Reconstructing functional neuronal circuits using dynamic Bayesian networks
Eldawlatly, Seif; Zhou, Yang; Jin, Rong; Oweiss, Karim
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Volume , Issue , 20-25 Aug. 2008 Page(s):5531 - 5534
Digital Object Identifier   10.1109/IEMBS.2008.4650467
Summary: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.

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