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A Neuropsychologically-Inspired Computational Approach to the Generalization of Cerebellar Learning

  • S. D. Teddy
  • E. M. -K. Lai
  • C. Quek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

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.

Keywords

Input Stimulus Memory Content Human Cerebellum Motor Skill Learning Compute Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. D. Teddy
    • 1
  • E. M. -K. Lai
    • 1
  • C. Quek
    • 1
  1. 1.Centre for Computational Intelligence, School of Computer EngineeringNanyang Technological UniversitySingapore

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