The fact that you can selectively turn off parts of cortex (e.g. via cooling) and the brain keeps working fine with only very specific deficits probably tells us all we need to know about the importance of backprop in biological learning.
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Consider an architecture where each module predicts its many inputs as a function of its outputs and some global conditioning. Might be very modular, despite the use of backprop like gradient flows.
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We may conclude that BP is not sufficient as a model of how the brain works (duh). Clearly the brain has a bias towards forming modules that vanilla BP does not have. I mean yes, there is anatomy in the brain. There is also anatomy in ANNs.
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