Cerebellar control of endpoint position--A simulation model

T. Sinkjaer*, C. H. Wu, A. G. Barto, J. C. Houk

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


The ability of a neural network model of the cerebellum to control a nonlinear dynamical model of the neuromuscular system is explored. The cerebellum is represented by adjustable pattern generator (APG) modules capable of commanding movements from arbitrary starting positions to specific endpoints. The network is trained to match endpoints to visual targets using a biologically motivated learning rule. Neural signals recorded from a monkey subject helped to guide realistic simulations. The simulation results illustrate how commanded velocity automatically increases when the initial position of the limb is farther from the target position. The mechanism of this 'feedforward' compensation can be traced to a smaller value of limb position input during the preselection period. The decreased excitation serves to increase the number of Purkinje cells that get switched to an off state before the movement begins, and the resultant decrease in loop inhibition leads to a larger commanded velocity. The simulations also demonstrate how limited feedback through the cerebellum can be used, without the threat of instability, to regulate the achievement of a targeted endpoint.

Original languageEnglish (US)
Title of host publicationIJCNN. International Joint Conference on Neural Networks
PublisherPubl by IEEE
Number of pages6
StatePublished - Dec 1 1990
Event1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3) - San Diego, CA, USA
Duration: Jun 17 1990Jun 21 1990


Other1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3)
CitySan Diego, CA, USA

ASJC Scopus subject areas

  • Engineering(all)


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