Can potentially useful dynamics to solve complex problems emerge from constrained chaos and/or chaotic itinerancy?

Area of Science:

• Complex Systems Science
• Computational Neuroscience
• Dynamical Systems Theory

Background:

• Biological systems, including the brain, exhibit complex dynamics with large degrees of freedom.
• Understanding these dynamics is crucial for explaining complex functioning, control, and natural structure formation.

Purpose of the Study:

• To investigate the role of complex dynamics, specifically chaos, in systems with large but finite degrees of freedom.
• To explore the potential applications of these dynamics in biological systems and artificial intelligence.

Main Methods:

• Computer experiments using a recurrent neural network model.
• Numerical analysis, including calculation of correlation functions between neurons and basin visiting measures.
• Functional experiments, such as executing a memory search task in an ill-posed context.

Main Results:

• Demonstrated complex dynamics, including instabilities, itinerancies, and localization in state space.
• Showcased the utility of "constrained chaos" (or "chaotic itinerancy") in a memory search task.
• Identified these dynamics as a balance between converging and diverging states in high-dimensional space.

Conclusions:

• Constrained chaos is potentially useful for complex functioning and control in biological systems.
• These dynamics operate in a delicate balance, adapting to situational context.
• Chaotic itinerancy offers a framework for understanding and engineering complex adaptive systems.