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.

自大卫·马尔（David Marr），伊藤正雄（Masao Ito）和詹姆斯·阿不思（James Albus）提出小脑功能的开创性模型以来已经过去了五十年。这些模型具有一个基本概念，即在感觉运动学习过程中，攀爬纤维的活动引导平行纤维-Purkinje细胞突触发生塑性变化。然而，它们在几个重要方面有所不同，包括小脑的整体与互补作用，作为计算目标的模式识别与控制，突触可塑性的增强与抑制，攀爬纤维传输的示教信号与错误信号，颗粒细胞的稀疏扩展编码，以及小脑内部模型。在这篇综述中，我们根据最近的计算和实验研究评估了三种模型的不同特征。