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Variational bayesian inference for point process generalized linear models in neural spike trains analysis

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dc.contributor.author Chen, Zhe
dc.contributor.author Kloosterman, Fabian
dc.contributor.author Wilson, Matthew A.
dc.contributor.author Brown, Emery N.
dc.date.accessioned 2012-05-14T16:41:02Z
dc.date.available 2012-05-14T16:41:02Z
dc.date.issued 2010-03
dc.identifier.isbn 978-1-4244-4296-6
dc.identifier.isbn 978-1-4244-4295-9
dc.identifier.issn 1520-6149
dc.identifier.uri http://hdl.handle.net/1721.1/70598
dc.description.abstract Point 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.sponsorship National Institutes of Health (U.S.) (NIH Grant DP1-OD003646) en_US
dc.description.sponsorship National Institutes of Health (U.S.) (Grant R01-DA015644) en_US
dc.language.iso en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.relation.isversionof http://dx.doi.org/10.1109/ICASSP.2010.5495095 en_US
dc.rights Article 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.source IEEE en_US
dc.title Variational bayesian inference for point process generalized linear models in neural spike trains analysis en_US
dc.type Article en_US
dc.identifier.citation Chen, 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.department Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences en_US
dc.contributor.approver Brown, Emery N.
dc.contributor.mitauthor Chen, Zhe
dc.contributor.mitauthor Kloosterman, Fabian
dc.contributor.mitauthor Wilson, Matthew A.
dc.contributor.mitauthor Brown, Emery N.
dc.relation.journal Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) en_US
dc.identifier.mitlicense PUBLISHER_POLICY en_US
dc.eprint.version Final published version en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US
dspace.orderedauthors Chen, Zhe; Kloosterman, Fabian; Wilson, Matthew A.; Brown, Emery N. en


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