Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layer

Bae, Hyojin and Park, Sa-Yoon and Kim, Sang Jeong and Kim, Chang-Eop (2022) Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layer. Frontiers in Computational Neuroscience, 16. ISSN 1662-5188

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Abstract

Sensorimotor information provided by mossy fibers (MF) is mapped to high-dimensional space by a huge number of granule cells (GrC) in the cerebellar cortex’s input layer. Significant studies have demonstrated the computational advantages and primary contributor of this expansion recoding. Here, we propose a novel perspective on the expansion recoding where each GrC serve as a kernel basis function, thereby the cerebellum can operate like a kernel machine that implicitly use high dimensional (even infinite) feature spaces. We highlight that the generation of kernel basis function is indeed biologically plausible scenario, considering that the key idea of kernel machine is to memorize important input patterns. We present potential regimes for developing kernels under constrained resources and discuss the advantages and disadvantages of each regime using various simulation settings.

Item Type: Article
Subjects: European Repository > Medical Science
Depositing User: Managing Editor
Date Deposited: 27 Mar 2023 03:58
Last Modified: 16 Jan 2024 04:17
URI: http://go7publish.com/id/eprint/1922

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