A neuropsychologically-inspired computational approach to the generalization of cerebellar learning

  • Authors:

  • S. D. Teddy;E. M. -K. Lai;C. Quek

  • Affiliations:

  • Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore

  • Venue:
  • ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
  • Year:
  • 2006

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Abstract

The CMAC neural network is a well-established computational model of the human cerebellum. A major advantage is its localized generalization property which allows for efficient computations. However, there are also two major problems associated with this localized associative property. Firstly, it is difficult to fully-train a CMAC network as the training data has to fully cover the entire set of CMAC memory cells. Secondly, the untrained CMAC cells give rise to undesirable network output when presented with inputs that the network has not previously been trained for. To the best of the authors' knowledge, these issues have not been sufficiently addressed. In this paper, we propose a neuropsychologically-inspired computational approach to alleviate the above-mentioned problems. Motivated by psychological studies on human motor skill learning, a ”patching” algorithm is developed to construct a plausible memory surface for the untrained cells in the CMAC network. We demonstrate through the modeling of the human glucose metabolic process that the ”patching” of untrained cells offers a satisfactory solution to incomplete training in CMAC.