A Cerebellar Model of Timing and Prediction
in the Control of Reaching

Andrew G. Barto$^{\dag }$, Andrew H. Fagg$^{\dag }$,
Nathan Sitkoff$^{\dag }$, and James C. Houk^*

$^{\dagger}$Department of Computer Science, University of Massachusetts
^*Department of Physiology, Northwestern University Medical School

Abstract

A simplified model of the cerebellum was developed to explore its potential for adaptive, predictive control based on delayed feedback information. An abstract representation of a single Purkinje cell with multistable properties was interfaced, via a formalized premotor network, with a simulated single degree-of-freedom limb. The limb actuator was a nonlinear spring-mass system based on the nonlinear velocity dependence of the stretch reflex. By including realistic mossy fiber signals, as well as realistic conduction delays in afferent and efferent pathways, the model allowed the investigation of timing and predictive processes relevant to cerebellar involvement in the control of movement. The model regulates movement by learning to react in an anticipatory fashion to sensory feedback. Learning depends on training information generated from corrective movements and uses a temporally-asymmetric form of plasticity for the parallel fiber synapses on Purkinje cells.

The HTML version is still a problem, but there are postscript and pdf versions available.

Full Reference:

Barto, A. G., Fagg, A. H., Sitkoff, N., Houk, J. C. (1999) A Cerebellar Model of Timing and Prediction in the Control of Reaching, Neural Computation, 11:565-594

fagg@cs.umass.edu

Last modified: Mon Dec 13 15:40:33 1999