A neural algorithm for a fundamental computing problem
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 a...
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| Published in | Science (American Association for the Advancement of Science) Vol. 358; no. 6364; p. 793 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
10.11.2017
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| Subjects | |
| Online Access | Get more information |
| ISSN | 1095-9203 |
| DOI | 10.1126/science.aam9868 |
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| 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. |
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| AbstractList | 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. |
| Author | Navlakha, Saket Dasgupta, Sanjoy Stevens, Charles F |
| Author_xml | – sequence: 1 givenname: Sanjoy orcidid: 0000-0002-5960-5157 surname: Dasgupta fullname: Dasgupta, Sanjoy organization: Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA – sequence: 2 givenname: Charles F orcidid: 0000-0002-3709-9003 surname: Stevens fullname: Stevens, Charles F organization: Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA, USA – sequence: 3 givenname: Saket orcidid: 0000-0002-5505-9718 surname: Navlakha fullname: Navlakha, Saket email: navlakha@salk.edu organization: Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA. navlakha@salk.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29123069$$D View this record in MEDLINE/PubMed |
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| Title | A neural algorithm for a fundamental computing problem |
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