Emergence and Criticality in Spatiotemporal Synchronization: The Complementarity Model

Area of Science:

• Complex Systems
• Artificial Life
• Computational Neuroscience

Background:

• Investigates collective excitability and critical events in a novel spatiotemporal many-body model.
• Explores a new paradigm for Artificial Life, focusing on emergent dynamic network structures.

Purpose of the Study:

• To analyze the long-term collective excitability properties and statistical characteristics of critical events.
• To understand the emergent dynamic network behaviors, including their life cycle (self-creation, homeostasis, self-destruction).

Main Methods:

• Employs numerical simulations to observe and quantify spatiotemporal dynamics.
• Analyzes power spectra of collective parameters and statistical properties of activity bursts (avalanches).

Main Results:

• Excitable collective structures form dynamic networks via spatiotemporal activity bursts (avalanches) near a synchronization phase transition.
• Observed network life cycles (self-creation, homeostasis, self-destruction) and 1/f power law tails in collective parameters.
• Avalanche size and duration statistics align with experimental findings in neural networks.

Conclusions:

• The model demonstrates self-organized criticality principles through local-to-collective excitability mechanisms.
• Findings link Artificial Life models to neural network dynamics and self-organized criticality research.