This is absolutely brilliant. I have been looking for a way into an understanding of learning within biological neural nets. I dont suppose there is source code around?
A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
11–17 of 17 posts
Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#12Needs a [pdf] flag
meta: this could be automated.
Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#13Needs a [pdf] flag
Why is it, by the way, that papers have the author names at the top but not the date? The dates are added to papers in references, so why not the date of this paper itself too? This one happens to have "Workshop track - ICLR 2018" at the top so has some dating, but most don't even have that
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Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#14So are we learning that brains and neurons are general purpose computation goo that can be applied to many different areas of signal processing yet?
Kind of feel like we already know that brains can do general purpose computation...
Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#15Yet to read this paper but wondering if the authors a familiar with the work of Dasgupta et al. on fly olfactory model for locality sensitive hashing?
https://www.biorxiv.org/content/biorxiv/early/2017/08/25/180...
I have been contemplating the relationship between random projections and compressive sensing since reading it and curious to read this paper for any insights on compressive sensing.
Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#16So are we learning that brains and neurons are general purpose computation goo that can be applied to many different areas of signal processing yet?
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Re: A computational model of the Moth Olfactory Network learns to read MNIST [pdf]
#17Earlier quoted context omitted.
It is ambiguous. It's not clear whether or not they performed the experiments with # samples / class > 20.
(paper author) You are correct that the 'natural' moth maxes out after about 20 samples/class. It is not yet clear whether this is an intrinsic limitation of the architecture (the competitive pressure on an insect is for fast and rough learning), or whether it is just an artifact of the parameters of the natural moth. For example, slowing the Hebbian growth parameters would allow the system to respond to more trainin…
It sounds like you ran experiments on the BNN with >20 samples/class. Why were those data points not included in Figure 2?