Analyzing multidimensional neural activity via CNN-UM

Abstract

In this paper we show that CNN-UM is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: the occurrences of different patterns are first counted, then the statistical significance of each occurrence frequency is calculated separately.