Place-cell capacity and volatility with grid-like inputs

  1. Man Yi Yim
  2. Lorenzo A Sadun
  3. Ila R Fiete  Is a corresponding author
  4. Thibaud Taillefumier  Is a corresponding author
  1. The University of Texas, Austin, United States
  2. Brain & Cognitive Sciences Department and the McGovern Institute at MIT, United States

Abstract

What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell's repertoire - how many field arrangements it can realize without external cues while its grid inputs are unique; and derive its capacity - the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article. Implementation details and code are available at: https://github.com/myyim/placecellperceptron.

Article and author information

Author details

  1. Man Yi Yim

    Department of Neuroscience, The University of Texas, Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Lorenzo A Sadun

    Department of Mathematics, The University of Texas, Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2518-573X
  3. Ila R Fiete

    Brain & Cognitive Sciences Department and the McGovern Institute at MIT, Cambridge, United States
    For correspondence
    fiete@mit.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4738-2539
  4. Thibaud Taillefumier

    Department of Mathematics and Neuroscience, The University of Texas, Austin, Austin, United States
    For correspondence
    ttaillef@austin.utexas.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3538-6882

Funding

Simons Foundation (Simons Collaboration on the Global Brain)

  • Man Yi Yim
  • Ila R Fiete

Howard Hughes Medical Institute (Faculty Scholars Program)

  • Ila R Fiete

Alfred P. Sloan Foundation (Alfred P. Sloan Research Fellowship FG-2017-9554)

  • Thibaud Taillefumier

Office of Naval Research (S&T BAA Award N00014-19-1-2584)

  • Ila R Fiete

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Yim et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,374
    views
  • 300
    downloads
  • 10
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Man Yi Yim
  2. Lorenzo A Sadun
  3. Ila R Fiete
  4. Thibaud Taillefumier
(2021)
Place-cell capacity and volatility with grid-like inputs
eLife 10:e62702.
https://doi.org/10.7554/eLife.62702

Share this article

https://doi.org/10.7554/eLife.62702

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Sarah De Beuckeleer, Tim Van De Looverbosch ... Winnok H De Vos
    Research Article

    Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.

    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics
    Bin Zheng, Meimei Duan ... Peng Zheng
    Research Article

    Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.