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Constrained chaos in three-module neural network enables to execute multiple tasks simultaneously.

Shigetoshi Nara1, Ken-Ichiro Soma1, Yutaka Yamaguti2

  • 1Dept. Electrical & Electronic Engineering, Okayama University, Okayama 700-8530, Japan.

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|January 1, 2020
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Summary
This summary is machine-generated.

Introducing constrained chaos into a three-module neural network enables simultaneous multiple task execution. This neural network with chaotic dynamics can control robot actuators for complex operations.

Keywords:
Neural chaosRedundant codingSimultaneous multiple tasks executionThree-module network

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Area of Science:

  • Computational neuroscience
  • Robotics
  • Chaos theory

Background:

  • Neural networks with feedforward inter-module structure can potentially perform multiple tasks.
  • Constrained chaos can be introduced into neural networks to explore novel functionalities.

Purpose of the Study:

  • To investigate the potential of constrained chaos in a three-module neural network for simultaneous multiple task execution.
  • To apply this concept to a robot actuator control system.

Main Methods:

  • A three-module neural network (sensory, inter, motor) with binary neurons was designed.
  • Chaos was introduced by reducing intra- and inter-module connectivity.
  • Computer experiments were used to simulate a robot actuator controlled by the network.

Main Results:

  • The system generated chaotic motion signals in the motor module.
  • Simultaneous application of two distinct inputs resulted in the simultaneous execution of two corresponding tasks.
  • Chaotic activities persisted even under strong dual-input conditions.

Conclusions:

  • Constrained chaos in redundant neural systems facilitates simultaneous multiple task execution, mimicking brain capabilities.
  • This approach offers a novel method for developing sophisticated robot control systems.