dc.contributor.author | Shimazaki, Hideaki | |
dc.contributor.author | Amari, Shun-ichi | |
dc.contributor.author | Brown, Emery N. | |
dc.contributor.author | Grun, Sonja | |
dc.date.accessioned | 2011-03-07T22:09:50Z | |
dc.date.available | 2011-03-07T22:09:50Z | |
dc.date.issued | 2009-04 | |
dc.identifier.isbn | 978-1-4244-2353-8 | |
dc.identifier.issn | 1520-6149 | |
dc.identifier.other | INSPEC Accession Number: 10701287 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/61621 | |
dc.description.abstract | A state-space method for simultaneously estimating time-dependent rate and higher-order correlation underlying parallel spike sequences is proposed. Discretized parallel spike sequences are modeled by a conditionally independent multivariate Bernoulli process using a log-linear link function, which contains a state of higher-order interaction factors. A nonlinear recursive filtering formula is derived from a log-quadratic approximation to the posterior distribution of the state. Together with a fixed-interval smoothing algorithm, time-dependent log-linear parameters are estimated. The smoothed estimates are optimized via EM-algorithm such that their prior covariance matrix maximizes the expected complete data log-likelihood. In addition, we perform model selection on the hierarchical log-linear state-space models to avoid over-fitting. Application of the method to simultaneously recorded neuronal spike sequences is expected to contribute to uncover dynamic cooperative activities of neurons in relation to behavior. | en_US |
dc.description.sponsorship | National Institute of Mental Health (U.S.) (R01 MH59733) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.). Pioneer Award (DP1 OD 003646) | 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.2009.4960380 | 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 | State-space analysis on time-varying correlations in parallel spike sequences | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Shimazaki, H. et al. “State-space analysis on time-varying correlations in parallel spike sequences.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. 2009. 3501-3504. © 2009, IEEE | en_US |
dc.contributor.department | Harvard University--MIT Division of Health Sciences and Technology | 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 | Brown, Emery N. | |
dc.relation.journal | IEEE International Conference on Acoustics, Speech, and Signal Processing : [proceedings] (ICASSP) | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
dspace.orderedauthors | Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grun, Sonja | en |
dc.identifier.orcid | https://orcid.org/0000-0003-2668-7819 | |
mit.license | PUBLISHER_POLICY | en_US |
mit.metadata.status | Complete | |