Modular topology emerges from plasticity in a minimalistic excitable network model

Area of Science:

• Computational neuroscience
• Network science
• Systems neuroscience

Background:

• Topological features are crucial for complex brain network dynamics and function.
• Modular organization is a common topological property observed across brain scales and species.
• Mechanisms generating and maintaining these topological features remain largely unknown.

Purpose of the Study:

• To investigate how local Hebbian plasticity rules influence global network topology.
• To explore the emergence of modular structures in brain network models.
• To understand the relationship between structural and functional connectivity.

Main Methods:

• Utilized a minimalistic network model with excitable nodes and discrete deterministic dynamics.
• Applied a local Hebbian plasticity rule to simulate network self-organization.
• Analyzed the resulting global network topology and its relation to network dynamics.

Main Results:

• A local Hebbian plasticity rule successfully reorganized global network topology into a modular structure.
• Structural reorganization led to enhanced correlations between structural and functional connectivity.
• The emergent network organization reflected characteristics of the underlying dynamical model.

Conclusions:

• Simple plasticity rules have the potential to generate complex topological features in brain networks.
• Local learning mechanisms can drive the formation of modular brain network architectures.
• This study provides insights into the self-organizing principles of brain connectivity.