Pattern grouping algorithm and de-convolution filtering of non-stationary correlated Poisson processes

The existence of precise temporal relations in sequences of spike intervals , referred to as "spatiotemporal patterns", is suggested by brain theories that emphasize the role of temporal coding. A pattern grouping algorithm wa s designed to identify and to evaluate the statistical significance of such patterns, particularly for data generated according to stationary Poisson processes. The experimental time series, however, can be characterized by c onsiderable deviations from independent stationary Poisson processes. This article describes a filtering method that de-convolute time series accordin g to their correlation functions and makes possible an application of the p attern grouping algorithm for such data too. (C) 2001 Elsevier Science B.V. All rights reserved.