Network Dynamics and Correlated Spikes

Oscillatory neuronal responses have been suspected to reveal assembly formation via synchronization. We analyse these data in terms of a hidden-state model, the parameters of which are estimated directly from experimental spike trains. The model characterizes the excitation dynamics of the network which is reflected by the stochastic activity of individual observed neurons. Our approach reproduces the spike dynamics observed in cat visual cortex and predicts the cross-correlations of units which belong to the same assembly.