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dc.contributor.authorChen, Zhe
dc.contributor.authorKloosterman, Fabian
dc.contributor.authorWilson, Matthew A.
dc.contributor.authorBrown, Emery N.
dc.date.accessioned2012-05-14T16:41:02Z
dc.date.available2012-05-14T16:41:02Z
dc.date.issued2010-03
dc.identifier.isbn978-1-4244-4296-6
dc.identifier.isbn978-1-4244-4295-9
dc.identifier.issn1520-6149
dc.identifier.urihttp://hdl.handle.net/1721.1/70598
dc.description.abstractPoint process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation. Variational Bayesian (VB) methods provide a computationally appealing means to infer the posterior density of unknown parameters, in which conjugate priors are designed for the regression coefficients in logistic and Poisson regression. In this paper, we develop and apply VB inference for point process GLMs in neural spike train analysis. The hierarchical Bayesian framework allows us to tackle the variable selection problem. We assess and validate our methods with ensemble neuronal recordings from rat's hippocampal place cells and entorhinal cortical cells during foraging in an open field environment.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH Grant DP1-OD003646)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant R01-DA015644)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2010.5495095en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleVariational bayesian inference for point process generalized linear models in neural spike trains analysisen_US
dc.typeArticleen_US
dc.identifier.citationChen, Zhe et al. “Variational Bayesian Inference for Point Process Generalized Linear Models in Neural Spike Trains Analysis.” IEEE, 2010. 2086–2089. Web. © 2010 IEEE.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.approverBrown, Emery N.
dc.contributor.mitauthorChen, Zhe
dc.contributor.mitauthorKloosterman, Fabian
dc.contributor.mitauthorWilson, Matthew A.
dc.contributor.mitauthorBrown, Emery N.
dc.relation.journalProceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsChen, Zhe; Kloosterman, Fabian; Wilson, Matthew A.; Brown, Emery N.en
dc.identifier.orcidhttps://orcid.org/0000-0003-2668-7819
dc.identifier.orcidhttps://orcid.org/0000-0001-7149-3584
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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