Algorithms for computing spike time distance and point process prototypes with application to feline neuronal responses to acoustic stimuli

The spike-time distance metric and the prototype of a collection of point patterns can be used to provide a metric description of repeated observations of point processes. Due to algorithmic limitations in computing spike-time distance, previous work in determining approximations of the prototype has largely been limited to single dimensional or small data sets. We develop new algorithms for each of these methods that are suitable for larger data sets. The first, an incremental matching algorithm, is a method to compute spike-time distance. The second algorithm involves the use of kernel smoothing for prototype construction. These methods readily extend to multiple dimensions and to marked point processes. We use a combination of these approaches to analyze neuronal spike data of cats in different behavioral states and across animals, and we compare our results to previous time series analyses that used averaged frequency histograms. Evidence is found for short latency responses to the acoustic CS that were sensitive to behavioral state.

► We develop an algorithm for computing spike-time distance in multiple dimensions. ► We also develop a method to estimate point pattern prototypes in multiple dimensions. ► The methods are applied to spike trains of feline responses to an acoustic stimulus. ► We find evidence that short latency responses were sensitive to behavioral state. ► These differences do not appear to be a byproduct of variation between animals.