Variational log‐ Gaussian point‐process methods for grid cells
Michael E. Rule, Prannoy Chaudhuri‐Vayalambrone, Marino Krstulović, Marius Bauža, Julija Krupic, Timothy S. O’Leary · Hippocampus · 2023
We present practical solutions to applying Gaussian-process (GP) methods to calculate spatial statistics for grid cells in large environments. GPs are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low-rank spatial frequency subspace for further acceleration, and demonstrate these methods on experimental data.