bingo! The brain probably approximates backprop at a minimum
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Replying to @tyrell_turing @MannyDePresso and
learning complex things in high-d space req. grad descent on global cost funcs
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Replying to @tyrell_turing @MannyDePresso and
see
@AdamMarblestone and@KordingLab review: http://journal.frontiersin.org/article/10.3389/fncom.2016.00094/full …1 reply 0 retweets 5 likes -
Replying to @tyrell_turing @MannyDePresso and
this might be a bit semantic about what we get to call backprop...
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Replying to @achristensen56 @tyrell_turing and
If back prop is just about using past rates with time dependency then a decaying elegibility trace is also back prop..
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Replying to @JonAMichaels @tyrell_turing and
IMO we get to call something 'approx backprop', if it approximates the updates that would happen if using actual backprop.
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Replying to @achristensen56 @tyrell_turing and
That's probably the most sane way of looking at it. We can test that.
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Replying to @JonAMichaels @tyrell_turing and
as for where to look, and what to look for, e.g. motor prosthetics might be a great place to start, you supply the target fxn.
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Replying to @achristensen56 @tyrell_turing and
I have such an experiment running so we'll see... neurons are fed into an RNN. I can test if the FR changes match ideal weight updates.
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Replying to @JonAMichaels @achristensen56 and
Yes sounds awesome
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Replying to @AdamMarblestone @KordingLab and
But... be mindful that only a *sub-population* of neurons may be subject to backprop-style updates...
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Replying to @AdamMarblestone @KordingLab and
...and the "weights" in question may be at a higher level than neuron-neuron connections, e.g., could be between assemblies/attractors...
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