Feedback alignment in the human brain:
https://youtu.be/C_2Q7uKtgNs
Deep Learning with Ensembles of Neocortical Microcircuits.
More nails for the DL hammer. In any case, thanks for the link.
@rhyolight sorry for lifting the old topic. I have recently followed other works by Mitya Chklovskii. He is doing really interesting research, especially on the fruit fly brains. Is it somehow possible to make an interview with him?
I know it’s not so easy. Just would like to point to this possibility. Thanks.
Link: https://twitter.com/chklovskii
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I don’t have plans of interviewing neuroscientists unless they have something to do with that thalamus or cortex or some higher level brain parts than the fly brain.
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You may want to read his papers - he has some very interesting views on local voting to build representations.
This is work on an extremely important problem - how to separate different objects in a local neighborhood. This is close to the Numenta work on objects in columns. Think “cocktail party effect” to get some idea what to expect:
Blind nonnegative source separation using biological neural networks
In this he raises objections to the base neural model used in deep learning:
And suggests a better alternative based on bio-similarity here:
And a dive into principle component analysis:
“A key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons - projection neurons (PNs) of the antennal lobe - into a sparse representation in a much larger population of neurons” Very relevant to encoder design.
What do K-means clustering and manifold learning have in common? A semi-definite program formulation:
This work is relevant to the current efforts to build object representations at the column level.
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