I've realized that Big Ideas about the brain tend to be a chaotic mix of:

• Big Ideas about how we think the brain works,
• Big Ideas about how to figure the brain out (tools, approaches and the like),
• Big Ideas about what we'll do with knowledge about the brain once we have it (like therapeutic approaches).

For example, I jumbled them all up here in the #BrainIdeasCountdown
https://neuromatch.social/@NicoleCRust/109557289393362842

Now that I see the potential for order in Big Idea chaos, I'm obsessed with putting everything that I read and hear (here and elsewhere) in these conceptual buckets. Most notably, I find myself thinking a lot:

"That's a very nice tool and all, but What have/will we learn with it?

#BigBrainIdeas #neuroscience #cognition #neuroAI

Some thoughts on #BigBrainIdeas

Writing a book, I read a lot and I one thing jumps out at me: a continuum (from clarity to muddled) in how big ideas, ideas and facts are presented. I wonder if nearly every scientist would benefit from thinking a bit more in this way: What big idea am I pursuing? What are the more specific ideas under this umbrella (that remain bigger than hypotheses)? What's the evidence? Where are the holes? What parts of this space remain largely untouched (and why)?

A masterclass in laying out ideas is this 2001 Annual Review from Simoncelli and Olshausen on Efficient Coding:

https://www.cns.nyu.edu/pub/eero/simoncelli01-reprint.pdf

Reading it, you see that these two clearly understand what they are working on, why they are doing it, how it fits in with everything else that's happening in brain research, what the evidence is, and what parts of the space remain largely unexplored. Inspiring!

In comparison, there's a trend for reviews to launch with smaller ideas absent context, focus largely on the author's own work, and leave you with a disoriented sense that this was an ad campaign rather than a survey. When I read a review like this, I'm convinced that the authors wrote it that way because they really don't understand the broader context of their work; they are narrowly focused on what they do. And I wonder: would their work benefit from a broader perspective? Or is the narrow focus benefiting them, perhaps by keeping them focused?

(BTW: the former also makes for the most brilliant type of faculty job talk whereas the latter fails to connect - that's a time when Zooming out needs to happen and often does not).

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 of 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?

#neuroscience #psychology #neuroAI #cognition @cogneurophys #BigBrainIdeas