A computational model for grid maps in neural populations
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 he...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131863 |
| _version_ | 1811078321109204992 |
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| author | Anselmi, Fabio Murray, Micah M Franceschiello, Benedetta |
| author_facet | Anselmi, Fabio Murray, Micah M Franceschiello, Benedetta |
| author_sort | Anselmi, Fabio |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:57:45Z |
| format | Article |
| id | mit-1721.1/131863 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:57:45Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1318632021-09-21T03:46:02Z A computational model for grid maps in neural populations Anselmi, Fabio Murray, Micah M Franceschiello, Benedetta 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. 2021-09-20T17:30:42Z 2021-09-20T17:30:42Z 2020-03-03 2020-09-24T21:34:23Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131863 en https://doi.org/10.1007/s10827-020-00742-9 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Anselmi, Fabio Murray, Micah M Franceschiello, Benedetta A computational model for grid maps in neural populations |
| title | A computational model for grid maps in neural populations |
| title_full | A computational model for grid maps in neural populations |
| title_fullStr | A computational model for grid maps in neural populations |
| title_full_unstemmed | A computational model for grid maps in neural populations |
| title_short | A computational model for grid maps in neural populations |
| title_sort | computational model for grid maps in neural populations |
| url | https://hdl.handle.net/1721.1/131863 |
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