Classification of neuronal activities from tetrode recordings using independent component analysis

Classifying spike shapes in multi-unit recordings has been important to extract single neuronal activities from nervous tissue. Although several methods for this purpose have been developed, most of them have had limitations in their ability to decompose single unit activities. When more than two neurons generate action potentials simultaneously, it is difficult to identify the spikes because of the overlap of the spike waveforms. In this paper, we suggest a procedure that solves this problem using independent component analysis. By testing for the refractory period of spikes in each independent component, the proposed procedure is efficient for the decomposition of neuronal activities. (C) 2002 Elsevier Science B.V. All rights reserved.