The problem of perfect predictors in statistical spike train models

Generalized Linear Models (GLMs) have been used extensively in statistical models of spike train data. However, the \cm{maximum likelihood estimates of the model parameters and their uncertainty}, can \cm{be challenging to compute} in situations where response and non-response can be separated by a single predictor or a linear combination of multiple predictors. Such situations are likely to arise in many neural systems due to properties such as refractoriness and incomplete sampling of the signals that influence spiking. In this paper, we describe multiple classes of approaches to address this problem: \cm{using an optimization algorithm with a fixed iteration limit}, computing the maximum likelihood solution in the limit, Bayesian estimation, regularization, change of basis, and modifying the search parameters. We demonstrate a specific application of each of these methods to spiking data from rat somatosensory cortex and discuss the advantages and disadvantages of each. We also provide an example of a roadmap for selecting a method based on the problem's particular analysis issues and scientific goals.