Neuronal spike train analysis using Gaussian process models

Abstract

Statistical analysis of simultaneously recorded neurons plays an important role in understanding complex behaviors, decision making process, and neurophysiological disorders. Here, we briefly review several statistical methods specifically developed for analysis of neuronal spike trains. We then focus on application of Gaussian process models for estimating time-varying firing rates of neurons and show how this approach can be extended for modeling synchrony among multiple neurons. We finish this chapter by discussing some possible future directions where more advanced nonparametric Bayesian methods can be utilized to improve existing models.