Testing the Odds of Inherent versus Observed Over-dispersion in Neural Spike Counts Odds of Inherent versus Observed Over-dispersion

TL;DR

This paper evaluates the prevalence of over-dispersion in neural spike counts, introduces a statistical test to distinguish inherent over-dispersion from sampling effects, and demonstrates improved decoding accuracy using the Negative-Binomial model.

Contribution

It introduces a statistical method to assess over-dispersion significance and compares the Negative-Binomial model to the Poisson model in neural data analysis.

Findings

Over-dispersion is common in neural spike counts.

The proposed test quantifies the likelihood of inherent over-dispersion.

Accounting for over-dispersion improves decoding performance.

Abstract

The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an " over-dispersion " effect: Responses that exhibit more inter-trial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify over-dispersion is the Negative-Binomial distribution model. This model is well-studied and widely used but has only recently been applied to neuroscience. In this paper, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that over-dispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological tests along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. We show that the decoding accuracy is improved when accounting for over-dispersion, especially under the hypothesis of tuned over-dispersion.