Symmetry breaking and avalanche shapes in modular neural networks

Area of Science:

• Computational Neuroscience
• Network Science
• Theoretical Neuroscience

Background:

• Brain networks exhibit modular organization across various scales.
• Understanding the relationship between network structure and brain dynamics is crucial.

Purpose of the Study:

• Investigate the stochastic Wilson-Cowan model on a modular network with varying synaptic strengths.
• Explore the phase diagram and critical behaviors arising from modular network architecture.

Main Methods:

• Simulated the Wilson-Cowan model on a network with distinct intra-module and inter-module synaptic strengths.
• Analyzed the system's phase diagram, identifying symmetric and broken symmetry phases.
• Examined critical points and avalanche dynamics along transition lines.

Main Results:

• Identified symmetric (low/high activity) and broken symmetry (subset active) phases.
• Discovered two critical lines exhibiting power-law avalanche distributions.
• Observed distinct avalanche shapes (symmetric, right-skewed, left-skewed) along critical lines.

Conclusions:

• Modular network structure and synaptic differences generate complex brain dynamics.
• The model explains both symmetric and left-skewed neural avalanche shapes observed experimentally.
• Links modular brain organization to critical dynamics and provides a theoretical framework.