Papers › Bayesian inference of grid cell firing patterns using Poisson point process models...
Bayesian inference of grid cell firing patterns using Poisson point process models with latent oscillatory Gaussian random fields
Ioannis Papastathopoulos, Graeme Auld, Finn Lindgren, Klára Zsófia Gerlei, Matthew F. Nolan
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections