Variational bayesian inference for point process generalized linear models in neural spike trains analysis

Abstract

Point process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation. Variational Bayesian (VB) methods provide a computationally appealing means to infer the posterior density of unknown parameters, in which conjugate priors are designed for the regression coefficients in logistic and Poisson regression. In this paper, we develop and apply VB inference for point process GLMs in neural spike train analysis. The hierarchical Bayesian framework allows us to tackle the variable selection problem. We assess and validate our methods with ensemble neuronal recordings from rat's hippocampal place cells and entorhinal cortical cells during foraging in an open field environment.