Neural Network Model for Muscle Force Control Based on the Size Principle and Recurrent Inhibition of Renshaw Cells

  • Takanori Uchiyama
  • Kenzo Akazawa
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


A neural network model for muscle force control was constructed. The model contained a single motor-cortex output cell, the actual number of a motoneurons found in human muscles, Renshaw cells and muscle units. The size of the motor units (motoneurons and muscle units) was distributed as the human brachialis muscle, the extensor digitorum muscle and the first dorsal interosseous muscle. The relationship between the model’s muscle force and the firing rate of a motoneurons was investigated. The relationship depended on the absolute refractory time of a motoneurons, RIPSP by Renshaw cells and the firing pattern of Renshaw cells. When these parameters were selected appropriately, the model showed a relationship similar to that observed in isometric contraction of human skeletal muscles. The size distribution of the motor units had a dominant effects on the relationship.


Firing Rate Motor Unit Neural Network Model Muscle Force Isometric Contraction 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Akazawa K and Kato K. Neural network model for control of muscle force based on the size principle. Proc. IEEE 1990; 78:1531–1535CrossRefGoogle Scholar
  2. [2]
    Segundo JP, Perkel DG, Wyman H, Hegstad H and Moore GP. Input-output relations in computer-simulated nerve cells, Infuluence of the statistical properties, strength, number and inter-dependence of exitatory pre-synaptic terminals. Kybernetik 1968; 4:157–171CrossRefGoogle Scholar
  3. [3]
    Burke RE: motor units: anatomy, physiology, and functional organization. In: Handbook of Physiology, vol 2, Motor Control, American Physiology Society, Maryland, 1981, pp 345–422Google Scholar
  4. [4]
    Friedman WA, Sypert GW, Munson JB and Fleshman JW. Recurrent inhibition in type-identified motoneurons, J Neurophysiol 1981; 46:1349–1359Google Scholar
  5. [5]
    McCurdy ML and Hamm TM. Topography of recurrent inhibitory postsynaptic potentials between individual motoneurons in the cat, J Neurophysiol 1994; 72:214–226Google Scholar
  6. [6]
    van Keulen L. Autogenetic recurrent inhibition of individual spinal motoneu-rones of th cat, Neurosci Lett 1981; 21:297–300CrossRefGoogle Scholar
  7. [7]
    Hultborn H, Katz R and Mackel R. Distribution of recurrent inhibition within a motor nucleus. II. Amount of recurrent inhibition in motoneurones to fast and slow units, Acta Physiol Scand 1988; 134:363–374CrossRefGoogle Scholar
  8. [8]
    Kanosue K, Yoshida M, Akazawa K and Fujii K. The number of active motor units and their firing rates in voluntary contraction of human brachialis muscle. Jpn J Physiol 1979; 29:427–443CrossRefGoogle Scholar
  9. [9]
    Monster AW and Chan H. Corticospinal neurons with a special role in precision grip. J Neurophysiol 1977; 40:1432–1443Google Scholar
  10. [10]
    De Luca CJ, Le Fever MP, McCue MP and Xenakis AP. Control scheme governing concurrently active human motor units during voluntary contractions. J Physiol 1982; 329:113–128Google Scholar

Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Takanori Uchiyama
    • 1
  • Kenzo Akazawa
    • 2
  1. 1.Fac. Science and TechnologyKeio UniversityYokohamaJapan
  2. 2.Fac. EngineeringKobe UniversityKobeJapan

Personalised recommendations