A computational model for grid maps in neural populations.

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Serval ID
serval:BIB_4AF9D4C11F3E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A computational model for grid maps in neural populations.
Journal
Journal of computational neuroscience
Author(s)
Anselmi F., Murray M.M., Franceschiello B.
ISSN
1573-6873 (Electronic)
ISSN-L
0929-5313
Publication state
Published
Issued date
05/2020
Peer-reviewed
Oui
Volume
48
Number
2
Pages
149-159
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain. In this work we introduce a novel theoretical and algorithmic framework able to explain the optimality of hexagonal grid-like response patterns. We show that this pattern is a result of minimal variance encoding of neurons together with maximal robustness to neurons' noise and minimal number of encoding neurons. The novelty lies in the formulation of the encoding problem considering neurons as an overcomplete basis (a frame) where the position information is encoded. Through the modern Frame Theory language, specifically that of tight and equiangular frames, we provide new insights about the optimality of hexagonal grid receptive fields. The proposed model is based on the well-accepted and tested hypothesis of Hebbian learning, providing a simplified cortical-based framework that does not require the presence of velocity-driven oscillations (oscillatory model) or translational symmetries in the synaptic connections (attractor model). We moreover demonstrate that the proposed encoding mechanism naturally explains axis alignment of neighbor grid cells and maps shifts, rotations and scaling of the stimuli onto the shape of grid cells' receptive fields, giving a straightforward explanation of the experimental evidence of grid cells remapping under transformations of environmental cues.
Keywords
Computational model, Grid cells, Hippocampus
Pubmed
Web of science
Create date
05/03/2020 15:07
Last modification date
20/07/2022 5:37
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