1. Neuroscience
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Environmental deformations dynamically shift the grid cell spatial metric

  1. Alexandra T Keinath  Is a corresponding author
  2. Russell A Epstein
  3. Vijay Balasubramanian  Is a corresponding author
  1. University of Pennsylvania, United States
Research Article
  • Cited 17
  • Views 1,413
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Cite this article as: eLife 2018;7:e38169 doi: 10.7554/eLife.38169


In familiar environments, the firing fields of entorhinal grid cells form regular triangular lattices. However, when the geometric shape of the environment is deformed, these time-averaged grid patterns are distorted in a grid scale-dependent and local manner. We hypothesized that this distortion in part reflects dynamic anchoring of the grid code to displaced boundaries, possibly through border cell-grid cell interactions. To test this hypothesis, we first reanalyzed two existing rodent grid rescaling datasets to identify previously unrecognized boundary-tethered shifts in grid phase that contribute to the appearance of rescaling. We then demonstrated in a computational model that boundary-tethered phase shifts, as well as scale-dependent and local distortions of the time-averaged grid pattern, could emerge from border-grid interactions without altering inherent grid scale. Together, these results demonstrate that environmental deformations induce history-dependent shifts in grid phase, and implicate border-grid interactions as a potential mechanism underlying these dynamics.

Data availability

All simulations were conducted with custom-written MATLAB scripts. These scripts and the simulation results presented here are available on Github at: https://github.com/akeinath/Keinath_BoundaryTetheredModel. All values generated during our reanalysis are included as source data files. All original reanalyzed data were originally reported in the following papers:1)Barry et al., 2007. Experience-dependent rescaling of entorhinal grids. https://doi.org/10.1038/nn1905;2)Stensola et al., 2012. The entorhinal map is descritized. https://doi.org/10.1038/nature11649.These data are available upon request from the corresponding authors of these papers.

Article and author information

Author details

  1. Alexandra T Keinath

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1622-7835
  2. Russell A Epstein

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Vijay Balasubramanian

    Department of Physics, University of Pennsylvania, Philadelphia, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6497-3819


National Science Foundation (PHY-1734030)

  • Vijay Balasubramanian

National Institutes of Health (EY022350)

  • Russell A Epstein

National Institutes of Health (EY022350)

  • Russell A Epstein

National Science Foundation (966142)

  • Alexandra T Keinath

National Science Foundation (PHY-584 1607611))

  • Vijay Balasubramanian

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

Reviewing Editor

  1. Laura Colgin, The University of Texas at Austin, Center for Learning and Memory, United States

Publication history

  1. Received: May 8, 2018
  2. Accepted: October 21, 2018
  3. Accepted Manuscript published: October 22, 2018 (version 1)
  4. Version of Record published: October 26, 2018 (version 2)


© 2018, Keinath 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.


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