PMID-29123069 A neural algorithm for a fundamental computing problem
- Ceneral idea: locality-sensitive hashing, e.g. hashing that is sensitive to the high-dimensional locality of the input space, can be efficiently solved using a circuit inspired by the insect olfactory system.
- Here, activation of 50 different types of ORNs is mapped to 50 projection neurons, which 'centers the mean' -- concentration dependence is removed.
- This is then projected via a random matrix of sparse binary weights to a much larger set of Kenyon cells, which in turn are inhibited by one APL neuron.
- Normal locality-sensitive hashing uses dense matrices of Gaussian-distributed random weights, which means higher computational complexity...
- ... these projections are governed by the Johnson-Lindenstrauss lemma, which says that projection from high-d to low-d space can preserve locality (distance between points) within an error bound.
- Show that the WTA selection of the top 5% plus random binary weight preserves locality as measured by overlap with exact input locality on toy data sets, including MNIST and SIFT.
- Flashy title as much as anything else got this into Science... indeed, has only been cited 6 times in Pubmed.
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