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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
Published on: September 23, 2025
Numerically evaluated functional equivalence between chaotic dynamics in neural networks and cellular automata under
Ryu Takada1, Daigo Munetaka, Shoji Kobayashi
1Department of Electronic & Information System Engineering, The Graduate School of Natural Science & Technology, Okayama University, 700-8530, Okayama, Japan.
Cognitive Neurodynamics
|November 13, 2008
Summary
Harnessing chaotic dynamics in recurrent neural networks and cellular automata enables complex control. Adaptive switching between chaotic regimes successfully solved maze problems, outperforming random methods.
Area of Science:
- Computational neuroscience
- Complex systems science
- Artificial intelligence
Background:
- Chaotic dynamics offer potential for complex computations.
- Recurrent neural networks (RNNs) and cellular automata (CA) exhibit chaotic behavior.
- Harnessing chaos for control remains an area of exploration.
Purpose of the Study:
- To investigate chaotic dynamics in RNNs and 2D CAs for complex function control.
- To propose a method for controlling complex problems by adaptive switching between chaotic regimes.
- To demonstrate the efficacy of harnessing chaos in solving ill-posed problems, such as maze navigation.
Main Methods:
- Investigated chaotic dynamics in a finite, large-degree-of-freedom RNN model and 2D CAs.
- Applied chaotic dynamics to motion control tasks, specifically solving a 2D maze.
- Utilized adaptive switching between weakly and strongly chaotic regimes for problem-solving.
- Conducted computer simulations and functional simulations over 300 trials.
Main Results:
- Chaotic dynamics in both systems generated autonomous complex patterns, itinerating between attractors.
- The proposed method successfully solved the 2D maze problem.
- Success rates in maze-solving significantly surpassed those of a random number generator.
- Both RNN and CA systems demonstrated equivalent functional aspects for harnessing chaos.
Conclusions:
- Chaotic dynamics in RNNs and CAs can be harnessed for complex function control.
- Adaptive switching between chaotic regimes provides a viable strategy for solving complex, ill-posed problems.
- These findings highlight the potential of chaos-based computation in AI and control systems.
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