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dc.contributor.authorPatnaik, Debprakashen
dc.contributor.authorLaxman, Srivatsanen
dc.contributor.authorRamakrishnan, Narenen
dc.date.accessioned2012-08-24T11:19:09Zen
dc.date.available2012-08-24T11:19:09Zen
dc.date.issued2010-07-20en
dc.identifier.citationBMC Neuroscience. 2010 Jul 20;11(Suppl 1):P171en
dc.identifier.urihttp://hdl.handle.net/10919/18828en
dc.description.abstractNeuronal circuits or cell assemblies carry out brain function through complex coordinated firing patterns [1]. Inferring topology of neuronal circuits from simultaneously recorded spike train data is a challenging problem in neuroscience. In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons [2]. The emphasis on excitatory networks allows us to learn connectivity models by exploiting fast data mining algorithms. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and can be summarized into a dynamic Bayesian network (DBN).en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleLearning probabilistic models of connectivity from multiple spike train dataen
dc.typePosteren
dc.date.updated2012-08-24T11:19:09Zen
dc.description.versionPublished versionen
dc.rights.holderDebprakash Patnaik et al.; licensee BioMed Central Ltd.en
dc.contributor.departmentComputer Scienceen
dc.title.serialBMC Neuroscienceen
dc.identifier.doihttps://doi.org/10.1186/1471-2202-11-S1-P171en
dc.type.dcmitypeTexten


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International