Task-dependent optimal representations for cerebellar learning

  1. Marjorie Xie
  2. Samuel P Muscinelli
  3. Kameron Decker Harris
  4. Ashok Litwin-Kumar  Is a corresponding author
  1. Columbia University, United States
  2. Western Washington University, United States

Abstract

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.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Code implementing the model is available on github: https://github.com/marjoriexie/cerebellar-task-dependent

Article and author information

Author details

  1. Marjorie Xie

    Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Samuel P Muscinelli

    Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kameron Decker Harris

    Department of Computer Science, Western Washington University, Bellingham, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Ashok Litwin-Kumar

    Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
    For correspondence
    a.litwin-kumar@columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2422-6576

Funding

National Institutes of Health (T32-NS06492)

  • Marjorie Xie

Simons Foundation

  • Samuel P Muscinelli
  • Ashok Litwin-Kumar

Swartz Foundation

  • Samuel P Muscinelli

Washington Research Foundation

  • Kameron Decker Harris

Burroughs Wellcome Fund

  • Ashok Litwin-Kumar

Gatsby Charitable Foundation (GAT3708)

  • Marjorie Xie
  • Samuel P Muscinelli
  • Ashok Litwin-Kumar

National Science Foundation (DBI-1707398)

  • Marjorie Xie
  • Samuel P Muscinelli
  • Ashok Litwin-Kumar

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

Reviewing Editor

  1. Jörn Diedrichsen, Western University, Canada

Version history

  1. Preprint posted: August 15, 2022 (view preprint)
  2. Received: August 22, 2022
  3. Accepted: September 5, 2023
  4. Accepted Manuscript published: September 6, 2023 (version 1)
  5. Version of Record published: September 29, 2023 (version 2)

Copyright

© 2023, Xie 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. Marjorie Xie
  2. Samuel P Muscinelli
  3. Kameron Decker Harris
  4. Ashok Litwin-Kumar
(2023)
Task-dependent optimal representations for cerebellar learning
eLife 12:e82914.
https://doi.org/10.7554/eLife.82914

Share this article

https://doi.org/10.7554/eLife.82914

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