Causal neuronal networks provide functional signatures of stimulus encoding

Graphical models are powerful tools to infer statistical relationships between simultaneously observed random variables. Here, we used Dynamic Bayesian Networks (DBN) to infer causal relationships between simultaneously recorded neurons in the rat somatosensory (barrel) cortex in response to whisker stimulation. DBNs attempt to explain the activity of the observed neurons by searching for the best network connectivity that explains the observed data. The results demonstrate that the networks inferred for the same whisker are stable across multiple repeated trials. In contrast to networks obtained using classical cross-correlograms, DBN was able to discriminate between direct and indirect connectivity. We also found strong consistency between the inferred connections and the sequence of neural firing relative to the stimulus onset.