Because the inference at the end conflates function with mechanism.
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Replying to @MHendr1cks @tyrell_turing and
Could you define the two? Function has a normative connotation? Mechanism has a causal one?
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Replying to @KordingLab @tyrell_turing and
I mean it in the Tinbergen sense. What is this thing for vs how does it do it. There are different kind of can openers that do the ~same thing but with different designs and mechanisms. So if you only look at whether the can was opened you didn't learn anythign about mechanism.
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Replying to @MHendr1cks @KordingLab and
Which is why I like
@neurograce's point.2 replies 0 retweets 2 likes -
Replying to @MHendr1cks @KordingLab and
But tbh when I have seen the claim "we found nodes in our network with response properties that look like neuron classes" it's usually so general that it's not clear how it couldn't be true, because the classes are OFF / ON / Nothing.
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Replying to @MHendr1cks @KordingLab and
Again, that's not what we're advocating. Lemme give a concrete example: the Sacramento,
@somnirons and Senn model predicts that apical dendrite activity will decrease with error. That's a testable, falsifiable prediction.1 reply 0 retweets 2 likes -
Replying to @tyrell_turing @KordingLab and
I took that article to imply that we shouldn't care whether things like that are true or not. Just molecules?
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Replying to @MHendr1cks @KordingLab and
No! With all due respect, if that's what you thought you completely missed the point. Our point was not: ignore cellular stuff. Our point was: don't try to explain computation cell-by-cell, bc computation emerges from evolutionary and learning optimization processes.
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Replying to @tyrell_turing @MHendr1cks and
The argument is about how to study computation in the brain. It is not claiming that biology doesn't matter, quite the opposite actually.
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Replying to @tyrell_turing @MHendr1cks and
Yea, emphasizing that we should have our eye on learning rules that can actually lead to better performance in a large system doesn't mean having nothing to say about what that looks like on a cellular level. Learning rules are implemented through molecular mechanisms.
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My wild hot take: 1) this perspective may well be right and if so is super important 4 neuro-theory and AI — I’m investing energy on the idea that it may be right, 2) most important advance in neuro in next 5-10 years will be molecularly annotated connectome. No contradiction.
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Replying to @tyrell_turing @AdamMarblestone and
What's a molecularly annotated connectome? I'm on the side that a connectome is not going to be that useful but happy to be proved wrong. Certainly the brain has myriad *unseen* mechanisms that contribute to function but not evident in a connectome.
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