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Self-organized critical neural networks.

Stefan Bornholdt1, Torsten Röhl

  • 1Institute for Theoretical Physics, University of Kiel, Germany. bornholdt@izbi.uni-leipzig.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 26, 2005
PubMed
Summary

This study explores how model neural networks self-organize connectivity. A local rewiring rule, based on correlated neuron activity, drives robust self-organization towards an order-disorder transition.

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Systems Biology

Background:

  • Neural network connectivity is crucial for information processing.
  • Understanding self-organization principles in neural networks is key to deciphering brain function.
  • Existing models often lack mechanisms for dynamic, activity-dependent rewiring.

Purpose of the Study:

  • To investigate a mechanism for self-organization of network connectivity.
  • To explore how local rules can lead to global network structure.
  • To understand the relationship between connectivity and dynamic phase transitions in neural networks.

Main Methods:

  • Studied a two-dimensional neural network model with asymmetric weights.
  • Implemented a local rewiring rule based on correlated neuronal activity.
  • Analyzed the network's dynamics and phase transitions between ordered and disordered states.

Main Results:

  • Demonstrated robust self-organization of network connectivity.
  • Showed convergence towards an order-disorder transition, independent of initial conditions.
  • Confirmed robustness against thermal noise and the need for parameter fine-tuning.

Conclusions:

  • A local, activity-dependent rewiring rule can drive global self-organization of neural network connectivity.
  • This mechanism facilitates robust emergence of network topology near a critical transition.
  • The findings offer insights into synaptic plasticity and network development.

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