On explanations in brain research:

A thread of the same idea comes up again and again in brain research. It's the notion that identifying the biological details (such as the brain areas/circuits or neurotransmitters) associated with some brain function (like seeing or fear or memory) is not a complete explanation of how the brain gives rise to that function (even if you can demonstrate the links are causal). To paraphrase:

Mountcastle: Where is not how hup.harvard.edu/catalog.php?is
Marr: How is not what or why mechanism.ucsd.edu/teaching/f1
@MatteoCarandini: Links from circuits to behavior are a "bridge too far" nature.com/articles/nn.3043
Krakauer et al: Describing that is not understanding how cell.com/neuron/pdf/S0896-6273
Poppel: Understanding brain maps does not formulate "what about" the brain gives rise to "what about" behavior ncbi.nlm.nih.gov/pmc/articles/

Any other explicit references to add to this list? @Iris, @knutson_brain, Anyone?

Also, I imagine that some form of the opposite idea must also be percolating: the notion that 'algorithmic' descriptions of the type used to build AI will be insufficient to do things like treat brain dysfunction (where we arguably need to know more about the biology to, e.g., create drugs). Any explicit references of that idea? @albertcardona @schoppik, @cyrilpedia, Anyone?

@NicoleCRust @MatteoCarandini @Iris @knutson_brain @schoppik @cyrilpedia

As a biologist by training, it seems self-evident that if we are to come up with preventive measures or a cure for e.g., neurodegenerative diseases, we ought to be explicitly studying biological neural networks that suffer from such diseases in the first place.

That is not to say that we aren't going to learn a lot from studies of how artificial networks work and behave. We will. But the cure, for instance, would most likely have to be of the biological kind, and even more likely, an intervention on the immune system, be it vaccination or otherwise–I'm referring to e.g., multiple sclerosis and the Epstein-Barr virus, as there may be many more such cryptic, decade-after-infection effects on the nervous system.

#neuroscience

@albertcardona
I agree. It's the answer to the apparent conundrum: Given that our ability to build brains is growing so fast (AI), why isn't fixing them? The answer: we aren't actually building brains; we are building algorithms.

Perhaps it's so self evident that no one has bothered to write it down (unlike it's complement, listed above)?

@NicoleCRust @albertcardona

There are a few parts to this - for the first one, how far a descriptive approach will get us in terms of understanding function, I like what Cori Bargmann said in our conversation (which started with an analogy to the Human Genome Project and pathophysiology): understanding the components won't be an explanation, but it will set the boundaries within which the explanation(s) must be found.

In terms of AI, for me the most likely to be useful approach is comparative: to treat it as we would an alien lifeform in questions around the origin of life - but it will not necessarily map directly on to any understanding of the biological brain.

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@NicoleCRust @albertcardona

A somewhat related ref that I enjoyed this week was sent to me by Zach Mainen, a perspective on LLMs by Terry Sejnowski

direct.mit.edu/neco/article/35

@NicoleCRust @cyrilpedia @albertcardona

Tx!
Also send my HT to Zach Mainen for such central quote in the central part of the ,In Silico’ documentary:

» No!
We don't really care about spikes in the dendrites!
We don't want to predict spikes in the dendrites!
We want to predict what is going to dooo!!! «

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