IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0060599.html
   My bibliography  Save this article

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

Author

Listed:
  • Praveen K Pilly
  • Stephen Grossberg

Abstract

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.

Suggested Citation

  • Praveen K Pilly & Stephen Grossberg, 2013. "Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-22, April.
  • Handle: RePEc:plo:pone00:0060599
    DOI: 10.1371/journal.pone.0060599
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0060599
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0060599&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0060599?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. M. R. Mehta & A. K. Lee & M. A. Wilson, 2002. "Role of experience and oscillations in transforming a rate code into a temporal code," Nature, Nature, vol. 417(6890), pages 741-746, June.
    2. Michael M. Yartsev & Menno P. Witter & Nachum Ulanovsky, 2011. "Grid cells without theta oscillations in the entorhinal cortex of bats," Nature, Nature, vol. 479(7371), pages 103-107, November.
    3. 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.
    4. John Huxter & Neil Burgess & John O'Keefe, 2003. "Independent rate and temporal coding in hippocampal pyramidal cells," Nature, Nature, vol. 425(6960), pages 828-832, October.
    5. 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.
    6. Sébastien Royer & Denis Paré, 2003. "Conservation of total synaptic weight through balanced synaptic depression and potentiation," Nature, Nature, vol. 422(6931), pages 518-522, April.
    7. Stephen Grossberg & Praveen K Pilly, 2012. "How Entorhinal Grid Cells May Learn Multiple Spatial Scales from a Dorsoventral Gradient of Cell Response Rates in a Self-organizing Map," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-31, October.
    8. Torkel Hafting & Marianne Fyhn & Tora Bonnevie & May-Britt Moser & Edvard I. Moser, 2008. "Hippocampus-independent phase precession in entorhinal grid cells," Nature, Nature, vol. 453(7199), pages 1248-1252, June.
    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. 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.
    2. Davide Spalla & Alessandro Treves & Charlotte N. Boccara, 2022. "Angular and linear speed cells in the parahippocampal circuits," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Eric Reifenstein & Martin Stemmler & Andreas V M Herz & Richard Kempter & Susanne Schreiber, 2014. "Movement Dependence and Layer Specificity of Entorhinal Phase Precession in Two-Dimensional Environments," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    4. Joseph D Monaco & Rose M De Guzman & Hugh T Blair & Kechen Zhang, 2019. "Spatial synchronization codes from coupled rate-phase neurons," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-42, January.
    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. Eleonora Russo & Nadine Becker & Aleks P. F. Domanski & Timothy Howe & Kipp Freud & Daniel Durstewitz & Matthew W. Jones, 2024. "Integration of rate and phase codes by hippocampal cell-assemblies supports flexible encoding of spatiotemporal context," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Zhenrui Liao & Kevin C. Gonzalez & Deborah M. Li & Catalina M. Yang & Donald Holder & Natalie E. McClain & Guofeng Zhang & Stephen W. Evans & Mariya Chavarha & Jane Simko & Christopher D. Makinson & M, 2024. "Functional architecture of intracellular oscillations in hippocampal dendrites," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. 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.
    13. Xiaoyang Long & Daniel Bush & Bin Deng & Neil Burgess & Sheng-Jia Zhang, 2025. "Allocentric and egocentric spatial representations coexist in rodent medial entorhinal cortex," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
    14. 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.
    15. Sara Mahallati & James C Bezdek & Milos R Popovic & Taufik A Valiante, 2019. "Cluster tendency assessment in neuronal spike data," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-29, November.
    16. Soraya L. S. Dunn & Stephen M. Town & Jennifer K. Bizley & Daniel Bendor, 2022. "Behaviourally modulated hippocampal theta oscillations in the ferret persist during both locomotion and immobility," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    17. 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.
    18. Siavash Ahmadi & Takuya Sasaki & Marta Sabariego & Christian Leibold & Stefan Leutgeb & Jill K. Leutgeb, 2025. "Distinct roles of dentate gyrus and medial entorhinal cortex inputs for phase precession and temporal correlations in the hippocampal CA3 area," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    19. Cheng Wang & Heekyung Lee & Geeta Rao & James J. Knierim, 2024. "Multiplexing of temporal and spatial information in the lateral entorhinal cortex," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. 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.

    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:pone00:0060599. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.