity of life and the fact that meaning can occur only when experienced through time. Well, there’s those two words again in rapid succession. I was sold - and only page 5.
The Problem With The Traditional Approach to Neuroscience, As Demonstrated by Dominos
Buzsáki points out a fundamental flaw in the way neuroscientists have traditionally approached their research. I didn’t really pay much attention to this the first time I read the book, but on the second pass the importance of it stood out. In fact, in 2019 Buzsáki published another book through Oxford University Press called ‘The Brain from Inside Out’ where he amplifies this critique loud and clear.
As that second book title suggests, the common flaw that he identified in neuroscience research strategy was to attempt to understand the brain from the ‘outside’. In contrast, Buzsáki asserts that when you know what’s actually going on inside the brain, you have a better chance of understanding what it really does - including how perception works and even consciousness.
Buzsáki points out that the philosophical roots of the outside-in problem stretch back as far as Aristotle where thinkers invented names for imagined concepts and now modern neuroscience fell into a trap of trying to explain these imagined things based on neural events. To make things even more confusing, more recently people started using computer terminology to ‘explain’ what happens in the brain. All of this led to the search for ‘representations’ of outside ‘objects’ in neural mechanisms.
So an outside-in research approach might set out to show correlations between neural events and a thing we (i.e. the experimenters) know about on the outside. If an experiment shows a ‘correlation’ between certain neural firings and the subject seeing a flower they are holding, this may strike the experimenter as an ‘Aha!’ moment. But it doesn’t take into account the endless other possible reasons for any given neuron to fire - such as sending messages to the muscles of the arm to grip the flower.
As Buzsáki puts it, there is no ‘representation’ of any stimuli in the brain ‘until the experimenter interprets the data.’ (Page 15: ‘The Brain From Inside Out’) It’s largely a subjective matter.
This search for neural representations of ideas we have actually invented ties directly into the search for neural correlates. This is a major characteristic of the old paradigm, along with an apparent tendency to look at neural networks from a ‘feedforward’ perspective. Let me illustrate what a feedforward network is, using a grossly simplified explanation that involves dominos.
You’ve seen the trick. The dominos are stood up close together, forming a line, defying probabilities of accidental falls. We wait for that first push, then ... away they go!
The more dominos, the better the trick. It’s a celebration of cause and effect that excites us, perhaps way more than it should.
And it’s a good metaphor for the way neurons behave in a feedforward network. Crudely put, when all the dominos have fallen and the action stops, there is your dead-end neural correlate. Crick in his book seems to talk around this point. He doesn’t clearly answer the question of where signals go after they hit a neural correlate. And, he rarely mentions feedback routes.
The Critical Importance of Feedback Loops
The work of Buzsáki and his colleagues was central to the massive paradigm shift in the mid 1990s. Buzsáki, jointly with two other scientists, won the inaugural Brain Prize in 2011 for his work in this area. The new paradigm places a lot of emphasis on feedback routes.
Many of the brain’s feedback loops do something that was largely ignored in the Outside-In era. They bring in inhibitory signals to modulate the flow of excitatory ‘traffic’. Traffic lights, basically. And what controls the inhibitory signals? Well other excitatory signals - other traffic. It’s a complex interplay between excitation and inhibition - kind of like life itself.
The result of this is that large scale network oscillation activity emerges. The brain starts to sing!
Traditional neuroscience perspectives had tended to avoid seeing oscillations as important because they were regarded as the stuff of physics. The simple oscillations of physics didn’t match up with the rich complexity of brain activity. Crick does have a chapter on Oscillations but it’s highly speculative, reflecting the views of that time.
But in Buzsáki’s Inside-Out perspective, neuronal oscillators, particularly those arising from feedback loops, are central to everything. Oscillators are found everywhere throughout the brain, from individual units (neurons) to massive collections of them (networks).
This remarkable ability of the brain to self-regulate its own information flow gives it the ability to rapidly reprogram itself. Think about that the next time you watch hockey players in action. This real time reprogramming capability is central to what Buzsáki declares is the primary job of the brain - to predict.
So, where neural processes were previously assumed to come to ‘dead ends’ AKA the neural correlates, Buzsáki points out that information never stops flowing. There are no dead ends in the brain.
Getting back to the dominos, this means that they don’t actually fall - they oscillate. They fire and they reset, propagating signals through the networks as they go.
The Power of Brains Built on Music, and Core-Radiance
A large part of Buzsáki’s first book goes into the technical details of how these oscillations occur. An important thing to note is that there are many levels of oscillators, and they each have their own frequency ranges. Buzsáki’s descriptions of this are very complex and technical. So I’m going to give it a try using the birds in my backyard. (Remember the birds at the feeder in my previous article?)
Neurons, like birds, are all about interaction. Like those birds in my backyard, they often work in sync - i.e. they act together at the same time.
The birds are tiny, low power creatures. They act in groups to achieve outcomes like picking seeds off the ground, together, and collectively watching out for danger. This is survival for them. Neurons can do similar group behaviour and the result is also collective power. These neural flocks are called ‘cell assemblies’.
In my backyard bird story, the noise of the neighbour’s bicycle scared the little things away. And if there were more birds grazing in the neighbour’s yard, they certainly would have also flown off into their nearest tree. If there was a construction site nearby and someone blasted a stick of dynamite, all the neighbourhood birds would have taken off.
And all these triggered events happen at different frequencies and power levels.
At the high frequency end we have birds coming and going constantly in small numbers. If enough birds accumulate on the ground, a threshold could be reached and a bunch more might suddenly appear out of the trees to join them. And then some will fly back. That’s how birds work.
At a slightly lower frequency, other trigger events happen - like the noisy neighbour going about their business, clattering their bicycle, slamming a door or whatever. Loud noises introduce a greater power level that sets our birds, and his birds, on edge. It’s a lower frequency event but the effect is bigger because more birds hear it, in multiple backyards. So lots of groups of birds take off to hide in their trees.
And the construction site explosions happen least often (thankfully) but are very high power. They have the biggest impact, upsetting the entire neighbourhood.
This is a pattern of energy impact that happens across different frequencies: the rarer the event, the bigger the impact. This scaled energy relationship is called the 1/f power dynamic. It describes a pattern that is halfway between chaos and order.
But, hold, I’m lying - or at least really over-simplifying this. My birds are only illustrating how power and frequency are inversely related and that’s just half the story. The other half is about feedback - which can make the entire system self-activating. A more accurate picture of true 1/f dynamics in the brain would involve some of the birds collectively acting on the lower frequency drivers - like tossing bicycles around, slamming doors or even setting off the occasional stick of dynamite. That breaks my analogy but hopefully you get the point.
Statistically, the 1/f pattern is how music works. It’s the reason music is pleasing to listen to. It is also the pattern behind the structure of trees.
And it’s central to how the brain works. Buzsáki reveals that the 1/f power distribution is the outcome of the interaction of nested oscillators in the brain. He also suggests that this ‘noise’ is critical to the way brain organizes information across time scales. Furthermore, he says that when the brain is in the 1/f state, it is highly sensitive to ‘weak and unpredictable environmental perturbations’. (Page 131: ‘Rhythms of the Brain’)
And here’s my main point. This is an important instance of the concept of Core-Radiance at work. Low frequency oscillations, with increased power, are very good examples of ‘Core’ energy. And likewise, the dispersed, high frequency, low power oscillations are equally clear examples of Radiant energy. The interfusion of the two produces something that not only matches the pattern of music, but is also critical to brain function, including, presumably, human consciousness.
Meaning Really is Continuity, Isn’t It?
When Buzsáki demonstrated the importance of feedback loops and oscillations in brain function, he also opened the door to seeing the concept of continuity as playing a bigger, possibly critical role.
The traditional view assumes the brain is a relatively passive ‘viewing’ mechanism, utilizing inner representations of the outside world. But Buzsáki points out that this is a fallacy. He asserts the brain’s primary function is to actively predict - and to test its own predictions. Reliable predictions about the outside world help us survive in time and space. This is true for any creatures with a nervous system. But we humans also evolved with increased memory capacity and various other mechanisms to better predict what the heck is going on in the world. Our brains help us figure out what happens next.
The result of predictability is continuity. Meaning.
All of it relates to survival - our most fundamental continuity. How we define ourselves also factors in. We can take on different ways of defining ourselves, different ‘identities’ and we will find things that are meaningful to each of those identities. Whatever we see as meaningful will help an identity persist.
Buzsáki states that ‘computation in the brain always means that information is moved from one place to another.’ (Page 116: ‘Rhythms of the Brain’) Core-Radiance also appears here, taking information from a radiant space into a focused core and projecting again into another radiant space. We can see this in every nerve cell and throughout the networks.
If we could see the information flowing through any of the billions of complex pathways, we would see it compressing and releasing, continuously. Almost like breathing.
Various forms of continuity occur in the brain either structurally or through its processing behaviour. As I see it, continuity, in one form or another, is the basis for all meaning. Applying the Continuity Principle to this, the brain is not only producing predictions, it produces all meaning - to help us survive.
This view of the brain is entirely different than that of the ‘passive viewer’ of an external fixed reality. The brain does not simply represent an external reality, it constructs all that we assume to be real.
This is not to paint a picture of a Matrix-style dual universe. There is a real Universe out there. But our ability to understand it and survive within it requires that our brains build ways that can reliably predict what comes next.
The insights coming from modern neuroscience can help us see the world in a different way. Objects are less like permanent fixtures and more like flocks of birds than we might have imagined. What is apparent though is that the pattern of Core-Radiance - halfway between order and chaos - is a key part of how our brains work.
And the Principle of Continuity also plays a big role here. Everything I’ve learned about the way the brain works reinforces the idea that where there is continuity, there is meaning. Meaning is not absolute, it is created when the brain finds continuities, through predictions. And a key aspect of how the brain does that is by moving information around between Core and Radiant spaces.
That is speculation on my part and a single article like this isn’t big enough to fully flesh out the argument. I’ve barely scratched the surface! I’ll return to this in future articles. But even this quick summary of recent discoveries and insights in neuroscience opens the door to a perspective that sees the universe as the interplay, and interfusion, of Core and Radiant energy forms. I want to know more about that from a physics perspective. So that’s going to be the topic of my next article.
This article is part 4 of a series on Core-Radiance. If you haven’t already seen the previous three here are the links. Enjoy!
The Big Bang Theory of Trees
I was in art school when I first saw trees in a way that changed my life. This moment of vision triggered a chain of discoveries about art, science and human relationships.
My Best Two Words That Describe All Human Relationships
In my previous article, I talked about how learning to see the energy of trees was a life-changing experience. It turned out my artist’s vision of tree energy was based on solid scientific concepts. But, as clear as my vision was, the words to describe it in
Thanks for reading!
References
- Francis Crick, *The Astonishing Hypothesis: The Scientific Search for the Soul* (Scribner, 1994)
- György Buzsáki, *Rhythms of the Brain* (Oxford University Press, 2006)
- György Buzsáki, *The Brain from Inside Out* (Oxford University Press, 2019)
- Voss, R.F. & Clarke, J. (1978). “’1/f noise’ in music: Music from 1/f noise.” *Journal of the Acoustical Society of America*, 63(1), 258-263.