Adaptive robot control by the general-purpose cerebellar neural network model

Abstract

The cerebellum plays a pivotal role in motor control and learning. Due to its uniform neuronal network structure throughout the cerebellar cortex among different animal species, the cerebellum is considered as a general motor learning machine that is capable of controlling wide varieties of control objects by the common neuronal network structure and learning algorithm. We have constructed a physio-anatomically realistic cerebellar neuronal network model, and implemented on a PC so that it can run in real-time and work as an adaptive controller of robots. Here we summarize our cerebellar model from its structure and learning algorithm to soft and hardware implementation, and show examples of application to robot control.