50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty 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. This article is part of a Special Issue entitled: In Memoriam: Masao Ito-A Visionary Neuroscientist with a Passion for the Cerebellum. (c) 2020 The Author(s). Published by Elsevier Ltd on behalf of IBRO. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Fifty years have passed since seminal models of cerebellar functions were proposed by David Marr, Masao Ito, and James Albus. These models share the concept of plastic changes in parallel-fiber-Purkinje-cell synapses guided by climbing-fiber activities during sensorimotor learning, but differ in key aspects. Evaluating the features of the three models based on recent studies, a new direction of hierarchical reinforcement learning with multiple internal models in cerebellar computational frameworks is proposed.