IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004596.html
   My bibliography  Save this article

Differences in Visual-Spatial Input May Underlie Different Compression Properties of Firing Fields for Grid Cell Modules in Medial Entorhinal Cortex

Author

Listed:
  • Florian Raudies
  • Michael E Hasselmo

Abstract

Firing fields of grid cells in medial entorhinal cortex show compression or expansion after manipulations of the location of environmental barriers. This compression or expansion could be selective for individual grid cell modules with particular properties of spatial scaling. We present a model for differences in the response of modules to barrier location that arise from different mechanisms for the influence of visual features on the computation of location that drives grid cell firing patterns. These differences could arise from differences in the position of visual features within the visual field. When location was computed from the movement of visual features on the ground plane (optic flow) in the ventral visual field, this resulted in grid cell spatial firing that was not sensitive to barrier location in modules modeled with small spacing between grid cell firing fields. In contrast, when location was computed from static visual features on walls of barriers, i.e. in the more dorsal visual field, this resulted in grid cell spatial firing that compressed or expanded based on the barrier locations in modules modeled with large spacing between grid cell firing fields. This indicates that different grid cell modules might have differential properties for computing location based on visual cues, or the spatial radius of sensitivity to visual cues might differ between modules.Author Summary: How do we navigate from one location to another and how do we represent space to accomplish this task? Researchers have collected data from the entorhinal cortex in rodents to answer these questions, finding grid cells that fire whenever a rodent traverses through an array of locations falling on the vertices of tightly packed equilateral triangles. Grid cells with large spacing (large side lengths of the triangles between firing fields) are distorted when the environment is manipulated, e.g. by pushing walls or inserting walls in a box. In contrast, grid cells of small spacing remain largely unaffected by such manipulations. We present a computational model to explain this behavior of grid cells. In our model information about the motion of features on the ground, which are unaffected by wall manipulations, drive grid cells with small spacing between firing fields, while static features like landmarks, which are affected by wall manipulations, drive grid cells with large spacing between firing fields. These differences could correspond to different positions within the visual field of the animal. This model puts forth a testable hypothesis about the type of features that drive grid cells of different spacing.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1004596
    DOI: 10.1371/journal.pcbi.1004596
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004596
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004596&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004596?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Florian Raudies & Michael E Hasselmo, 2012. "Modeling Boundary Vector Cell Firing Given Optic Flow as a Cue," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-17, June.
    2. 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.
    3. Torkel Hafting & Marianne Fyhn & Sturla Molden & May-Britt Moser & Edvard I. Moser, 2005. "Microstructure of a spatial map in the entorhinal cortex," Nature, Nature, vol. 436(7052), pages 801-806, August.
    4. Alexei V. Egorov & Bassam N. Hamam & Erik Fransén & Michael E. Hasselmo & Angel A. Alonso, 2002. "Graded persistent activity in entorhinal cortex neurons," Nature, Nature, vol. 420(6912), pages 173-178, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiming Shao & Ligu Chen & Xiaowan Li & Miao Li & Hui Cui & Xiaoyue Li & Xinran Zhao & Yuying Shi & Qiang Sun & Kaiyue Yan & Guangfu Wang, 2024. "A non-canonical visual cortical-entorhinal pathway contributes to spatial navigation," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Krishna Choudhary & Sven Berberich & Thomas T. G. Hahn & James M. McFarland & Mayank R. Mehta, 2024. "Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    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. 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.
    9. 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.
    10. 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.
    11. Erik Hermansen & David A. Klindt & Benjamin A. Dunn, 2024. "Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    12. Isabella C. Wagner & Luise P. Graichen & Boryana Todorova & Andre Lüttig & David B. Omer & Matthias Stangl & Claus Lamm, 2023. "Entorhinal grid-like codes and time-locked network dynamics track others navigating through space," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    13. Kyerl Park & Yoonsoo Yeo & Kisung Shin & Jeehyun Kwag, 2024. "Egocentric neural representation of geometric vertex in the retrosplenial cortex," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    14. Balázs Ujfalussy & Tamás Kiss & Péter Érdi, 2009. "Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-16, September.
    15. Louis-Emmanuel Martinet & Denis Sheynikhovich & Karim Benchenane & Angelo Arleo, 2011. "Spatial Learning and Action Planning in a Prefrontal Cortical Network Model," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-21, May.
    16. Florian Raudies & Michael E Hasselmo, 2012. "Modeling Boundary Vector Cell Firing Given Optic Flow as a Cue," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-17, June.
    17. Avgar, Tal & Deardon, Rob & Fryxell, John M., 2013. "An empirically parameterized individual based model of animal movement, perception, and memory," Ecological Modelling, Elsevier, vol. 251(C), pages 158-172.
    18. Sabrina L. L. Maoz & Matthias Stangl & Uros Topalovic & Daniel Batista & Sonja Hiller & Zahra M. Aghajan & Barbara Knowlton & John Stern & Jean-Philippe Langevin & Itzhak Fried & Dawn Eliashiv & Nanth, 2023. "Dynamic neural representations of memory and space during human ambulatory navigation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    19. Fabian Kessler & Julia Frankenstein & Constantin A. Rothkopf, 2024. "Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    20. Federica Sigismondi & Yangwen Xu & Mattia Silvestri & Roberto Bottini, 2024. "Altered grid-like coding in early blind people," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1004596. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.