Picking up on some of the BIG IDEAS in brain research, which was wonderfully chaotic when we last discussed in December under the hashtag #BrainIdeasCountdown, e.g. https://neuromatch.social/@NicoleCRust/109557289393362842

Here's an attempt to fill in some blanks, and let's flip the hashtag: #BigBrainIdeas. I'll focus on the notion that there are facts, ideas and then there are "Big Ideas" and I'll focus on the last one. Please join in!

I'd argue that one of the most influential Big Ideas about the brain in the latter half of the 20th century is the is the notion that:

The neocortex of the brain is made up of a generic functional element that is repeated again and again and from this repetition, all of cortical function emerges

I'm talking about the cortical column, first described by Vernon Mountcastle in 1957. The unit contains ~10K neurons and humans have ~25 million of them. The rapid evolution of humans is proposed to have followed from a rapid expansion of cortex that happened because it has this repetitive crystalline structure. The gist behind the "functional" bit is that each unit always does the same generic computation, and the different functions of different brain areas result from the different inputs that these units receive. @TrackingActions very nicely summarizes the ideas here: https://www.nature.com/articles/s41583-022-00658-6

So what does this generic functional unit do? Proposals vary. One idea, also reflected in deep convolutional neural networks, is that it does two(ish) things: selectivity and invariance, stacked repetitively to support things like recognizing objects. Other proposals suggest that the brain is a prediction machine and each unit contributes a little bit to those predictions in a manner that relies not just on feedforward connectivity, but also feedback. Some proposals suggest that the function of the unit varies along a gradient as a consequence of biophysical properties like receptor expression: https://www.nature.com/articles/s41583-020-0262-x.

Among brain researchers, this Big Idea is polarizing - obvious to some and misguided to others. Where are you in terms of your 'buy in' with this big idea?

@NicoleCRust @TrackingActions @cogneurophys I am not sure that this idea has actually been very influential on the grand scale of neurocog theories. The modern theoretical approach seems to revolve around understanding how brain areas connect in networks to solve problems, and I can't see how generic computation would inspire/drive this perspective.

@bwyble @TrackingActions @cogneurophys
The idea spawned maps like this, which have been highly influential for neurocog theories, no?

@NicoleCRust @TrackingActions @cogneurophys I thought that diagram was the result of neuroanatomy studies. Why would a generalized function theory lead to a highly specific wiring diagram like this?

@bwyble
Yes, but ... Felleman & Van Essen defined the hierarchical levels of this diagram according to the cannonical microcircuit rule: L4 receives input; L2/3=feedforward output; L4/5=feedback output.

@NicoleCRust @bwyble

That rule was derived from anatomy too.

@DrYohanJohn @NicoleCRust I agree with Yohan, I don't think the microcircuit is crucial there, rather it's observing that there are laminar patterns that are more or less ubiquitous.

@bwyble @DrYohanJohn
Interesting! To me, those ideas could not be more connected.

@NicoleCRust @bwyble @DrYohanJohn

shameless plug, read my CONB on this topic with Adam K, for my more edited, fleshed out opinion!
https://www.sciencedirect.com/science/article/pii/S0959438822001246

(just insert "microcircuit motif" whereever you read cell-type, Adam K. and I disagree about which phrase is the better one :p)

I get the argument about confusing implementation and computation, but I agree with with @NicoleCRust that the idea of canonical cortical computations has been super influential, especially / at least in vision (which is all of computational neuro anyway, amiright?)

I think the idea of simple, repeated computation is kind of necessary / permissive for a certain types of "grand unified theory" that are very intuitively appealing exactly because they squash together computation and implementation into one little thing that is intuitively understandable in words. The fact that these "theories" are conceptually "small" down to implementation mean that people understand them, they catch on, they drive research.

Now whether this is positive or not, is an open debate. But I think there's no way we can say that ideas like predictive processing, backprop, divisive normalization, and maybe even convolution havent been wildly influential in the field.

To state this in a slightly more aggressive way than I feel: I do think the idea of a canonical microcircuit is very useful, because it's studiable! If we just start off by assuming everything is brain soup it's very easy to just give up and assume we'll never understand implementation. I rather start off with the assumption that there exist architectural motifs that matter, and try to take that as a hypothesis from which to start, than just admit defeat.

So maybe the answer is, it depends on the level of explanation you are lookign for. If you dont care baout multilevel understanding then "how brain regions connect in networks" black box type of understanding is enough. I personally think it's only step 1, and then understanding more details of implementation is step 2. Furthermore, I've come to think that the implementation detaiuls will probably constrain and help us understand the higher level.

@achristensen56 @NicoleCRust @bwyble @DrYohanJohn

The fact that, when, say, sight is lost early, other sensory modalities can successfully invade areas they wouldn't necessarily be processed, in suggests standardisation of function/microanatomy across large parts of the brain.

@strangetruther @achristensen56 @NicoleCRust @bwyble @DrYohanJohn

A simple argument for some sort of canonical cortical computation is: "cerebral cortex ... processes ... diverse tasks with what appears to be a remarkably uniform, primarily six-layer architecture, albeit with significant differences in details across species and cortical areas [1,2,3􏰩,4–10,11􏰩,12–14]. ... This has long suggested the idea that a piece of six-layer cortex with a surface area on the order of a square millimeter constitutes a fundamental cortical ‘processing unit’ [e.g. 16,17].The cortex varies in surface area by a factor of 10000 across a set of 37 mammalian species, while thickness (the distance across the layers) varies only by a factor of 10 over the same species [18], suggesting that the most salient evolutionary change in cortex has been enormous multiplication of the number of ‘units’ [e.g. 14]."
The last two references are:
14. Rakic P: Confusing cortical columns. Proc Natl Acad Sci U S A 2008, 105:12099-12100.
18. Hofman MA: On the evolution and geometry of the brain in mammals. Prog Neurobiol 1989, 32:137-158.

This is from the 1st paragraph of a Current Opinion review paper that I wrote that Nicole cited further up in the thread (https://pubmed.ncbi.nlm.nih.gov/26868041/)

More generally I think there are (at least) 4 mammalian (and, except for cortex, vertebrate) brain structures that each clearly have repeating architecture, and that -- at least as studied in primates -- communicate pretty intimately with one another: cortex, thalamus, basal ganglia, and cerebellum. They communicate with specificity, eg a given piece of cortex communicates with given thalamic nuclei and given regions of basal ganglia and cerebellum, which communicate with one another, e.g. Boston & Strick https://www.nature.com/articles/s41583-018-0002-7. These specific ctx/BG/cerebellum interactions cover at least posterior parietal through frontal cortex, and perhaps higher sensory cortices as well, i.e. they cover all sorts of cognitive processing, not just motor processing which is the traditional function assigned to BG and cerebellum. So it's not just the enormous multiplication of cortical "units" (with diversification, i.e. the spectrum Y.J. referred to) , but also the corresponding multiplication of their partner thalamic, basal ganglia, and cerebellar "units" that suggest some fundamental computional operation, albeit again with diversification.

You don't see this sort of thing in the brainstem. Different bespoke nuclei or other sorts of neural units each do different pieces of different computations. In contrast, the existence of these structures with repeated modular subunits with roughly repeating architectures (despite much variability and diversification), and with specific patterns of interconnections between their modules, as well as their enormous growth in numbers of modules at least in mammalian evolution, all just scream out that some sort of computational motif is being repeated (with variations on the repeated units, much as multiple copies of a gene provide a substrate for evolution into multiple variants -- and occasionally quite new structures). That wouldn't happen by accident.

Kenji Doya long ago postulated that cortex is for associative learning, BG for reinforcement learning, and cerebellum for error-correcting learning. That still sounds like a decent 0th-order take. And, I'll add my speculation, one function of thalamus -- not all that it is doing -- is to take any modality of information whatsoever and convert it into a language that cortex understands, using a roughly uniform architecture with roughly uniform biophysics across all these different modalities of information.

#neuroscience