Rule, Michael Everett;
Chaudhuri‐Vayalambrone, Prannoy;
Krstulovic, Marino;
Bauza, Marius;
Krupic, Julija;
O'Leary, Timothy;
(2023)
Variational log-Gaussian point-process methods for grid cells.
Hippocampus
10.1002/hipo.23577.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Variational log-Gaussian point-process methods for grid cells |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/hipo.23577 |
| Publisher version: | https://doi.org/10.1002/hipo.23577 |
| Language: | English |
| Additional information: | © 2023 The Authors. Hippocampus published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Gaussian process, grid cells, point process, spatial statistics, variational Bayesian inference |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > The Sainsbury Wellcome Centre |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10178120 |
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