Applying the Multivariate Time-Rescaling Theorem to Neural Population Models
DC Element | Wert | Sprache |
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dc.contributor.author | Gerhard, Felipe | |
dc.contributor.author | Haslinger, Robert | |
dc.contributor.author | Pipa, Gordon | |
dc.date.accessioned | 2021-12-23T16:22:20Z | - |
dc.date.available | 2021-12-23T16:22:20Z | - |
dc.date.issued | 2011 | |
dc.identifier.issn | 08997667 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/14266 | - |
dc.description.abstract | Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based on the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models that neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem and provide a practical step-by-step procedure for applying it to testing the sufficiency of neural population models. Using several simple analytically tractable models and more complex simulated and real data sets, we demonstrate that important features of the population activity can be detected only using the multivariate extension of the test. | |
dc.description.sponsorship | NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [K25 NS052422-02]; Max Planck SocietyMax Planck SocietyFoundation CELLEX; EUEuropean Commission [PHOCUS, 240763]; Stiftung Polytechnische Gesellschaft (Frankfurt am Main, Germany); Swiss National Science FoundationSwiss National Science Foundation (SNSF)European Commission [200020-117975]; NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKEUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [K25NS052422] Funding Source: NIH RePORTER; We are grateful to Sergio Neuenschwander and Bruss Lima for supplying the macaque V1 recordings discussed in section 3.3. This work was supported by NIH grant K25 NS052422-02, the Max Planck Society, and EU Grant PHOCUS, 240763, FP7-ICT-2009-C. F.G. acknowledges partial support by the Stiftung Polytechnische Gesellschaft (Frankfurt am Main, Germany) and support by the Swiss National Science Foundation under grant number 200020-117975. | |
dc.language.iso | en | |
dc.publisher | MIT PRESS | |
dc.relation.ispartof | NEURAL COMPUTATION | |
dc.subject | Computer Science | |
dc.subject | Computer Science, Artificial Intelligence | |
dc.subject | CONNECTIVITY | |
dc.subject | ENSEMBLES | |
dc.subject | EXCESS | |
dc.subject | FIELD | |
dc.subject | FRAMEWORK | |
dc.subject | GENERATION | |
dc.subject | INFORMATION | |
dc.subject | Neurosciences | |
dc.subject | Neurosciences & Neurology | |
dc.subject | SPIKE TRAINS | |
dc.subject | STATISTICAL-MODELS | |
dc.subject | TRIAL | |
dc.title | Applying the Multivariate Time-Rescaling Theorem to Neural Population Models | |
dc.type | journal article | |
dc.identifier.doi | 10.1162/NECO_a_00126 | |
dc.identifier.isi | ISI:000290300400002 | |
dc.description.volume | 23 | |
dc.description.issue | 6 | |
dc.description.startpage | 1452 | |
dc.description.endpage | 1483 | |
dc.contributor.orcid | 0000-0002-3416-2652 | |
dc.contributor.researcherid | M-1813-2014 | |
dc.identifier.eissn | 1530888X | |
dc.publisher.place | ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA | |
dcterms.isPartOf.abbreviation | Neural Comput. | |
dcterms.oaStatus | Green Accepted, Green Published, Green Submitted | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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