1. Neuroscience
Download icon

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
Research Article
  • Cited 2
  • Views 2,584
  • Annotations
Cite this article as: eLife 2020;9:e43140 doi: 10.7554/eLife.43140

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

Publication 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

  • 2,584
    Page views
  • 444
    Downloads
  • 2
    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. Neuroscience
    Anastassios Karagiannis et al.
    Research Article Updated

    Glucose is the mandatory fuel for the brain, yet the relative contribution of glucose and lactate for neuronal energy metabolism is unclear. We found that increased lactate, but not glucose concentration, enhances the spiking activity of neurons of the cerebral cortex. Enhanced spiking was dependent on ATP-sensitive potassium (KATP) channels formed with KCNJ11 and ABCC8 subunits, which we show are functionally expressed in most neocortical neuronal types. We also demonstrate the ability of cortical neurons to take-up and metabolize lactate. We further reveal that ATP is produced by cortical neurons largely via oxidative phosphorylation and only modestly by glycolysis. Our data demonstrate that in active neurons, lactate is preferred to glucose as an energy substrate, and that lactate metabolism shapes neuronal activity in the neocortex through KATP channels. Our results highlight the importance of metabolic crosstalk between neurons and astrocytes for brain function.

    1. Neuroscience
    Riccardo Caramellino et al.
    Research Advance

    Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.