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

• Published in: Neuroscience 2021-05, Vol.462, p.151-174
• Main Authors: Kawato, Mitsuo, Ohmae, Shogo, Hoang, Huu, Sanger, Terry
• Format: Article
• Language: English
• Subjects: Albus; Cerebellum; Ito; Marr; Masao Ito; Models, Neurological; Neuronal Plasticity; Purkinje Cells; Synapses
• ISSN: 0306-4522; 1873-7544
• DOI: 10.1016/j.neuroscience.2020.06.019

•Comparisons of the models of Marr, Ito and Albus for motor learning and control.•A review of synaptic plasticity in achieving supervised learning.•Condon theory may be relevant to newer functions of the cerebellum.•Computational roles of cerebellar internal models.•Hierarchical reinforcement learning with multiple internal models as a new computational theory 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.