Stable task information from an unstable neural population

  1. Michael E Rule
  2. Adrianna R Loback
  3. Dhruva Raman
  4. Laura N Driscoll
  5. Christopher D Harvey
  6. Timothy O'Leary  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. Stanford University, United States
  3. Harvard Medical School, United States

Abstract

Over days and weeks, neural activity representing an animal's position and movement in sensorimotor cortex has been found to continually reconfigure or 'drift' during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

Data availability

Datasets recorded in Driscoll et al., 2017, are available from the Dryad repository under the doi:/10.5061/dryad.gqnk98sjq. The analysis code generated during this study is available on Github github.com/michaelerule/stable-task-information.

The following data sets were generated

Article and author information

Author details

  1. Michael E Rule

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4196-774X
  2. Adrianna R Loback

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Dhruva Raman

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Laura N Driscoll

    Electrical Engineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Christopher D Harvey

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Timothy O'Leary

    Engineering, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    tso24@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1029-0158

Funding

Human Frontier Science Program (RGY0069)

  • Michael E Rule
  • Adrianna R Loback
  • Christopher D Harvey
  • Timothy O'Leary

H2020 European Research Council (FLEXNEURO 716643)

  • Dhruva Raman
  • Timothy O'Leary

National Institutes of Health (NS089521)

  • Christopher D Harvey

National Institutes of Health (MH107620)

  • Christopher D Harvey

National Institutes of Health (NS108410)

  • Christopher D Harvey

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

Reviewing Editor

  1. Stephanie Palmer, University of Chicago, United States

Version history

  1. Received: August 15, 2019
  2. Accepted: June 17, 2020
  3. Accepted Manuscript published: July 14, 2020 (version 1)
  4. Version of Record published: July 30, 2020 (version 2)

Copyright

© 2020, Rule 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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  1. Michael E Rule
  2. Adrianna R Loback
  3. Dhruva Raman
  4. Laura N Driscoll
  5. Christopher D Harvey
  6. Timothy O'Leary
(2020)
Stable task information from an unstable neural population
eLife 9:e51121.
https://doi.org/10.7554/eLife.51121

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https://doi.org/10.7554/eLife.51121

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