Dynamical synapses causing self-organized criticality in neural networks

2007/11/18 by Anna Levina, J. Michael Herrmann, T. Geisel +1 · 13 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Neural dynamics and brain function #cond-mat.dis-nn #cond-mat.stat-mech #q-bio.NC #stochastic dynamics and bifurcation

paper · pdf · doi:10.1038/nphys758

published as A. Levina, J. M. Herrmann, T. Geisel. Dynamical synapses causing self-organized criticality in neural networks, Nature Phys. 3, 857-860 (2007) · 9 pages, 4 figures

openalex publication_date 2007/11/18 · arxiv created 2007/12/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We show that a network of spiking neurons exhibits robust self-organized criticality if the synaptic efficacies follow realistic dynamics. Deriving analytical expressions for the average coupling strengths and inter-spike intervals, we demonstrate that networks with dynamical synapses exhibit critical avalanche dynamics for a wide range of interaction parameters. We prove that in the thermodynamical limit the network becomes critical for all large enough coupling parameters. We thereby explain experimental observations in which cortical neurons show avalanche activity with the total intensity of firing events being distributed as a power-law.

Citations

• Neural Networks with Dynamic Synapses

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