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Pattern recognition computation using action potential timing for stimulus representation

Hopfield, J. J. (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature, 376 (6535). pp. 33-36. ISSN 0028-0836. doi:10.1038/376033a0. https://resolver.caltech.edu/CaltechAUTHORS:20150605-090832064

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Abstract

A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one scheme of neuroarchitecture can be used for very different sensory modalities and seemingly different computations. The oscillations and anatomy of the mammalian olfactory systems have a simple interpretation in terms of this representation, and relate to processing in the auditory system. Single-electrode recording would not detect such neural computing. Recognition 'units' in this style respond more like radial basis function units than elementary sigmoid units.


Item Type:Article
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http://dx.doi.org/10.1038/376033a0 DOIArticle
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Additional Information:© 1995 Nature Publishing Group. I thank A. V. M. Herz, J. F. Hopfield, G. Laurent, D. W. Tank and M. C. Waltham for criticism of the manuscript, and I. Aleksander for the hospitality of Imperial College, where part of this work was done. This work was supported by the NSF and the Ron and Maxine Linde Venture Fund.
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NSFUNSPECIFIED
Ronald and Maxine Linde Venture FundUNSPECIFIED
Issue or Number:6535
DOI:10.1038/376033a0
Record Number:CaltechAUTHORS:20150605-090832064
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150605-090832064
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:58032
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 Jun 2015 17:56
Last Modified:10 Nov 2021 21:58

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