Literature DB >> 26335200

A principle of economy predicts the functional architecture of grid cells.

Xue-Xin Wei1, Jason Prentice2, Vijay Balasubramanian3,4.   

Abstract

Grid cells in the brain respond when an animal occupies a periodic lattice of 'grid fields' during navigation. Grids are organized in modules with different periodicity. We propose that the grid system implements a hierarchical code for space that economizes the number of neurons required to encode location with a given resolution across a range equal to the largest period. This theory predicts that (i) grid fields should lie on a triangular lattice, (ii) grid scales should follow a geometric progression, (iii) the ratio between adjacent grid scales should be √e for idealized neurons, and lie between 1.4 and 1.7 for realistic neurons, (iv) the scale ratio should vary modestly within and between animals. These results explain the measured grid structure in rodents. We also predict optimal organization in one and three dimensions, the number of modules, and, with added assumptions, the ratio between grid periods and field widths.

Entities:  

Keywords:  efficient coding; grid cells; neuroscience; rat; spatial cognition; theoretical neuroscience

Mesh:

Year:  2015        PMID: 26335200      PMCID: PMC4616244          DOI: 10.7554/eLife.08362

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  52 in total

1.  Grid cells without theta oscillations in the entorhinal cortex of bats.

Authors:  Michael M Yartsev; Menno P Witter; Nachum Ulanovsky
Journal:  Nature       Date:  2011-11-02       Impact factor: 49.962

Review 2.  A manifold of spatial maps in the brain.

Authors:  Dori Derdikman; Edvard I Moser
Journal:  Trends Cogn Sci       Date:  2010-10-15       Impact factor: 20.229

3.  Microstructure of a spatial map in the entorhinal cortex.

Authors:  Torkel Hafting; Marianne Fyhn; Sturla Molden; May-Britt Moser; Edvard I Moser
Journal:  Nature       Date:  2005-06-19       Impact factor: 49.962

4.  Conjunctive representation of position, direction, and velocity in entorhinal cortex.

Authors:  Francesca Sargolini; Marianne Fyhn; Torkel Hafting; Bruce L McNaughton; Menno P Witter; May-Britt Moser; Edvard I Moser
Journal:  Science       Date:  2006-05-05       Impact factor: 47.728

5.  Fragmentation of grid cell maps in a multicompartment environment.

Authors:  Dori Derdikman; Jonathan R Whitlock; Albert Tsao; Marianne Fyhn; Torkel Hafting; May-Britt Moser; Edvard I Moser
Journal:  Nat Neurosci       Date:  2009-09-13       Impact factor: 24.884

6.  Grid cells generate an analog error-correcting code for singularly precise neural computation.

Authors:  Sameet Sreenivasan; Ila Fiete
Journal:  Nat Neurosci       Date:  2011-09-11       Impact factor: 24.884

Review 7.  Computational models of grid cells.

Authors:  Lisa M Giocomo; May-Britt Moser; Edvard I Moser
Journal:  Neuron       Date:  2011-08-25       Impact factor: 17.173

8.  The input-output transformation of the hippocampal granule cells: from grid cells to place fields.

Authors:  Licurgo de Almeida; Marco Idiart; John E Lisman
Journal:  J Neurosci       Date:  2009-06-10       Impact factor: 6.167

9.  Evidence for grid cells in a human memory network.

Authors:  Christian F Doeller; Caswell Barry; Neil Burgess
Journal:  Nature       Date:  2010-01-20       Impact factor: 49.962

10.  Accurate path integration in continuous attractor network models of grid cells.

Authors:  Yoram Burak; Ila R Fiete
Journal:  PLoS Comput Biol       Date:  2009-02-20       Impact factor: 4.475

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  12 in total

1.  Low-dimensional dynamics of structured random networks.

Authors:  Johnatan Aljadeff; David Renfrew; Marina Vegué; Tatyana O Sharpee
Journal:  Phys Rev E       Date:  2016-02-05       Impact factor: 2.529

Review 2.  Dynamical self-organization and efficient representation of space by grid cells.

Authors:  Ronald W DiTullio; Vijay Balasubramanian
Journal:  Curr Opin Neurobiol       Date:  2021-11-30       Impact factor: 6.627

3.  Multiple bumps can enhance robustness to noise in continuous attractor networks.

Authors:  Raymond Wang; Louis Kang
Journal:  PLoS Comput Biol       Date:  2022-10-10       Impact factor: 4.779

4.  A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial remapping and grid cell field-to-field variability.

Authors:  Haggai Agmon; Yoram Burak
Journal:  Elife       Date:  2020-08-11       Impact factor: 8.140

5.  Robust and efficient coding with grid cells.

Authors:  Lajos Vágó; Balázs B Ujfalussy
Journal:  PLoS Comput Biol       Date:  2018-01-08       Impact factor: 4.475

6.  An efficient coding theory for a dynamic trajectory predicts non-uniform allocation of entorhinal grid cells to modules.

Authors:  Noga Mosheiff; Haggai Agmon; Avraham Moriel; Yoram Burak
Journal:  PLoS Comput Biol       Date:  2017-06-19       Impact factor: 4.475

7.  Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor.

Authors:  Noga Mosheiff; Yoram Burak
Journal:  Elife       Date:  2019-08-30       Impact factor: 8.140

8.  Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding.

Authors:  Yanbo Lian; Anthony N Burkitt
Journal:  eNeuro       Date:  2021-07-08

9.  Environmental deformations dynamically shift the grid cell spatial metric.

Authors:  Alexandra T Keinath; Russell A Epstein; Vijay Balasubramanian
Journal:  Elife       Date:  2018-10-22       Impact factor: 8.140

10.  A geometric attractor mechanism for self-organization of entorhinal grid modules.

Authors:  Louis Kang; Vijay Balasubramanian
Journal:  Elife       Date:  2019-08-02       Impact factor: 8.140

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