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This study models adaptive rewiring of neuronal connections using diffusion. It shows that this process robustly generates complex brain network structures, like modular or centralized topologies, in both binary and weighted networks.

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Systems Biology

Background:

  • Activity-dependent plasticity shapes neuronal connections.
  • Previous models focused on binary networks, showing development of modular/centralized topologies.
  • Realistic brain networks are weighted, requiring investigation into their plasticity mechanisms.

Purpose of the Study:

  • To investigate if adaptive rewiring models activity-dependent plasticity in weighted networks.
  • To determine the impact of network weighting on emergent structural complexity.
  • To identify key parameters governing the development of brain network topologies.

Main Methods:

  • Simulated adaptive rewiring based on neural activity represented as diffusion on a network.
  • Utilized both normally- and lognormally-distributed weighted networks.
  • Analyzed network evolution towards modular or centralized small-world topologies.

Main Results:

  • Weighted networks, like binary ones, evolved into modular or centralized topologies.
  • A single control parameter, representing homeostatic regulation, determined the prevailing topology.
  • Intermediate parameter values yielded the highest network complexity, integrating modular and centralized features.

Conclusions:

  • Diffusion-based adaptive rewiring is a parsimonious model for activity-dependent reshaping of brain connectivity.
  • The model successfully predicts the emergence of complex network structures in weighted neural networks.
  • Global regulation mechanisms play a crucial role in determining brain network architecture.