Control of a simulated arm using a novel combination of a cerebellar learning mechanisms

Christopher Assad*, Mitra J. Hartmann, Michael G. Paulin

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

We present a model of cerebellar cortex that combines two types of learning: feedforward predictive association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity. The model is motivated by recent physiological and anatomical evidence and has more computational capacity than previous functional models of cerebellum. To demonstrate the model's utility, we simulated the control of a simple virtual arm. The model successfully learned to control the timing of release for the arm during a target-throwing task.

Original languageEnglish (US)
Pages (from-to)275-283
Number of pages9
JournalNeurocomputing
Volume44-46
DOIs
StatePublished - Jul 27 2002

Keywords

  • Cerebellar learning
  • Cerebellum
  • Dynamic state estimation
  • Sensorimotor control

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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