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bioRxiv · 10.1101/2023.03.18.533177

Variational Log-Gaussian Point-Process Methods for Grid Cells

Rule, M. E.·Vayalambrone, P. C.·Krstulovic, M.·Bauza, M.·Krupic, J.·O'Leary, T.
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Abstract

We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes 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.

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Rule, M. E., Vayalambrone, P. C., Krstulovic, M., Bauza, M., Krupic, J., O'Leary, T.. 2023-03-18. Variational Log-Gaussian Point-Process Methods for Grid Cells. https://doi.org/10.1101/2023.03.18.533177

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