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dc.contributor.authorDu, Bosen
Sorensen, Danny
Cox, Steven J.
dc.date.accessioned 2015-01-08T14:55:03Z
dc.date.available 2015-01-08T14:55:03Z
dc.date.issued 2014
dc.identifier.citation Du, Bosen, Sorensen, Danny and Cox, Steven J.. "Model reduction of strong-weak neurons." Frontiers in Computational Neuroscience, 8, (2014) http://dx.doi.org/10.3389/fncom.2014.00164.
dc.identifier.urihttp://hdl.handle.net/1911/78907
dc.description.abstract We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio-temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust.
dc.language.iso eng
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.title Model reduction of strong-weak neurons
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.contributor.funder Air Force Office of Scientific Research
dc.citation.journalTitle Frontiers in Computational Neuroscience
dc.subject.keywordLGMD
predictor-corrector
quasi-active
proper orthogonal decomposition
discrete empirical interpolation
dc.citation.volumeNumber 8
dc.contributor.publisher Frontiers Media
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.3389/fncom.2014.00164
dc.identifier.pmcid PMC4267280
dc.identifier.pmid 25566048
dc.identifier.grantID DMS-1122455 (National Science Foundation)
dc.identifier.grantID CCF-1320866 (National Science Foundation)
dc.identifier.grantID FA9550-12-1-0155 (Air Force Office of Scientific Research)
dc.type.publication publisher version


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