The Rules of Cerebellar Learning: Around the Ito Hypothesis

As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning sys-tems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the mag-nitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys. This article is part of a Special Issue entitled: In Memoriam: Masao Ito-A Visionary Neuroscientist with a Passion for the Cerebellum. (c) 2020 IBRO. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a cerebellar learning model based on Masao Ito's original model, with four principles aligning well with conclusions from multiple cerebellar learning systems. A computational neural circuit model based on these principles mimics neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.