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Entorhinal velocity signals reflect environmental geometry

Nature Neuroscience volume 23, pages 239–251 (2020)Cite this article

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Abstract

The entorhinal cortex contains neurons that represent self-location, including grid cells that fire in periodic locations and velocity signals that encode running speed and head direction. Although the size and shape of the environment influence grid patterns, whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here we report that speed cells rescale after changes to the size and shape of the environment. Moreover, head direction cells reorganize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows dissociation of the coordination between cell types, with grid and speed cells, but not head direction cells, responding in concert to environmental change. These results point to malleability in the coding features of multiple entorhinal cell types and have implications for which cell types contribute to the velocity signal used by computational models of grid cells.

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Fig. 1: Environmental perturbation distorts grid spacing.
Fig. 2: S-encoding cells rescale in response to environmental perturbation.
Fig. 3: The gain (slope and intercept) of the theta frequency–speed relationship increases in compression and decreases in expansion conditions.
Fig. 4: A directionally specific asymmetric bias develops after multiple exposures to modified environments.
Fig. 5: TRIP8b KO grid spacing is less sensitive to environmental perturbation.
Fig. 6: TRIP8b KO speed signals are less sensitive to environmental perturbation while directional signals remain malleable.

Data availability

Data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

Code used in the analyses described in this manuscript can be accessed at: https://github.com/GiocomoLab/Munn_et_al_2019. The code used in the LNP model can be accessed at https://github.com/GiocomoLab/ln-model-of-mec-neurons

Change history

  • 21 February 2020

    The Supplementary Information originally published was missing and has been replaced on 21/02/2020.

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Acknowledgements

L.M.G. recieves funding as a New York Stem Cell Foundation–Robertson Investigator. This work was supported by funding from the New York Stem Cell Foundation, NIMH MH106475, the Office of Naval Research N000141812690, the Simons Foundation 542987SPI, the Whitehall Foundation, the James S. McDonnell Foundation and a Klingenstein–Simons award to L.M.G.; the Philip Wrightson Postdoctoral Fellowship from the Neurological Foundation of New Zealand awarded to R.G.M.; a National Science Foundation Graduate Research Fellowship awarded to C.S.M; a Stanford Interdiscplinary Graduate Fellowship awarded to K.H.; and NINDS NS059934 to D.M.C. We thank A. Borrayo and A. Diaz for histology assistance and M.E. Hasselmo for input on the oscillatory interference model.

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Authors and Affiliations

  1. Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA

    Robert G. K. Munn, Caitlin S. Mallory, Kiah Hardcastle & Lisa M. Giocomo

  2. Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA

    Dane M. Chetkovich

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  1. Robert G. K. Munn
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  2. Caitlin S. Mallory
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  3. Kiah Hardcastle
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  4. Dane M. Chetkovich
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  5. Lisa M. Giocomo
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Contributions

L.M.G. and R.G.M. conceptualized experiments and analyses. C.S.M. and R.G.M. performed chronic implantations and collected and analyzed in vivo data. K.H. provided support on analyses and performed computational simulations. D.M.C. provided the TRIP8b KO mouse line. L.M.G. and R.G.M. wrote the paper with feedback from all authors.

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Correspondence to Robert G. K. Munn or Lisa M. Giocomo.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Dori Derdikman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Munn, R.G.K., Mallory, C.S., Hardcastle, K. et al. Entorhinal velocity signals reflect environmental geometry. Nat Neurosci 23, 239–251 (2020). https://doi.org/10.1038/s41593-019-0562-5

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