Identification of synaptic connections in neural ensembles by graphical models

A method for the identification of direct synaptic connections in a larger neural net is presented. It is based on a conditional correlation graph for multivariate point processes. The connections are identified via the partial spectral coherence of two neurons, given all others. It is shown how these coherences can be calculated by inversion of the spectral density matrix. In simulations with GENESIS, we discuss the relevance of the method for identifying different neural ensembles including an excitatory feedback loop and networks with lateral inhibitions.