Self-organized critical neural networks

    Phys. Rev. E 67, 066118 – Published 27 June 2003
    Stefan Bornholdt and Torsten Röhl

    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.

    DOI: http://dx.doi.org/10.1103/PhysRevE.67.066118

    • Received 2 May 2002
    • Revised 11 April 2003
    • Published 27 June 2003

    © 2003 The American Physical Society

    Authors & Affiliations

    Stefan Bornholdt1,2,* and Torsten Röhl1

    • 1Institute for Theoretical Physics, University of Kiel, Leibnizstrasse 15, D-24098 Kiel, Germany
    • 2Interdisciplinary Center for Bioinformatics, University of Leipzig, Kreuzstrasse 7b, D-04103 Leipzig, Germany

    • *Email address: bornholdt@izbi.uni-leipzig.de

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