1. Computational and Systems Biology
  2. Neuroscience
Download icon

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
Research Article
  • Cited 0
  • Views 925
  • Annotations
Cite this article as: eLife 2021;10:e62702 doi: 10.7554/eLife.62702

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

    Department of Mathematics, The University of Texas, Austin, Austin, 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.

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Publication history

  1. Received: September 2, 2020
  2. Accepted: April 28, 2021
  3. Accepted Manuscript published: May 24, 2021 (version 1)
  4. Accepted Manuscript updated: May 26, 2021 (version 2)
  5. Version of Record published: July 21, 2021 (version 3)

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

  • 925
    Page views
  • 197
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

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

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

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Kyoung Jo et al.
    Research Article Updated

    Human primordial germ cells (hPGCs) form around the time of implantation and are the precursors of eggs and sperm. Many aspects of hPGC specification remain poorly understood because of the inaccessibility of the early postimplantation human embryo for study. Here, we show that micropatterned human pluripotent stem cells (hPSCs) treated with BMP4 give rise to hPGC-like cells (hPGCLC) and use these as a quantitatively reproducible and simple in vitro model to interrogate this important developmental event. We characterize micropatterned hPSCs up to 96 hr and show that hPGCLC populations are stable and continue to mature. By perturbing signaling during hPGCLC differentiation, we identify a previously unappreciated role for Nodal signaling and find that the relative timing and duration of BMP and Nodal signaling are critical parameters controlling the number of hPGCLCs. We formulate a mathematical model for a network of cross-repressive fates driven by Nodal and BMP signaling, which predicts the measured fate patterns after signaling perturbations. Finally, we show that hPSC colony size dictates the efficiency of hPGCLC specification, which led us to dramatically improve the efficiency of hPGCLC differentiation.

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Leandro M Velez et al.
    Short Report Updated

    Skeletal muscle plays an integral role in coordinating physiological homeostasis, where signaling to other tissues via myokines allows for coordination of complex processes. Here, we aimed to leverage natural genetic correlation structure of gene expression both within and across tissues to understand how muscle interacts with metabolic tissues. Specifically, we performed a survey of genetic correlations focused on myokine gene regulation, muscle cell composition, cross-tissue signaling, and interactions with genetic sex in humans. While expression levels of a majority of myokines and cell proportions within skeletal muscle showed little relative differences between males and females, nearly all significant cross-tissue enrichments operated in a sex-specific or hormone-dependent fashion; in particular, with estradiol. These sex- and hormone-specific effects were consistent across key metabolic tissues: liver, pancreas, hypothalamus, intestine, heart, visceral, and subcutaneous adipose tissue. To characterize the role of estradiol receptor signaling on myokine expression, we generated male and female mice which lack estrogen receptor α specifically in skeletal muscle (MERKO) and integrated with human data. These analyses highlighted potential mechanisms of sex-dependent myokine signaling conserved between species, such as myostatin enriched for divergent substrate utilization pathways between sexes. Several other putative sex-dependent mechanisms of myokine signaling were uncovered, such as muscle-derived tumor necrosis factor alpha (TNFA) enriched for stronger inflammatory signaling in females compared to males and GPX3 as a male-specific link between glycolytic fiber abundance and hepatic inflammation. Collectively, we provide a population genetics framework for inferring muscle signaling to metabolic tissues in humans. We further highlight sex and estradiol receptor signaling as critical variables when assaying myokine functions and how changes in cell composition are predicted to impact other metabolic organs.