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Cerebellar Climbing Fibers Anticipate Error in Motor Performance

  • Y. Burnod
  • M. Dufossé
  • A. A. Frolov
  • A. Kaladjian
  • S. Řízek
Conference paper

Abstract

The proposed formulation lies upon four main preliminaries, the Marr-Albus-Ito theory of cerebellar learning, the equilibrium point theory of motor control, the columnar organization of the cerebral cortex and the thoery of the differential neurocontroller. It is shown that: 1) any linear combination of cerebral motor commands which generates olivary signals is able to drive the cerebellar learning processes; 2) climbing fiber activities which supervise the cerebellar learning may originate from the generation of the cerebral commands, before any error of performance occurs, and thus early enough to improve these commands.

Keywords

Purkinje Cell Sensorimotor Cortex Parallel Fiber Inferior Olive Climbing Fiber 
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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References

  1. [1]
    Albus J.A.: Math. Eiosci. 10, 1971, 25–61.CrossRefGoogle Scholar
  2. [2]
    Asanuma H., Keller A.: Goncepts in Neurosci. 2(1), 1991, 1–30.Google Scholar
  3. [3]
    Baranyi A., Feher O.: Exp. Erain Res. 33, 1978, 283–298.Google Scholar
  4. [4]
    Burnod Y.: An adaptive neural networks: the cerebral cortex, Masson, Paris.Google Scholar
  5. [5]
    Burnod Y., Dufosse M.: In: “Brain and Space”, J. Paillard (Ed), Oxford Univ., pp 446–4Google Scholar
  6. [6]
    Daniel H., Levenes C., Crepel F.: Trends in Neurosci. 21(9), 1998, 401–407.CrossRefGoogle Scholar
  7. [7]
    Dufossé M., Ito M., Jastreboff P. J., Miyashita Y.: Brain Res. 150, 1978, 611–616.CrossRefGoogle Scholar
  8. [8]
    Georgopoulos A.P., Caminiti R., Kalaska J.F.: Brain Res. 54, 1984, 447–454.Google Scholar
  9. [9]
    Gilbert P.F.C., Thach W.T.: Brain Res. 128,1977, 309–328.CrossRefGoogle Scholar
  10. [10]
    Ito M.: The cerebellum and neural control. Raven Press, New York, 1984.Google Scholar
  11. [11]
    Ito M., Sakurai M., Tongroach P.: J. Physiol. (Lond.) 324, 1982, 113–134.Google Scholar
  12. [12]
    Kitazawa S., Kimura T., Yin P.B.: Nature 392, 1998, 494–497.CrossRefGoogle Scholar
  13. [13]
    Mano N.L., Kanazawa I., Yamamoto K.I.: Neurophysiol 56, 1986, 137–158Google Scholar
  14. [14]
    Marr D.: A theory of cerebellar cortex. J. Physiol. (Lond.) 202, 1979,437–470.Google Scholar
  15. [15]
    Nakano E., Imamizu H., Osu R., Gomi H., Yoshioka T., Kawato M.J.: Neurophysiol. 81, 1999, 2140–2155.Google Scholar
  16. [16]
    Rispal-Padel L., Pananceau M., Meftah E.M.: J. Physiol (Paris) 90, 1996, 373–379.Google Scholar
  17. [17]
    Řízek S., Fralov A.A.: Differential contral by neural networks. Neural Networks World, 4, 1994, 494–508.Google Scholar
  18. [18]
    Saint-Cyr J.A., Courville J.: In: The inferior olivary nucleus, Raven, 1980, 97–124.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Y. Burnod
  • M. Dufossé
    • 1
  • A. A. Frolov
    • 2
  • A. Kaladjian
    • 1
  • S. Řízek
    • 3
  1. 1.INSERM U483Univ. P.M. CurieParisFrance
  2. 2.Institute of Higher Nervous Activity and NeurophysiologyRussian Academy of SciencesMoscowRussia
  3. 3.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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