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dc.contributor.authorBrown, Emery Neal
dc.date.accessioned2020-08-21T13:16:17Z
dc.date.available2020-08-21T13:16:17Z
dc.date.issued2018-03
dc.identifier.issn2326-831X
dc.identifier.urihttps://hdl.handle.net/1721.1/126718
dc.description.abstractMathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant DMS-1127914)en_US
dc.description.sponsorshipNational Institute of Mental Health (U.S.) (Grant MH064537)en_US
dc.language.isoen
dc.publisherAnnual Reviewsen_US
dc.relation.isversionof10.1146/ANNUREV-STATISTICS-041715-033733en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceother univ websiteen_US
dc.titleComputational Neuroscience: Mathematical and Statistical Perspectivesen_US
dc.typeArticleen_US
dc.identifier.citationKass, Robert E. et al. “Computational Neuroscience: Mathematical and Statistical Perspectives.” Annual review of statistics and its application, 5, (March 2018): 183-214 © 2018 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.relation.journalAnnual review of statistics and its applicationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-09-30T15:09:46Z
dspace.date.submission2019-09-30T15:09:48Z
mit.journal.volume5en_US
mit.metadata.statusComplete


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