A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains

TL;DR

This paper introduces a multivariate extension of the Skellam process with resetting (MSPR) for modeling simultaneous neural spike trains, providing a flexible, biologically justified, and computationally efficient framework for analyzing multi-neuron data.

Contribution

It presents the MSPR model, a novel multivariate point process that captures neural dependencies and is computationally tractable, advancing neurophysiological data analysis.

Findings

MSPR effectively models neural spike train dependencies.

Simulation results validate the model's flexibility and accuracy.

Application to experimental data demonstrates practical utility.

Abstract

The current state-of-the-art in neurophysiological data collection allows for simultaneous recording of tens to hundreds of neurons, for which point processes are an appropriate statistical modelling framework. However, existing point process models lack multivariate generalizations which are both flexible and computationally tractable. This paper introduces a multivariate generalization of the Skellam process with resetting (SPR), a point process tailored to model individual neural spike trains. The multivariate SPR (MSPR) is biologically justified as it mimics the process of neural integration. Its flexible dependence structure and a fast parameter estimation method make it well-suited for the analysis of simultaneously recorded spike trains from multiple neurons. The strengths and weaknesses of the MSPR are demonstrated through simulation and analysis of experimental data.