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Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

Gail A. Carpenter1

  • 1Boston University, Boston, U.S.A.

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
Summary
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New distributed Adaptive Resonance Theory (ART) models offer stable, fast learning without forgetting. These models combine ART

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Traditional Adaptive Resonance Theory (ART) models face limitations with code representations.
  • Winner-take-all ART systems offer stable learning but lack noise tolerance.
  • Multilayer perceptrons provide noise tolerance and code compression but can suffer from catastrophic forgetting.

Purpose of the Study:

  • Introduce a novel class of distributed Adaptive Resonance Theory (ART) models.
  • Enhance learning, recognition, and prediction capabilities using distributed code representations.
  • Combine the strengths of winner-take-all ART and multilayer perceptrons.

Main Methods:

  • Developed distributed ART neural networks (dART and dARTMAP) with distributed code representations.
  • Replaced traditional path weights with dynamic weights based on coding node activation and adaptive thresholds.

Related Experiment Videos

  • Implemented a parallel distributed match-reset-search process for memory stabilization.

    • Main Results:

    • Distributed ART models enable fast and slow learning without catastrophic forgetting by apportioning learned changes.
    • Dynamic weights exhibit behavior resembling long-term potentiation (LTP) and long-term depression (LTD) based on input frequency.
    • The match-reset-search system stabilizes memory, preventing dART from becoming a simple distributed competitive learning network.

    Conclusions:

    • Distributed ART models offer a robust framework for learning, recognition, and prediction with enhanced stability and flexibility.
    • The novel dynamic weight mechanism and adaptive thresholds contribute to overcoming limitations of previous ART architectures.
    • These models present a significant advancement in artificial neural networks for complex cognitive tasks.