A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control

The study of cerebellum has resulted in a common agreement that it is implicated in motor learning for movement coordination. Learning governed by error signal through synaptic eligibility traces has been proposed to be a learning mechanism in cerebellum. In this paper, we extend this idea and suggest a simplified and improved cerebellar model with priority-based delayed eligibility trace learning rule (S-CDE) that enables a mobile robot to freely and smoothly navigate in an environment. S-CDE is constructed in a brain-based device which mimics the anatomy, physiology, and dynamics of cerebellum. The input signal in terms of depth information generated from a simulated laser sensor is encoded as neuronal region activity for velocity and turn rate control. A priority-based delayed eligibility trace learning rule is proposed to maximize the usage of input signals for learning in synapses on Purkinje cell and cells in the deep cerebellar nuclei of cerebellum. Error signal generation and input signal conversion algorithms for turn rate and velocity are designed to facilitate training in an environment containing turns of varying curvatures. S-CDE is tested on a simulated mobile robot which had to randomly navigate maps of Singapore and Hong Kong expressways.