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Self-organizing continuous attractor networks and motor function.

S M Stringer1, E T Rolls, T P Trappenberg

  • 1Department of Experimental Psychology, Centre for Computational Neuroscience, Oxford University, South Parks Road, Oxford OX1 3UD, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|March 12, 2003
PubMed
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This study demonstrates how neural networks can learn complex motor sequences using a dynamical systems approach and continuous attractor networks. The model shows that learned motor skills can be automatically selected and executed with consistent speed and force.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Robotics

Background:

  • Motor skill learning is crucial for autonomous agents.
  • Current models often lack mechanisms for automatic sequence selection and execution.
  • Visual sensory guidance plays a key role in training motor systems.

Purpose of the Study:

  • To present a dynamical systems model for learning complex motor sequences.
  • To demonstrate how neural networks can automatically select and perform motor sequences.
  • To investigate the role of 'trace' learning rules in temporal sequence acquisition.

Main Methods:

  • Utilized a dynamical systems perspective for motor control.
  • Employed a continuous attractor network architecture for path integration.

Related Experiment Videos

  • Implemented 'trace' learning rules incorporating temporal averages of cell activity.

    • Main Results:

    • The model successfully learned complex motor sequences.
    • Demonstrated automatic selection of motor sequences after training.
    • Showcased key features of motor function including arbitrary speed and force control.
    • Exhibited consistent relative time and force proportions across motor sequence parts.

    Conclusions:

    • Continuous attractor network models can effectively learn and execute motor sequences.
    • The proposed 'trace' learning mechanism enables temporal sequence learning.
    • This framework provides insights into the neural basis of motor skill acquisition and automatic execution.