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Noise-driven attractor switching device.

Naoki Asakawa1, Yasushi Hotta, Teruo Kanki

  • 1Institute of Scientific and Industrial Research, Osaka University, Mihogaoka, Ibaraki, Osaka, Japan. asakawa@sanken.osaka-u.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 28, 2009
PubMed
Summary
This summary is machine-generated.

Artificial neural networks can improve adaptability to environmental changes using stochastically excitable threshold units. These units create noise-stabilized attractors, enhancing system resilience and exhibiting hysteresis similar to human vision.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Dynamical systems

Background:

  • Artificial neural networks (ANNs) struggle with adaptability due to deterministic nature and fixed prior learning.
  • Unpredictable environmental changes pose significant challenges for current ANN models.

Purpose of the Study:

  • To investigate the use of stochastically excitable threshold units to enhance ANN adaptability.
  • To explore noise-driven mechanisms for stabilizing and switching system states.

Main Methods:

  • Implementation of an excitable threshold system to create quasiequilibrium states (attractors).
  • Utilizing inhibitory connections and noise to stabilize and switch attractors.
  • Modeling a ring network with class 1 neurons and inhibitory connections.

Main Results:

  • The excitable threshold system demonstrated partial overcoming of environmental change.
  • Noise was shown to stabilize and switch attractors, inducing hysteresis.
  • The ring network model exhibited properties similar to the threshold system.

Conclusions:

  • Stochastically excitable threshold units offer a pathway to improve ANN adaptability.
  • Noise-driven dynamics and hysteresis are key mechanisms for robust system behavior.
  • The findings have implications for developing more resilient AI and understanding biological neural systems.