Cerebellar adaptive control of a biomimetic manipulator

We present a learning cerebellar model to control reaching movements of a simulated biomimetic manipulator. The system utilizes the servo mechanism of the spinal re#ex circuitry to allow kinematic control of slow movements while learning predictive dynamic compensation. Key elements of the model are: (1) parallel postural and dynamics controllers; (2) a neural network model explicitly based on the physiology of the cerebellum, utilizing the cerebelloolivary loop for stable learning; (3) modulation of gamma-motoneurons during movements to sense trajectory errors from Ia spindle e!erents. Results demonstrate the ability to rapidly learn to accurately control stereotyped fast movements. ( 1999 Published by Elsevier Science B.V.M All rights reserved.