Karl Friston is one of the greatest neuroscientists in history, cited over 245,000 times, known for many influential ideas in brain imaging, neuroscience, and theoretical neurobiology, including the fascinating idea of the free-energy principle for action and perception.

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Karl's Website: https://www.fil.ion.ucl.ac.uk/~karl/
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OUTLINE:
0:00 - Introduction
1:50 - How much of the human brain do we understand?
5:53 - Most beautiful characteristic of the human brain
10:43 - Brain imaging
20:38 - Deep structure
21:23 - History of brain imaging
32:31 - Neuralink and brain-computer interfaces
43:05 - Free energy principle
1:24:29 - Meaning of life

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The following is a conversation with Carl Fristen, 1 of the greatest neuroscientists in history.

Cited over 245,000 times, known for many influential ideas in brain imaging, neuroscience, and theoretical neurobiology, including especially the fascinating idea of the free energy principle for action and perception.

Carl's mix of humor, brilliance, and kindness, to me, are inspiring and captivating.

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And now, here's my conversation with Carl Fristen.

How much of the human brain do we understand from

the low level of neuronal communication to the functional level

to the highest level, maybe the psychiatric disorder level?

Well, we're certainly in a better position than we were last century.

How far we've got to go, I think, is almost an unanswerable question.

So you'd have to set the parameters, what constitutes understanding, what level of understanding do you want?

I think we've made enormous progress in terms of broad brush principles.

Whether that affords a detailed cartography of the functional anatomy of the brain and what it does right down to the microcircuitry in the neurons.

That's probably out of reach at the present time.

So the cartography, so mapping the brain.

Do you think mapping of the brain, the detailed, perfect imaging of it, does that get us closer to understanding of the mind, of the brain?

So how far does it get us if we have that perfect cartography of the brain?

It's a really interesting question, and it would determine the sort of scientific career you'd pursue if you believe that knowing every dendritic connection, every sort of microscopic synaptic structure right down to the molecular level was going to give you the right kind of information to understand the computational anatomy, then you'd choose to be a microscopist and you would study little cubic millimeters of brain for the rest of your life.

If on the other hand, you were interested in holistic functions and a sort of functional anatomy of the sort that a neuropsychologist would understand, you'd study brain lesions and strokes, just looking at the whole person.

So again, it comes back to at what level do you want understanding?

I think there are principled reasons not to go too far.

If you commit to a view of the brain as a machine that's performing a form of inference and representing things.

There are that level of understanding is necessarily cast in terms of probability densities and ensemble densities, distributions.

And what that tells you is that you don't really want to look at the atoms to understand the thermodynamics of probabilistic descriptions of how the brain works.

So I personally wouldn't look at the molecules or indeed the single neurons in the same way if I wanted to understand the thermodynamics of some non-equilibrium steady state of a gas or an active material.

I wouldn't spend my life looking at the individual molecules that constitute that ensemble.

On the other hand, if you go too coarse grain, you're gonna miss some basic canonical principles of connectivity and architectures.

I'm thinking here, this bit colloquial, but there's current excitement about high-field magnetic resonance imaging at 7 Tesla.

Well, it gives us for the first time the opportunity to look at the brain in action at the level of a few millimeters that distinguish between different layers of the cortex that may be very important in terms of evincing generic principles of conical microcircuitry that are replicated throughout the brain that may tell us something fundamental about message passing in the brain and these density dynamics or neuronal ensemble population dynamics that underwrite our brain function.

So somewhere between a millimeter and a meter.

Lingering for a bit on the big questions, if you allow me, what to you is the most beautiful or surprising characteristic of the human brain?

I think it's hierarchical and recursive aspect, it's recurrent aspect.

Of the structure or of the actual representation of power of the brain?

I was actually answering in a dull-minded way from the point of view of purely its anatomy and its structural aspects.

I mean, there are many marvelous organs in the body.

Without it, you wouldn't be around for very long.

And it does some beautiful and delicate biochemistry and homeostasis and you're evolved with a finesse that would easily parallel the brain.

It has a simple anatomy which is attractive in a minimalist sense, but it doesn't have that crafted structure of sparse connectivity and that recurrence and that specialization that the brain has.

So you said a lot of interesting terms here.

So the recurrence, the sparsity, but you also started by saying hierarchical.

So I've never thought of our brain as hierarchical.

Sort of I always thought it was just like a giant mess, interconnected mess where it's very difficult to figure anything out.

But in what sense do you see the brain as hierarchical?

of course, is what I used to think when I was, before I studied medicine and the like.

So a lot of those terms imply each other.

So hierarchies, if you just think about the nature of a hierarchy, how would you actually build 1?

And what you would have to do is basically carefully remove the right connections that destroy the completely connected soups that you might have in mind.

So a hierarchy is, in and of itself, defined by a sparse and particular connectivity structure.

I'm not committing to any particular form of hierarchy.

In virtue of the fact that there is a sparsity of connectivity, Not necessarily of a qualitative sort, but certainly for quantitative sorts.

So it is demonstrably so that the further apart 2 parts of the brain are, the less likely they are to be wired, to possess axonal processes, neuronal processes that directly communicate 1 message or messages from 1 part of that brain to the other part of the brain.

So we know there's a sparse connectivity.

And furthermore, on the basis of anatomical connectivity and tracer studies, we know that sparsity underwrites a hierarchical and very structured sort of connectivity that might be best understood a little bit like an onion.

There is a concentric, sometimes referred to as centripetal by people like Marcel Mesulam, hierarchical organization to the brain.

So you can think of the brain as in a rough sense like an onion.

And all the sensory information and all the afferent outgoing messages that supply commands to your muscles or to your secretory organs come from the surface.

So there's a massive exchange interface with the world out there on the surface.

And then underneath, there's a little layer that sits and looks at the exchange on the surface.

And then underneath that, there's a layer right the way down to the very center, to the deepest part of the onion.

That's what I mean by a hierarchical organization.

There's a discernible structure defined by the sparsity of connections that lends the architecture a hierarchical structure that tells 1 a lot about the kinds of representations and messages.

So going back to your earlier question, is this about the representational capacity or is it about the anatomy?

If 1 simply thinks of the brain as a message-passing machine, a process that is in the service of doing something, then the circuitry and the connectivity that shape that message passing also dictate its function.

So you've done a lot of amazing work in a lot of directions.

So let's look at 1 aspect of that, of looking into the brain and trying to study this onion structure.

What can we learn about the brain by imaging it?

Which is 1 way to sort of look at the anatomy of it, broadly speaking.

What are the methods of imaging, but even bigger, what can we learn about it?

Right, so, well, most imaging, human neuroimaging, that you might see in science journals, that speaks to the way the brain works, measures brain activity over time.

So, that's the first thing to say, that we're effectively looking at fluctuations in neuronal responses, usually in response to some sensory input or some instruction, some task.

Not necessarily, there's a lot of interest in just looking at the brain in terms of resting state and dodginess or intrinsic activity, but crucially at every point, looking at these fluctuations, either induced or intrinsic in the neural activity, and understanding them at 2 levels.

So normally people would recourse to 2 principles of brain organization that are complementary.

1, functional specialization or segregation.

It simply means that there are certain parts of the brain that may be specialized for certain kinds of processing.

For example, visual motion, our ability to recognize or to perceive movement in the visual world.

And furthermore, that specialized processing may be spatially or anatomically segregated, leading to functional segregation.

Which means that if I were to compare your brain activity during a period of viewing a static image, and then compare that to the responses of fluctuations in the brain when you were exposed to a moving image, say a flying bird, and we'd expect to see restricted, segregated differences in activity.

And those are basically the hotspots that you see in the statistical parametric maps that test for the significance of the responses that are circumscribed.

So now basically we're talking about some people have perhaps unkindly called a neocartography.

This is a phonology augmented by modern day neuroimaging, basically finding blobs or bumps on the brain that do this or do that, and trying to understand the cartography of that functional specialization.

So how much is there such, since that's such a beautiful sort of ideal to strive for, we humans, scientists, would like to hope that there's a beautiful structure to this, whereas, like you said, there are segregated regions that are responsible for the different function.

How much hope is there to find such regions in terms of looking at the progress of studying the brain?

Oh, I think enormous progress has been made in the past 20 or 30 years.

At the advent of brain imaging, the very notion of functional segregation was just a hypothesis based upon a century, if not more, of careful neuropsychology looking at people who had lost via insult or traumatic brain injury particular parts of the brain and then say, well, they can't do this or they can't do that.

For example, losing the visual cortex and not being able to see or losing particular parts of the visual cortex or regions known as V5 or the middle temporal region, MT, and noticing that they selectively could not see moving things.

And so that created the hypothesis that perhaps movement processing, visual movement processing, was located in this functionally segregated area.

And you could then go and put invasive electrodes in animal models and say, yes, indeed, we can excite activity here, we can form receptive fields that are sensitive to or defined in terms of visual motion.

But at no point could you exclude the possibility that everywhere else in the brain was also very interested in visual motion.

By the way, I apologize to interrupt, but a tiny little tangent, you said animal models.

Just out of curiosity, from your perspective, how different is the human brain versus the other animals in terms of our ability to study the brain?

Well, clearly, the further away you go from a human brain, the greater the difference is, but not as remarkable as you might think.

So people will choose their level of approximation to the human brain depending upon the kinds of questions that they want to answer.

So if you're talking about sort of canonical principles of microcircuitry, it might be perfectly okay to look at a mouse, indeed.

If on the other hand, you wanted to look at the finer details of organization of visual cortex and V1, V2, these are designated sort of patches of cortex that may do different things indeed do.

You probably want to use a primate that looked a little bit more like a human, because there are lots of ethical issues in terms of the use of non-human primates to transfer questions about human anatomy.

But I think most people assume that most of the important principles are conserved in a continuous way, right from, well, yes, worms right the way through to you and me.

So now returning to, so that was the early ideas of studying the functional regions of the brain by if there's some damage to it to try to infer that there's, that part of the brain might be somewhat responsible for this type of function.

Right, well, this actually is reverse a bit, come back to your sort of notion that the brain is a magic soup.

That was actually a very prominent idea at 1 point.

Notions such as Lashley's law of mass action inherited from the observation that for certain animals, if you just took out spoonfuls of the brain, it didn't matter where you took these spoonfuls out, they always showed the same kinds of deficits.

So, you know, it was very difficult to infer functional specialization pure on the basis of lesion deficit studies.

But once we had the opportunity to look at the brain lighting up, and it's literally, it's sort of excitement, neuronal excitement, when looking at this versus that, 1 was able to say, yes, indeed, these functionally specialized responses are very restricted and they're here or they're over there.

If I do this, then this part of the brain lights up.

In fact, shortly before with the advent of positron emission tomography, and then functional magnetic resonance imaging came along in the early 90s.

And since that time, there has been an explosion of discovery, refinement, confirmation.

There are people who believe that it's all in the anatomy.

If you understand the anatomy, then you understand the function at some level.

And many, many hypotheses were predicated on a deep understanding of the anatomy and the connectivity, but they were all confirmed and taken much further with neuroimaging.

So that's what I meant by we've made an enormous amount of progress in this century, indeed, and in relation to the previous century, by looking at these functionally selective responses.

So there's this sort of neophrenology, but finding bumps and hot spots in the brain that did this or that.

The bigger question was, of course, the functional integration.

How all of these regionally specific responses were orchestrated, how they were distributed, how did they relate to distributed processing and indeed representations in the brain?

So then you turn to the more challenging issue of the integration, the connectivity, and then we come back to this beautiful, sparse, recurrent, hierarchical connectivity that seems characteristic of the brain and probably not many other organs.

But nevertheless, we come back to this challenge of trying to figure out how everything is integrated.

But what's your feeling, what's the general consensus?

Have we moved away from the magic soup view of the brain?

And then maybe a further question, you said some people believe that the structure is most of it, that you can really get at the core of the function by just deeply understanding the structure.

I think it's got some mileage to it, yes.

So it's a worthy pursuit of going, of studying through imaging and all the different methods to actually study the structure.

Sorry, I'm just noting, you were accusing me of using lots of long words, and then you introduced 1 there which is deep, which is interesting.

Because deep is the sort of millennial equivalent of hierarchical.

So if you put deep in front of anything, not only are you very millennial and very trending, but you're also implying a hierarchical architecture.

is a depth, which is, for me, the beautiful thing.

That's right, the word deep kind of, yeah, exactly, it implies hierarchy.

That indeed, the implicit meaning of the word deep is a hierarchy.

So deep inside the onion is the center of your soul.

Maybe briefly, if you could paint a picture of the kind of methods of neuroimaging, maybe the history which you were a part of, you know, from statistical parametric mapping.

I mean, just what's out there that's interesting for people maybe outside the field to understand of what are the actual methodologies of looking inside the human brain?

Right, well, you can answer that question from 2 perspectives.

Basically it's the modality, what kind of signal are you measuring?

And they can range from, and let's limit ourselves to imaging based non-invasive techniques.

So you've essentially got brain scanners and brain scanners can either measure the structural attributes, the amount of water, the amount of fat or the amount of iron in different parts of the brain.

You can make lots of inferences about the structure of the organ of the sort that you might have abduced from an x-ray, but a very nuanced x-ray that is looking at this kind of property or that kind of property.

So looking at the anatomy non-invasively would be the first sort of neuroimaging that people might want to employ.

Then you move on to the kinds of measurements that reflect dynamic function.

And the most prevalent of those fall into 2 camps.

You've got these metabolic, sometimes hemodynamic, blood-related signals.

So these metabolic and or hemodynamic signals are basically proxies for elevated activity and message passing and neuronal dynamics in particular parts of the brain.

Characteristically though, the time constants of these hemodynamic or metabolic responses to neuronal activity are much longer than the neural activity itself.

And this is referring, forgive me for the dumb questions, but this would be referring to blood, like the flow of blood.

So there's a ton of, it seems like there's a ton of blood vessels in the brain.

So, but what's the interaction between the flow of blood and the function of the neurons?

And that interplay accounts for several careers of world-renowned scientists.

So this is known as neurovascular coupling, is exactly what you said.

It's how does a neural activity, the neuronal infrastructure, the actual message passing that we think underlies our capacity to perceive and act, how is that coupled to the vascular responses that supply the energy for that neural processing?

So there's a delicate web of large vessels, arteries and veins, that gets progressively finer and finer in detail until it perfuses at a microscopic level the machinery where little neurons lie.

So coming back to this sort of onion perspective, we were talking before using the onion as a metaphor for a deep hierarchical structure, But also I think it's just anatomically quite a useful metaphor.

All the action, all the heavy lifting in terms of neural computation is done on the surface of the brain.

And then the interior of the brain is constituted by fatty wires, essentially, axonal processes that are enshrouded by myelin sheaths.

And when you dissect them, they look fatty and white, and so it's called white matter, as opposed to the actual neuro pill which does the computation constituted largely by neurons and that's known as gray matter.

So the gray matter is a surface or a skin that sits on top of this big ball.

Now we are talking magic soup, but a big ball of connections like spaghetti, very carefully structured with sparse connectivity that preserve this deep hierarchical structure.

But all the action takes place on the surface, on the cortex of the onion.

And that means that you have to supply the right amount of blood flow, the right amount of nutrient, which is rapidly absorbed and used by neural cells that don't have the same capacity that your leg muscles would have to basically spend their energy budget and then claim it back later.

So 1 peculiar thing about cerebral metabolism, brain metabolism, is it really needs to be driven in the moment, which means you basically have to turn on the taps.

So if there's lots of neural activity in 1 part of the brain, a little patch of a few millimeters, even less possibly, you really do have to water that piece of the garden now and quickly.

And by quickly I mean within a couple of seconds.

So that contains a lot of, hence the imaging could tell you a story of what's happening

But it is slightly compromised in terms of the resolution.

So the deployment of these little micro vessels that water the garden to enable the activity to the neural activity to play out, the spatial resolution is in order of a few millimeters.

And crucially, the temporal resolution is the order of a few seconds.

So you can't get right down and dirty into the actual spatial and temporal scale of neuronal activity in and of itself.

To do that, you'd have to turn to the other big imaging modality, which is the recording of electromagnetic signals as they're generated in real time.

So here, the temporal bandwidth, if you like, or the low limit on the temporal resolution is incredibly small.

You're talking about, you know, nanoseconds, milliseconds.

And then you can get into the phasic fast responses that is in and of itself the neural activity and start to see the succession or cascade of hierarchical recurrent message passing evoked by a particular stimulus but the problem is you're looking at electromagnetic signals that have passed through an enormous amount of magic soup or spaghetti of connectivity and through the scalp and the skull.

So it's very difficult to know where you are.

You can either use an imaging modality that tells you within millimeters which part of the brain is activated, but you don't know when.

Or you've got these electromagnetic EEG, MEG setups that tell you to within a few milliseconds when something has responded, but you don't know where.

So you've got these 2 complementary measures, either indirect via the blood flow or direct via the electromagnetic signals caused by neural activity.

And then the second level of responding to your question, what are the, from the outside, what are the big ways of using this technology?

So once you've chosen the kind of mirror imaging that you want to use to answer your set questions, and sometimes it would have to be both, then you've got a whole raft of analyses, time series analyses usually, that you can bring to bear in order to answer your questions or address your hypothesis about those data.

And interestingly, they both fall into the same 2 camps we were talking about before.

You know, this dialectic between specialization and integration, differentiation and integration.

So it's the cartography, the blobology analyses.

I apologize, I probably shouldn't interrupt so much, but just heard a fun word, the blobology.

It's a neologism, which means the study of blobs.

Are you being witty and humorous, or is there an actual, does the word blobology ever appear in a textbook somewhere?

It would not appear in a worthy specialist journal.

It's the fond word for the study of literally little blobs on brain maps showing activations.

So the kind of thing that you'd see in the newspapers on ABC or BBC reporting the latest finding from brain imaging.

Interestingly though, the maths involved in that stream of analysis does actually call upon the mathematics of blobs.

So seriously, they're actually called Euler characteristics and they have a lot of fancy names in mathematics.

We'll talk about it, but your ideas in free energy principle, I mean, there's echoes of blobs there when you consider sort of entities, mathematically speaking.

You entities of, well, from the free energy point of view, entities of anything, but from the point of view of the analysis, the cartography of the brain, these are the entities that constitute the evidence for this functional segregation.

You have segregated this function in this blob and it is not outside of the blob.

And that's basically if you were a mapmaker of America and you did not know its structure, the first thing you're doing, constituting or creating a map, would be to identify the cities, for example, or the mountains or the rivers.

All of these uniquely spatially localizable features, possibly topological features, have to be placed somewhere.

Because that requires a mathematics of identifying what does a city look like on a satellite image, or what does a river look like, or what does a mountain look like.

What would it, what data features would evidence that particular thing that you wanted to put on the map.

And they normally are characterized in terms of literally these blobs or these sort of, another way of looking at this is a certain statistical measure of the degree of activation crosses a threshold.

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Karl Friston: Neuroscience and the Free Energy Principle | Lex Fridman Podcast #99