  The Cerebellar Model Articulation Controller (CMAC) is an influential
brain-inspired computing model in many relevant fields. Since its inception in
the 1970s, the model has been intensively studied and many variants of the
prototype, such as Kernel-CMAC, Self-Organizing Map CMAC, and Linguistic CMAC,
have been proposed. This review article focus on how the CMAC model is
gradually developed and refined to meet the demand of fast, adaptive, and
robust control. Two perspective, CMAC as a neural network and CMAC as a table
look-up technique are presented. Three aspects of the model: the architecture,
learning algorithms and applications are discussed. In the end, some potential
future research directions on this model are suggested.
