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The current state-of-the-art in neurophysiological data collection allows forsimultaneous recording of tens to hundreds of neurons, for which pointprocesses are an appropriate statistical modelling framework. However, existingpoint process models lack multivariate generalizations which are both flexibleand computationally tractable. This paper introduces a multivariategeneralization of the Skellam process with resetting (SPR), a point processtailored to model individual neural spike trains. The multivariate SPR (MSPR)is biologically justified as it mimics the process of neural integration. Itsflexible dependence structure and a fast parameter estimation method make itwell-suited for the analysis of simultaneously recorded spike trains frommultiple neurons. The strengths and weaknesses of the MSPR are demonstratedthrough simulation and analysis of experimental data.

A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains