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DY: Fachverband Dynamik und Statistische Physik

DY 7: Poster I

DY 7.10: Poster

Monday, March 11, 2013, 17:30–19:30, Poster C

Self-organized criticality in adaptive neural network models — •Matthias Rybarsch and Stefan Bornholdt — Institut für Theoretische Physik, Universität Bremen, Hochschulring 18, D-28359 Bremen

It has long been argued that neural networks have to establish and maintain a certain intermediate level of activity in order to keep away from the regimes of chaos and silence. Strong evidence for criticality has been observed in terms of spatio-temporal activity avalanches in cortical cultures first in ref. [1] and subsequently in many more experimental setups. These findings sparked intense research on theoretical models for criticality and avalanche dynamics in neural networks, where usually some dynamical order parameter is fed back onto the network topology by adapting the synaptic couplings. We here review and categorize two classes of models and also discuss a novel correlation-dependent mechanism for self-organized connectivity evolution, leading to a realistic distribution of avalanche sizes in agreement with the experimental findings [2].

[1] J.M. Beggs and D. Plenz: Neuronal Avalanches in Neocortical Circuits, J. Neurosci. 2003, 23(35):11167

[2] M. Rybarsch and S. Bornholdt: A minimal model for self-organization to criticality in binary neural networks, arXiv:1206.0166