Visual cue-related activity of cells in the medial entorhinal cortex during navigation in virtual reality

  1. Amina A Kinkhabwala  Is a corresponding author
  2. Yi Gu  Is a corresponding author
  3. Dmitriy Aronov
  4. David W Tank  Is a corresponding author
  1. Caltech, United States
  2. Princeton University, United States

Abstract

During spatial navigation, animals use self-motion to estimate positions through path integration. However, estimation errors accumulate over time and it is unclear how they are corrected. Here we report a new cell class ('cue cell') encoding visual cues that could be used to correct errors in path integration in mouse medial entorhinal cortex (MEC). During virtual navigation, individual cue cells exhibited firing fields only near visual cues and their population response formed sequences repeated at each cue. These cells consistently responded to cues across multiple environments. On a track with cues on left and right sides, most cue cells responded to cues only one side. During navigation in a real arena, they showed spatially stable activity and accounted for 32% of unidentified, spatially stable MEC cells. These cue cell properties demonstrate that the MEC contains a code representing spatial landmarks, which could be important for error correction during path integration.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Amina A Kinkhabwala

    Biology and Biological Engineering, Caltech, Pasadena, United States
    For correspondence
    amina.kinkhabwala@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Yi Gu

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    guyi.thu@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  3. Dmitriy Aronov

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. David W Tank

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    dwtank@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9423-4267

Funding

National Institute of Neurological Disorders and Stroke (5R37NS081242)

  • David W Tank

National Institute of Mental Health (5R01MH083686)

  • David W Tank

National Institutes of Health (F32NS070514-01A1)

  • Amina A Kinkhabwala

The funders had no role in the experiments or analysis in this publication.

Ethics

Animal experimentation: All procedures were approved by the Princeton University Institutional Animal Care and Use Committee (IACUC protocol# 1910-15) and were in compliance with the Guide for the Care and Use of Laboratory Animals.

Reviewing Editor

  1. Sachin Deshmukh, Indian Institute of Science Bangalore, India

Version history

  1. Received: October 26, 2018
  2. Accepted: March 6, 2020
  3. Accepted Manuscript published: March 9, 2020 (version 1)
  4. Version of Record published: March 23, 2020 (version 2)

Copyright

© 2020, Kinkhabwala 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

  • 3,402
    Page views
  • 563
    Downloads
  • 12
    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)

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. Amina A Kinkhabwala
  2. Yi Gu
  3. Dmitriy Aronov
  4. David W Tank
(2020)
Visual cue-related activity of cells in the medial entorhinal cortex during navigation in virtual reality
eLife 9:e43140.
https://doi.org/10.7554/eLife.43140

Further reading

    1. Cell Biology
    2. Neuroscience
    Elisabeth Jongsma, Anita Goyala ... Collin Yvès Ewald
    Research Article Updated

    The amyloid beta (Aβ) plaques found in Alzheimer’s disease (AD) patients’ brains contain collagens and are embedded extracellularly. Several collagens have been proposed to influence Aβ aggregate formation, yet their role in clearance is unknown. To investigate the potential role of collagens in forming and clearance of extracellular aggregates in vivo, we created a transgenic Caenorhabditis elegans strain that expresses and secretes human Aβ1-42. This secreted Aβ forms aggregates in two distinct places within the extracellular matrix. In a screen for extracellular human Aβ aggregation regulators, we identified different collagens to ameliorate or potentiate Aβ aggregation. We show that a disintegrin and metalloprotease a disintegrin and metalloprotease 2 (ADM-2), an ortholog of ADAM9, reduces the load of extracellular Aβ aggregates. ADM-2 is required and sufficient to remove the extracellular Aβ aggregates. Thus, we provide in vivo evidence of collagens essential for aggregate formation and metalloprotease participating in extracellular Aβ aggregate removal.

    1. Computational and Systems Biology
    2. Neuroscience
    Marjorie Xie, Samuel P Muscinelli ... Ashok Litwin-Kumar
    Research Article Updated

    The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.