IDENTIFICATION OF PATTERNS OF NEURONAL CONNECTIVITY-PARTIAL SPECTRA, PARTIAL COHERENCE, AND NEURONAL INTERACTIONS

The cross-correlation histogram has provided the primary tool for infe rring the structure of common inputs to pairs of neurones. While this technique has produced useful results it not clear how it may be exten ded to complex networks. In this report we introduce a linear model fo r point process systems. The finite Fourier transform of this model le ads to a regression type analysis of the relations between spike train s. An advantage of this approach is that the full range of techniques for multivariate regression analyses becomes available for spike train analysis. The two main parameters used for the identification of neur al networks are the coherence and partial coherences. The coherence de fines a bounded measure of association between two spike trains and pl ays the role of a squared correlation coefficient defined at each freq uency lambda. The partial coherences, analogous to the partial correla tions of multiple regression analysis, allow an assessment of how any number of putative input processes may influence the relation between any two output processes. In many cases analytic solutions may be foun d for coherences and partial coherences for simple neural networks, an d in combination with simulations may be used to test hypotheses conce rning proposed networks inferred from spike train analyses. (C) 1998 E lsevier Science B.V. All rights reserved.