Dynamics of a two-layer neuronal network with asymmetry in coupling

Area of Science:

• Neuroscience
• Complex Network Theory
• Computational Neuroscience

Background:

• Understanding brain behavior relies on analyzing neuronal connectivity changes.
• Complex network theory and multi-layer networks offer realistic models for brain dynamics.
• Existing models often simplify inter-hemispheric communication.

Purpose of the Study:

• To examine the impact of asymmetric coupling on multi-layer neuronal network behavior.
• To model the communication between the left and right cerebral hemispheres using a two-layer network.
• To analyze how varying coupling strengths affect network dynamics and synchronization.

Main Methods:

• A two-layer neuronal network was constructed, modeling left and right hemispheres.
• The Hindmarsh-Rose chaotic model defined the dynamics of individual neurons.
• Asymmetric coupling strengths between the two layers were systematically varied and analyzed.

Main Results:

• Asymmetric coupling induced the emergence of different attractors, not present in the uncoupled model.
• Bifurcation diagrams illustrated significant variations in node dynamics with altered coupling.
• Network synchronization was achieved only with sufficiently large and symmetric coupling strengths.

Conclusions:

• Coupling asymmetry in multi-layer neuronal networks can lead to novel emergent behaviors.
• Synchronization in this model requires substantial symmetric coupling, indicating sensitivity to connection balance.
• The findings emphasize the critical role of balanced inter-hemispheric communication in brain function.