title: Reconstructing functional neuronal circuits using dynamic Bayesian networks.
abstract: Identifying functional connectivity from simultaneously recorded spike trains is important in understanding how the brain processes information and instructs the body to perform complex tasks. We investigate the applicability of dynamic Bayesian networks (DBN) to infer the structure of neural circuits from observed spike trains. A probabilistic point process model was used to assess the performance. Results confirm the utility of DBNs in inferring functional connectivity as well as directions of signal flow in cortical network models. Results also demonstrate that DBN outperforms the Granger causality when applied to populations with highly non-linear synaptic integration mechanisms.
authors.affiliation: ECE Dept. at Michigan State University, East Lansing, MI 48824, USA. eldawlat@msu.edu
authors.forename: Seif
authors.initials: S
authors.lastname: Eldawlatly
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhou
authors.forename: Rong
authors.initials: R
authors.lastname: Jin
authors.forename: Karim
authors.initials: K
authors.lastname: Oweiss
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008953:Models, Anatomic; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*; D009474:Neurons / Q000166:cytology* / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*