Stupidly late to these comments but very well articulated, Blake.
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Replying to @SussilloDavid @tyrell_turing and
I think it's perfectly reasonable for a scientist to choose to avoid a certain field of theoretical research because they don't believe the tools are there to falsify it experimentally.
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Replying to @pfau @SussilloDavid and
Yes, totally. But, that's precisely what we have to change (and what a few groups are working on, including mine): our credit assignment models need to start making physiological predictions that can be falsified.
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Replying to @tyrell_turing @pfau and
Wait, credit assignment = backprop?
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Replying to @xaqlab @tyrell_turing and
The brain does credit assignment by whatever was cool at NeurIPS 5 years ago.
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Let’s add meat to this then, though. What are good options for credit assignment that *don’t* require efficient access to an estimate of the 1st order gradient of an objective function w.r.t. a given synaptic weight deep in a network? Honest question.
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Replying to @AdamMarblestone @pfau and
(I realize many answers will restrict the architecture or objective fxn greatly, to allow specialized non-backprop ways to get such gradients, which wouldn’t work for general fxn approx in arbitrary net topology. What can you really do with those? What’s the best alternative?)
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Replying to @AdamMarblestone @xaqlab and
I was more commenting on the sociology of the field, where CoSyNe tends to pick up on whatever was big in ML a few years ago.
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Replying to @pfau @AdamMarblestone and
And I think the reason neurotheory tends to go in cycles based on ML trends rather than building on prior work is the lack of experimental validation.
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