Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layeropen access
- Authors
- Bae, Hyojin; Park, Sa-Yoon; Kim, Sang Jeong; Kim, Chang-Eop
- Issue Date
- Dec-2022
- Publisher
- FRONTIERS MEDIA SA
- Keywords
- cerebellum; expansion recoding; kernel machine; radial basis (RBF) neural network; granule cell layer
- Citation
- FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, v.16
- Journal Title
- FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
- Volume
- 16
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86747
- DOI
- 10.3389/fncom.2022.1062392
- ISSN
- 1662-5188
- 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.
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Collections - 한의과대학 > 한의예과 > 1. Journal Articles
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