Self-organized critical neural networks

S. Bornholdt and T. Röhl
Physical Review E 67, 066118, 2003.

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.

This paper in Physical Review E

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