Task-dependent optimal representations for cerebellar learning
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
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.
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