IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0202561.html
   My bibliography  Save this article

The importance of forgetting: Limiting memory improves recovery of topological characteristics from neural data

Author

Listed:
  • Samir Chowdhury
  • Bowen Dai
  • Facundo Mémoli

Abstract

We develop of a line of work initiated by Curto and Itskov towards understanding the amount of information contained in the spike trains of hippocampal place cells via topology considerations. Previously, it was established that simply knowing which groups of place cells fire together in an animal’s hippocampus is sufficient to extract the global topology of the animal’s physical environment. We model a system where collections of place cells group and ungroup according to short-term plasticity rules. In particular, we obtain the surprising result that in experiments with spurious firing, the accuracy of the extracted topological information decreases with the persistence (beyond a certain regime) of the cell groups. This suggests that synaptic transience, or forgetting, is a mechanism by which the brain counteracts the effects of spurious place cell activity.

Suggested Citation

  • Samir Chowdhury & Bowen Dai & Facundo Mémoli, 2018. "The importance of forgetting: Limiting memory improves recovery of topological characteristics from neural data," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
  • Handle: RePEc:plo:pone00:0202561
    DOI: 10.1371/journal.pone.0202561
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202561
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0202561&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0202561?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. Y Dabaghian & F Mémoli & L Frank & G Carlsson, 2012. "A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-14, August.
    2. Carina Curto & Vladimir Itskov, 2008. "Cell Groups Reveal Structure of Stimulus Space," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-13, October.
    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. Mamiko Arai & Vicky Brandt & Yuri Dabaghian, 2014. "The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    2. Sudhamayee, K. & Krishna, M. Gopal & Manimaran, P., 2023. "Simplicial network analysis on EEG signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Chong, Woon Kian & Chang, Chiachi, 2024. "Information exploitation of human resource data with persistent homology," Journal of Business Research, Elsevier, vol. 172(C).
    4. Y Dabaghian & F Mémoli & L Frank & G Carlsson, 2012. "A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-14, August.
    5. Williams, Robert, 2018. "Strongly maximal intersection-complete neural codes on grids are convex," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 162-175.
    6. Zixuan Cang & Lin Mu & Guo-Wei Wei, 2018. "Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-44, 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:pone00:0202561. 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.