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dc.contributor.authorChen, Rong
dc.date.accessioned2021-01-13T19:41:24Z
dc.date.available2021-01-13T19:41:24Z
dc.date.issued2021-01-04
dc.identifier.urihttp://hdl.handle.net/10713/14354
dc.description.abstractInteractions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters. © 2021, The Author(s).en_US
dc.description.urihttps://doi.org/10.1007/s12021-020-09505-4en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofNeuroinformaticsen_US
dc.subjectCausal discoveryen_US
dc.subjectClusteringen_US
dc.subjectDynamic Bayesian networken_US
dc.subjectNeuroimagingen_US
dc.titleCausal Network Inference for Neural Ensemble Activity.en_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s12021-020-09505-4
dc.identifier.pmid33393054
dc.source.countryUnited States
dc.source.countryUnited States


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