Computer Method for Identifying Bursts in Trains of Spikes

Publisher Summary This chapter describes an empirical method for the detection and the identification of bursts. An algorithm based on the analysis of histograms of interspike intervals (ISIs) from spike train data can detect and locate bursts with good reliability and few assumptions about data distribution. The chapter discusses ineffective methods for the detection and identification of bursts. The most successful method found for identifying bursts in a train of spikes is based on an analysis of the distribution of interspike intervals of the data set. In any spike train containing bursts, there will be two populations of ISIs: one population will consist of the relatively short intervals found between spikes within bursts, and the other population will consist of the relatively long intervals found between spikes that are not part of any burst or between bursts. In a method presented in the chapter, the distribution of ISIs was analyzed to locate the transition between these two populations.

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