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)


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.


Input Stimulus Memory Content Human Cerebellum Motor Skill Learning Compute Output 
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  1. 1.
    Middleton, F.A., Strick, P.L.: The cerebellum: An overview. Trends in Cognitive Sciences 27(9), 305–306 (1998)CrossRefGoogle Scholar
  2. 2.
    Albus, J.S.: Marr and Albus theories of the cerebellum two early models of associative memory. In: Proc. IEEE Compcon. (1989)Google Scholar
  3. 3.
    Albus, J.S.: A theory of cerebellar function. Math. Biosci. 10(1), 25–61 (1971)CrossRefGoogle Scholar
  4. 4.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill, New York (2000)Google Scholar
  5. 5.
    Marr, D.: A theory of cerebellar cortex. J. Physiol. London 202, 437–470 (1969)Google Scholar
  6. 6.
    Albus, J.S.: A new approach to manipulator control: The Cerebellar Model Articulation Controller (CMAC). J. Dyn. Syst. Meas. Control, Trans. ASME, 220–227 (1975)Google Scholar
  7. 7.
    Albus, J.S.: Data storage in Cerebellar Model Articullation Controller (CMAC). J. Dyn. Syst. Meas. Control, Trans. ASME, 228–233 (1975)Google Scholar
  8. 8.
    Yamamoto, T., Kaneda, M.: Intelligent controller using CMACs with self-organized structure and its application for a process system. IEICE Trans. Fundamentals 82(5), 856–860 (1999)Google Scholar
  9. 9.
    Wahab, A., Tan, E.C., Abut, H.: HCMAC amplitude spectral subtraction for noise cancellation. In: Intl. Conf. Neural Inform. Processing (2001)Google Scholar
  10. 10.
    Huang, K.L., Hsieh, S.C., Fu, H.C.: Cascade-CMAC neural network applications on the color scanner to printer calibration. In: Intl. Conf. Neural Networks, vol. 1, pp. 10–15 (1997)Google Scholar
  11. 11.
    Miller, W.T., Glanz, F.H., Kraft, L.G.: CMAC: An associative neural network alternative to backpropagation. Proc. IEEE 78(10), 1561–1657 (1990)CrossRefGoogle Scholar
  12. 12.
    Tomporowski, P.D.: The Psychology of Skill: A life-Span Approach. Praeger, Westport CT (2003)Google Scholar
  13. 13.
    Mazur, J.E.: Learning and Behavior. Pearson/Prentice Hall (2006)Google Scholar
  14. 14.
    Scheidt, R.A., Dingwell, J.B., Mussa-Ivaldi, F.A.: Learning to move amid uncertainty. Journal of Neurophysiology 86, 971–985 (2001)Google Scholar
  15. 15.
    Lam, T., Dietz, V.: Transfer of motor performance in an obstacle avoidance task to different walking conditions. Journal of Neurophysiology 92, 2010–2016 (2004)CrossRefGoogle Scholar
  16. 16.
    Chen, Y., et al.: The interaction of a new motor skill and an old one: H-reflex conditioning and locomotion in rats. Journal of Neuroscience 25(29), 6898–6906 (2005)CrossRefGoogle Scholar
  17. 17.
    Houk, J.C., Buckingham, J.T., Barto, A.G.: Models of the cerebellum and motor learning. Behavioral and Brain Sciences 19(3), 368–383 (1996)Google Scholar
  18. 18.
    Tyrrell, T., Willshaw, D.: Cerebellar cortex: Its simulation and the relevance ofMarr’s theory. Philosophical Transactions: Biological Sciences 336(1277), 239–257 (1992)CrossRefGoogle Scholar
  19. 19.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs (1985)MATHGoogle Scholar
  20. 20.
    Palmer, C., Meyer, R.K.: Conceptual and motor learning in music performance. Psychological Science 11(1), 63–68 (2000)CrossRefGoogle Scholar
  21. 21.
    Weigelt, C., et al.: Transfer of motor skill learning in association football. Ergonomics 43(10), 1698–1707 (2000)CrossRefGoogle Scholar
  22. 22.
    Fletcher, L., et al.: Feasibility of an implanted, closed-loop, blood-glucose control device. Immunology 230 (2001)Google Scholar
  23. 23.
    Schetky, L.M., Jardine, P., Moussy, F.: A closed loop implantable artificial pancreas using thin film nitinol mems pumps. In: Proceedings of International Conference on Shape Memory and Superelastic Technologies, SMST 2003 (2003)Google Scholar
  24. 24.
    Sorensen, J.T.: A Physiologic Model of Glucose Metabolism in Man and its Use to Design and Assess Improved Insulin Therapies for Diabetes. PhD thesis, Departement of Chemical Engineering, MIT (1985)Google Scholar
  25. 25.
    Illinois Institute of Technology: GlucoSim: A web-based educational simulation package for glucose-insulin levels in the human body, Online,
  26. 26.
    Health Promotion Board Singapore, Online,
  27. 27.
    Tung, W.L., Teddy, S.D., Zhao, G.: Neuro-cognitive approaches to the control and regulation of insulin for the treatment of diabetes mellitus. Phase 1: Neurologically inspired modeling of the human glucose metabolic process. Technical Report C2i-TR-05/002, Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore (2005)Google Scholar

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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