The argument is about how to study computation in the brain. It is not claiming that biology doesn't matter, quite the opposite actually.
Fortunately there is a lot of mol bio of learning/memory to build on. And can compare before/after various learning. Point is you still want all this. Even if you interpret computationally by seeking ~ architectures & cost functions that will get optimized, and ask which & how.
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Right. But wouldn’t one want to be way way more explicit about how one actually thinks about the mechanisms by which these architectures and their structure is brought about. Simply labeling large parts of neuroscience as stamp collectors seems unhelpful at best.
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I do prefer this 2 come w/ large dose of circuit hypothesis detail. Refactor much neuro as looking 4 cost fxns, optimizers, conserved dev programs + what doesn’t fit that. As Blake’s dendritic DL work does. & yes “tuning” gives insight to it. Pytorch code=result, not sole input.
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. With sequencing you have to have a pretty good idea what you are looking for; e.g. antisense lncRNA and promoter choice of protocadherins.