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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.