Missing mass approximations for the partition function of stimulus driven lsing models

DC FieldValueLanguage
dc.contributor.authorHaslinger, Robert
dc.contributor.authorBa, Demba
dc.contributor.authorGaluske, Ralf
dc.contributor.authorWilliams, Ziv
dc.contributor.authorPipa, Gordon
dc.date.accessioned2021-12-23T16:21:00Z-
dc.date.available2021-12-23T16:21:00Z-
dc.date.issued2013
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/13684-
dc.description.abstractlsing models are routinely used to quantify the second order, functional structure of neural populations. With some recent exceptions, they generally do not include the influence of time varying stimulus drive. Yet if the dynamics of network function are to be understood, time varying stimuli must be taken into account. Inclusion of stimulus drive carries a heavy computational burden because the partition function becomes stimulus dependent and must be separately calculated for all unique stimuli observed. This potentially increases computation time by the length of the data set. Here we present an extremely fast, yet simply implemented, method for approximating the stimulus dependent partition function in minutes or seconds. Noting that the most probable spike patterns (which are few) occur in the training data, we sum partition function terms corresponding to those patterns explicitly. We then approximate the sum over the remaining patterns (which are improbable, but many) by casting it in terms of the stimulus modulated missing mass (total stimulus dependent probability of all patterns not observed in the training data). We use a product of conditioned logistic regression models to approximate the stimulus modulated missing mass. This method has complexity of roughly O(LNNpat) where is L the data length, N the number of neurons and N-pat the number of unique patterns in the data, contrasting with the O(L2(N)) complexity of alternate methods. Using multiple unit recordings from rat hippocampus, macaque DLPFC and cat Area 18 we demonstrate our method requires orders of magnitude less computation time than Monte Carlo methods and can approximate the stimulus driven partition function more accurately than either Monte Carlo methods or deterministic approximations. This advance allows stimuli to be easily included in lsing models making them suitable for studying population based stimulus encoding.
dc.description.sponsorshipNIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [K25 N5052422-02, 5R01-HD059852]; PECASEUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA; Whitehall Foundation; The authors would like to thank Emery Brown for helpful conversations regarding the research presented in this paper and also the use of his rat hippocampal data. This work was supported by NIH grant K25 N5052422-02 (Robert Haslinger), the Max Planck-Gesellschaft (Ralf Galuske), and NIH grant 5R01-HD059852, PECASE and the Whitehall Foundation (Ziv Williams).
dc.language.isoen
dc.publisherFRONTIERS MEDIA SA
dc.relation.ispartofFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
dc.subjectENSEMBLE
dc.subjectHISTORY
dc.subjectlsing model
dc.subjectMathematical & Computational Biology
dc.subjectmultiple unit recordings
dc.subjectnetwork function
dc.subjectNeurosciences
dc.subjectNeurosciences & Neurology
dc.subjectpartition function
dc.subjectPATTERNS
dc.subjectpopulation codes
dc.subjectstimulus coding
dc.titleMissing mass approximations for the partition function of stimulus driven lsing models
dc.typejournal article
dc.identifier.doi10.3389/fncom.2013.00096
dc.identifier.isiISI:000322509300001
dc.description.volume7
dc.contributor.orcid0000-0002-3416-2652
dc.contributor.researcheridM-1813-2014
dc.identifier.eissn16625188
dc.publisher.placeAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
dcterms.isPartOf.abbreviationFront. Comput. Neurosci.
dcterms.oaStatusGreen Published, gold
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptidinstitute28-
crisitem.author.orcid0000-0002-3416-2652-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidPiGo340-
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