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Motor Skill Development and Neural Networks for Position Code Invariance under Speed and Compliance Rescaling

  • Daniel Bullock
  • Stephen Grossberg
Part of the NATO ASI Series book series (ASID, volume 56)

Abstract

This chapter presents two neural network modules capable of providing a secure foundation for safe self-organization of readily generalized movement skills. Called VITE and FLETE, these networks ensure position-code invariance under speed and compliance rescaling, respectively. This invariance property enables use of a simple strategy for skill development: For safety, we begin skill learning while performing at relatively low speed with relatively low limb compliance. Once learning guided by error feedback has reduced positioning errors, we increase speed and compliance. The invariance properties ensure that the shift to new values of the speed and compliance control signals will not require relearning. Both neural network models and the developmental strategy are compatible with, and help organize, large bodies of existing data. The FLETE network constitutes a comprehensive new model of the mammalian spino-muscular system.

Keywords

Joint Angle Muscle Length Difference Vector Joint Rotation Back Propagation Network 
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

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Daniel Bullock
    • 1
  • Stephen Grossberg
    • 1
  1. 1.Program in Cognitive and Neural SystemsBoston UniversityBostonUSA

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