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.
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M. M. M. is supported by grants from the Swiss National Science Foundation (grant 320030-169206), the Fondation Asile des aveugles, and a grantor advised by Carigest SA. B. F. is supported by the Fondation Asile des aveugles. F. A. acknowledges the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216 and the financial support of the AFOSR projects FA9550-17-1-0390 and BAA-AFRL-AFOSR-2016-0007 (European Office of Aerospace Research and Development), and the EU H2020-MSCA-RISE project NoMADS - DLV-777826. F. A. is also supported by the Italian Institute of Technology.
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Anselmi, F., Murray, M.M. & Franceschiello, B. A computational model for grid maps in neural populations. J Comput Neurosci 48, 149–159 (2020). https://doi.org/10.1007/s10827-020-00742-9
- Grid cells
- Computational model