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The entorhinal grid map is discretized

Author

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  • Hanne Stensola

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

  • Tor Stensola

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

  • Trygve Solstad

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

  • Kristian Frøland

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

  • May-Britt Moser

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

  • Edvard I. Moser

    (Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway)

Abstract

The medial entorhinal cortex (MEC) is part of the brain’s circuit for dynamic representation of self-location. The metric of this representation is provided by grid cells, cells with spatial firing fields that tile environments in a periodic hexagonal pattern. Limited anatomical sampling has obscured whether the grid system operates as a unified system or a conglomerate of independent modules. Here we show with recordings from up to 186 grid cells in individual rats that grid cells cluster into a small number of layer-spanning anatomically overlapping modules with distinct scale, orientation, asymmetry and theta-frequency modulation. These modules can respond independently to changes in the geometry of the environment. The discrete topography of the grid-map, and the apparent autonomy of the modules, differ from the graded topography of maps for continuous variables in several sensory systems, raising the possibility that the modularity of the grid map is a product of local self-organizing network dynamics.

Suggested Citation

  • Hanne Stensola & Tor Stensola & Trygve Solstad & Kristian Frøland & May-Britt Moser & Edvard I. Moser, 2012. "The entorhinal grid map is discretized," Nature, Nature, vol. 492(7427), pages 72-78, December.
    • Handle: RePEc:nat:nature:v:492:y:2012:i:7427:d:10.1038_nature11649
    DOI: 10.1038/nature11649
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    Citations

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    Cited by:

    1. Torsten Neher & Amir Hossein Azizi & Sen Cheng, 2017. "From grid cells to place cells with realistic field sizes," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-27, July.
    2. Bailu Si & Sandro Romani & Misha Tsodyks, 2014. "Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-18, April.
    3. Kamiar Rahnama Rad & Arian Maleki, 2020. "A scalable estimate of the out‐of‐sample prediction error via approximate leave‐one‐out cross‐validation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 965-996, September.
    4. Taylor J. Malone & Nai-Wen Tien & Yan Ma & Lian Cui & Shangru Lyu & Garret Wang & Duc Nguyen & Kai Zhang & Maxym V. Myroshnychenko & Jean Tyan & Joshua A. Gordon & David A. Kupferschmidt & Yi Gu, 2024. "A consistent map in the medial entorhinal cortex supports spatial memory," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    5. Benjamin Dunn & Maria Mørreaunet & Yasser Roudi, 2015. "Correlations and Functional Connections in a Population of Grid Cells," PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-21, February.
    6. Noga Mosheiff & Haggai Agmon & Avraham Moriel & Yoram Burak, 2017. "An efficient coding theory for a dynamic trajectory predicts non-uniform allocation of entorhinal grid cells to modules," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    7. Tiziano D’Albis & Richard Kempter, 2017. "A single-cell spiking model for the origin of grid-cell patterns," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-41, October.
    8. Lajos Vágó & Balázs B Ujfalussy, 2018. "Robust and efficient coding with grid cells," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-28, January.
    9. Trygve Solstad & Hosam N Yousif & Terrence J Sejnowski, 2014. "Place Cell Rate Remapping by CA3 Recurrent Collaterals," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-10, June.
    10. Axel Kammerer & Christian Leibold, 2014. "Hippocampal Remapping Is Constrained by Sparseness rather than Capacity," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-12, December.
    11. Alexander Thomas Keinath, 2016. "The Preferred Directions of Conjunctive Grid X Head Direction Cells in the Medial Entorhinal Cortex Are Periodically Organized," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-11, March.
    12. Daniel Müller-Komorowska & Baris Kuru & Heinz Beck & Oliver Braganza, 2023. "Phase information is conserved in sparse, synchronous population-rate-codes via phase-to-rate recoding," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    13. Florian Raudies & Michael E Hasselmo, 2015. "Differences in Visual-Spatial Input May Underlie Different Compression Properties of Firing Fields for Grid Cell Modules in Medial Entorhinal Cortex," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-27, November.

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