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Neural network models of bilateral coordination.

D S Farrar1, D Zipser

  • 1Department of Cognitive Science, University of California, San Diego, USA.

Biological Cybernetics
|April 8, 1999
PubMed
Summary
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This study presents two arm movement control methods: a motion planner for complex movements and neural networks that efficiently emulate it. The research offers insights into brain systems for bilateral coordination.

Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Neuroscience

Background:

  • Controlling robotic arm movement, especially for noncolliding, goal-directed tasks, is computationally challenging.
  • Existing motion planning algorithms can be resource-intensive.

Purpose of the Study:

  • To describe two distinct mechanisms for controlling a pair of arms.
  • To develop computationally efficient methods for coordinated arm movement.
  • To provide insights into neural mechanisms of bilateral coordination.

Main Methods:

  • An engineered motion planner was developed to solve the problem of noncolliding, goal-directed arm movements.
  • Neural networks were trained to emulate the functional behavior of the motion planner.
  • Computational resource requirements were compared between the two methods.

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Main Results:

  • The engineered motion planner successfully generated solutions for complex arm movements.
  • Neural networks effectively emulated the motion planner's coordinated behaviors.
  • The neural network approach demonstrated significantly reduced computational resource demands.

Conclusions:

  • Neural networks offer a computationally efficient alternative for emulating complex motion planning.
  • The study provides a framework for understanding brain systems involved in bilateral coordination.
  • Testable predictions regarding neural response properties in bilateral coordination are proposed.