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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)

Abstract

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.

Keywords

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.

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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

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