Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems
Abstract
This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.National Science Foundation (IRI-90-24877, IRI-87-16960); Office of Naval Research (N00014-92-J-1309); Consejo Nacional de Ciencia y Technología (63462); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (AFOSR 90-0083, ONR N00014-92-J-4015
