Michigan State University - Research Groups

A fundamental goal in systems neuroscience is to infer the
functional connectivity among neuronal elements coordinating
information processing in the brain. In this work, we investigate
the applicability of Dynamic Bayesian Networks (DBN) in
inferring the structure of cortical networks from the observed spike
trains. DBNs have unique features that make them capable of
detecting causal relationships between spike trains such as
modeling time-dependent relationships, detecting non-linear
interactions and inferring connectivity between neurons from the
observed ensemble activity. A probabilistic point process model
was used to assess the performance under systematic variations of
the model parameters. Results demonstrate the utility of DBN in
inferring functional connectivity in cortical network models.