• Mitsuo Kawato, Shogo Ohmae, Huu Hoang, and Terry Sanger.
• Brain Information Communication Research Group, Advanced Telecommunications Research Institutes International (ATR), Hikaridai 2-2-2, "Keihanna Science City", Kyoto 619-0288, Japan; Center for Advanced Intelligence Project (AIP), RIKEN, Nihonbashi Mitsui Building, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan. Electronic address: kawato@atr.jp.
• Neuroscience. 2021 May 10; 462: 151-174.

AbstractFifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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