A Computational Model of Cerebellar Learning
for Limb Control

Andrew H. Fagg, Nathan Sitkoff, Andrew G. Barto

{fagg | sitkoff | barto}@cs.umass.edu
Computer Science Dept

James C. Houk

Dept of Physiology
Northwestern University School of Medicine

In this paper, we present a model of cerebellar learning for control of limb movements. The model learns to generate relatively smooth and direct arm movements, while not assuming (as do some models) a high-level input that specifies a pre-planned trajectory . The model controls a simulated two-joint arm, which is actuated by a set of six muscles. Control of the arm is shared by a learning cerebellar module, and a hard-wired extra-cerebellar (EC) system. The cerebellar module consists of a group of independent submodules, referred to as Adjustable Pattern Generators (APGs). Each APG is made up of a set of Purkinje cells and a single cerebellar nuclear cell. Activation of an APG's nuclear cell drives a muscle synergy (a subset of the muscles). The EC system is responsible for producing low-quality corrective movements in situations where the cerebellar module is unable to bring the arm to the specified target. Via proprioceptive inputs, the inferior olive assesses directional errors in cerebellar-generated movements by observing subsequent corrective movements produced by the EC system.

For more information, see the poster text.

Presented at 1997 Spring Meeting on the Neural Control of Movement

Andrew H. Fagg