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.

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.

Metrics

  • 1,545
    views
  • 247
    downloads
  • 6
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.