Cooperative motor learning model for cerebellar control of balance and locomotion

  • Authors:

  • Mingxiao Ding;Naigong Yu;Xiaogang Ruan

  • Affiliations:

  • Beijing University of Technology, Beijing, China;Beijing University of Technology, Beijing, China;Beijing University of Technology, Beijing, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

A computational model in the framework of reinforcement learning, called Cooperative Motor Learning (CML) model, is proposed to realize cerebellar control of balance and locomotion. In the CML model, cerebellum is a parallel pathway that the vermis and the flocculonodular lobe play a role of reflex actions executor, and that the intermediate zone of the cerebellum participates in initiating voluntary actions. During the training phase, the cerebral cortex provides the predictive error through climbing fiber to modulate the concurrently activated synapses between parallel fibers and purkinje cells. Meanwhile, the intermediate zone of the cerebellum computes the temporal difference (TD) error as a training signal for the cerebral cortex. In the simulation experiment for the balance of double inverted pendulum on a cart, a well-trained CML model can smoothly push the pendulum into the equilibrium position.