An efficient method for detecting connectivity in neural ensembles

Abstract

Modern technology is allowing researchers to collect data from neural ensembles with a large number of units, and the analysis of interaction between these units can be very time consuming. Estimation of pairwise connectivity is the most common method of determining the neural `network' but usually necessitates the production of numerous histograms for each pair considered. We present a method which will indicate which pairs in a network represent potential connections and thereby simplify the postexperimental analysis. The technique uses cross-interval information to create an n x n matrix which represents all possible connections in an n neuron ensemble and can be calculated recursively on-line. The performance of this technique is analyzed with respect to data size and strength of the connections. It is compared to 2 similar techniques that are also presented here, one in which perfect knowledge of the timing of the excitation is known, and one in which the timing can be bounded.