SEQUENTIAL INTERVAL HISTOGRAM ANALYSIS OF NONSTATIONARY NEURONAL SPIKE TRAINS

The spike interval histogram, a commonly used tool for the analysis of neuronal spike trains, was evaluated as a statistical estimator of the probability density function (PDF) of interspike intervals. Using a mean square error criterion, it was concluded that a Parzen convolution estimate of the PDF is superior to the conventional histogram procedure. The Parzen estimate using a Gaussian weighting function reduced the number of intervals required to achieve a given error by a factor of 5-10. The Parzen estimation procedure was implemented in the sequential interval histogram (SQIH) procedure for analysis of non-stationary spike trains. Segments of the spike train were defined using a moving window, and the PDF for each segment was estimated sequentially. The procedure that was most practical was interactive with the user and utilized the theoretical results of the error analysis as guidelines for the evolution of an estimation strategy. The SQIH procedure appears useful both as a criterion for stationarity and as a means to characterize non-stationary activity.