An efficient method for detecting connectivity in neural ensembles

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.