Gilmer, JI, Farries, MA, Kilpatrick, Z et al. (3 more authors) (2023) An emergent temporal basis set robustly supports cerebellar time-series learning. Journal of Neurophysiology, 129 (1). pp. 159-176. ISSN 0022-3077
Abstract
The cerebellum is considered a “learning machine” essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum’s input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2022, Journal of Neurophysiology. This is an author produced version of a paper published in Journal of Neurophysiology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | basis set; cerebellum; granule cell; learning; pattern separation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Dec 2022 17:04 |
Last Modified: | 23 Nov 2023 01:13 |
Status: | Published |
Publisher: | American Physiological Society |
Identification Number: | 10.1152/jn.00312.2022 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193910 |