Reproducing kernel Hilbert spaces for spike train analysis

This paper introduces a generalized cross-correlation (GCC) measure for spike train analysis derived from reproducing kernel Hilbert spaces (RKHS) theory. An estimator for GCC is derived that does not depend on binning or a specific kernel and it operates directly and efficiently on spike times. For instantaneous analysis as required for real-time use, an instantaneous estimator is proposed and proved to yield the GCC on average. We finalize with two experiments illustrating the usefulness of the techniques derived.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: António R. C. Paiva, Il Park, Jose C. Principe
• Keywords: Generalized Cross-correlation, ICASSP 2008, Reproducing Kernel Hilbert Spaces, Signal Processing, Spike Train Analysis