Measuring asymmetric temporal interdependencies in simulated and biological networks

Area of Science:

• Complex systems science
• Network science
• Dynamical systems theory

Background:

• Characterizing temporal interdependencies in complex networks is crucial for understanding system dynamics.
• Existing metrics may not fully capture asymmetric relationships within temporal data.
• Investigating coupled dynamical elements requires robust analytical tools.

Purpose of the Study:

• To introduce and validate a novel metric for quantifying asymmetric temporal interdependencies.
• To explore the emergence of temporal ordering in networks of coupled dynamical elements.
• To apply this metric to both model systems and biological neural networks.

Main Methods:

• Development of a new metric to characterize asymmetric temporal interdependencies.
• Simulation of coupled Rossler oscillators with varying connectivity and topologies.
• Application of the metric to analyze the functional structure of the Helix snail's cerebral ganglia network.

Main Results:

• Demonstrated the evolution of local temporal ordering to global ordering based on network structure.
• Showcased the spontaneous emergence of functional interdependence between electrode groups in a biological network.
• Validated the metric's efficacy in diverse network contexts.

Conclusions:

• The newly developed metric effectively characterizes asymmetric temporal interdependencies in complex networks.
• The study provides insights into the formation and evolution of temporal order in both artificial and biological systems.
• This metric offers a powerful tool for analyzing functional connectivity and emergent dynamics.