Linking non-binned spike train kernels to several existing spike train metrics

Authors:
Benjamin Schrauwen;Jan Van Campenhout
Affiliations:
Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium
Venue:
Neurocomputing
Year:
2007

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Abstract

This work presents three kernel functions that can be used as inner product operators on non-binned spike trains, allowing the use of state-of-the-art classification techniques. One of the main advantages is that this approach does not require the spike trains to be binned. Thus a high temporal resolution is preserved which is needed when temporal coding is used. The kernels are closely related to several recent and often-used spike train metrics which take into account the biological variability of spike trains. It follows that the different existing metrics are unified by the spike train kernels presented. As a test of the classification potential of the new kernel functions, a jittered spike train template classification problem is solved.