A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.
%0 Journal Article
%1 Bornholdt2003
%A Bornholdt, Stefan
%A Röhl, Torsten
%D 2003
%I American Physical Society
%J Phys. Rev. E
%K criticality evolution networks soc
%N 6
%P 066118
%R 10.1103/PhysRevE.67.066118
%T Self-organized critical neural networks
%V 67
%X A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.
@article{Bornholdt2003,
abstract = {A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.},
added-at = {2011-01-13T13:25:35.000+0100},
author = {Bornholdt, Stefan and Röhl, Torsten},
biburl = {https://www.bibsonomy.org/bibtex/21c01487ef4c05d569c0bdeca53d9f146/rincedd},
doi = {10.1103/PhysRevE.67.066118},
file = {Bornholdt2003 - Self-organized critical neural networks.pdf:Bornholdt2003 - Self-organized critical neural networks.pdf:PDF},
groups = {public},
interhash = {fc1660b5e109fa9e80423d2179aa3078},
intrahash = {1c01487ef4c05d569c0bdeca53d9f146},
journal = {Phys. Rev. E},
keywords = {criticality evolution networks soc},
number = 6,
pages = 066118,
publisher = {American Physical Society},
timestamp = {2011-03-30T16:38:26.000+0200},
title = {Self-organized critical neural networks},
username = {rincedd},
volume = 67,
year = 2003
}