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Science. 2017 Nov 10;358(6364):793-796. doi: 10.1126/science.aam9868.

A neural algorithm for a fundamental computing problem.

Author information

1
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
2
Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
3
Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA, USA.
4
Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA. navlakha@salk.edu.

Abstract

Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem.

PMID:
29123069
DOI:
10.1126/science.aam9868
[Indexed for MEDLINE]

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