Self-organized criticality in a realistic model of adaptive neural networks

C. Meisel and T. Gross
Physical Review E 80(6), 061917, 2009.

Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing in the brain should operate close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how the brain can remain close to a critical point while undergoing continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf which pointed to a generic mechanism of robust self-organized criticality in adaptive networks. Here we address the question whether this mechanism is relevant for real neural networks; we show in a realistic model that synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength. These results suggest that the interplay between dynamics and topology is essential for neural information processing.

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