Bayesian inference of grid cell firing patterns using Poisson point process models with latent oscillatory Gaussian random fields

TL;DR

This paper introduces a Bayesian Poisson point process model with latent Gaussian fields to analyze grid cell firing patterns, capturing continuous spatial and directional influences more effectively than previous methods.

Contribution

The paper develops a likelihood-based Bayesian framework using Gaussian random fields to model and infer neural firing patterns with covariate effects, improving over existing discretized approaches.

Findings

Evidence for head direction effects on grid firing

Model captures inhomogeneous spatial patterns

Quantifies uncertainty in neural activity estimates

Abstract

Questions about information encoded by the brain demand statistical frameworks for inferring relationships between neural firing and features of the world. The landmark discovery of grid cells demonstrates that neurons can represent spatial information through regularly repeating firing fields. However, the influence of covariates may be masked in current statistical models of grid cell activity, which by employing approaches such as discretizing, aggregating and smoothing, are computationally inefficient and do not account for the continuous nature of the physical world. These limitations motivated us to develop likelihood-based procedures for modelling and estimating the firing activity of grid cells conditionally on biologically relevant covariates. Our approach models firing activity using Poisson point processes with latent Gaussian effects, which accommodate persistent inhomogeneous spatial-directional patterns and overdispersion. Inference is performed in a fully Bayesian manner, which allows us to quantify uncertainty. Applying these methods to experimental data, we provide evidence for temporal and local head direction effects on grid firing. Our approaches offer a novel and principled framework for analysis of neural representations of space.