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Intracellular dynamics of hippocampal place cells during virtual navigation

Author

Listed:
  • Christopher D. Harvey

    (Princeton Neuroscience Institute,
    Lewis-Sigler Institute for Integrative Genomics,
    Princeton University, Princeton, New Jersey 08544, USA)

  • Forrest Collman

    (Princeton Neuroscience Institute,
    Lewis-Sigler Institute for Integrative Genomics,
    Princeton University, Princeton, New Jersey 08544, USA)

  • Daniel A. Dombeck

    (Princeton Neuroscience Institute,
    Lewis-Sigler Institute for Integrative Genomics,
    Princeton University, Princeton, New Jersey 08544, USA)

  • David W. Tank

    (Princeton Neuroscience Institute,
    Lewis-Sigler Institute for Integrative Genomics,
    Princeton University, Princeton, New Jersey 08544, USA)

Abstract

Hippocampal place cells encode spatial information in rate and temporal codes. To examine the mechanisms underlying hippocampal coding, here we measured the intracellular dynamics of place cells by combining in vivo whole-cell recordings with a virtual-reality system. Head-restrained mice, running on a spherical treadmill, interacted with a computer-generated visual environment to perform spatial behaviours. Robust place-cell activity was present during movement along a virtual linear track. From whole-cell recordings, we identified three subthreshold signatures of place fields: an asymmetric ramp-like depolarization of the baseline membrane potential, an increase in the amplitude of intracellular theta oscillations, and a phase precession of the intracellular theta oscillation relative to the extracellularly recorded theta rhythm. These intracellular dynamics underlie the primary features of place-cell rate and temporal codes. The virtual-reality system developed here will enable new experimental approaches to study the neural circuits underlying navigation.

Suggested Citation

  • Christopher D. Harvey & Forrest Collman & Daniel A. Dombeck & David W. Tank, 2009. "Intracellular dynamics of hippocampal place cells during virtual navigation," Nature, Nature, vol. 461(7266), pages 941-946, October.
    • Handle: RePEc:nat:nature:v:461:y:2009:i:7266:d:10.1038_nature08499
    DOI: 10.1038/nature08499
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    Citations

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    Cited by:

    1. Eric Lowet & Krishnakanth Kondabolu & Samuel Zhou & Rebecca A. Mount & Yangyang Wang & Cara R. Ravasio & Xue Han, 2022. "Deep brain stimulation creates informational lesion through membrane depolarization in mouse hippocampus," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Jeffrey P Nguyen & Ashley N Linder & George S Plummer & Joshua W Shaevitz & Andrew M Leifer, 2017. "Automatically tracking neurons in a moving and deforming brain," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-19, May.
    3. Florian Raudies & Michael E Hasselmo, 2012. "Modeling Boundary Vector Cell Firing Given Optic Flow as a Cue," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-17, June.
    4. Brent Kevin Young & Jayden Nicole Brennan & Ping Wang & Ning Tian, 2018. "Virtual reality method to analyze visual recognition in mice," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-14, May.
    5. Samuel K. H. Sy & Danny C. W. Chan & Roy C. H. Chan & Jing Lyu & Zhongqi Li & Kenneth K. Y. Wong & Chung Hang Jonathan Choi & Vincent C. T. Mok & Hei-Ming Lai & Owen Randlett & Yu Hu & Ho Ko, 2023. "An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Kirsten Bohmbach & Nicola Masala & Eva M. Schönhense & Katharina Hill & André N. Haubrich & Andreas Zimmer & Thoralf Opitz & Heinz Beck & Christian Henneberger, 2022. "An astrocytic signaling loop for frequency-dependent control of dendritic integration and spatial learning," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    7. Shinichiro Kira & Houman Safaai & Ari S. Morcos & Stefano Panzeri & Christopher D. Harvey, 2023. "A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions," Nature Communications, Nature, vol. 14(1), pages 1-28, December.

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