Dynamical synapses causing self-organized criticality in neural networks

Abstract

Self-organized criticality(1) is one of the key concepts to describe the emergence of complexity in natural systems. The concept asserts that a system self-organizes into a critical state where system observables are distributed according to a power law. Prominent examples of self-organized critical dynamics include piling of granular media(2), plate tectonics(3) and stick-slip motion(4). Critical behaviour has been shown to bring about optimal computational capabilities(5), optimal transmission(6), storage of information(7) and sensitivity to sensory stimuli(8-10). In neuronal systems, the existence of critical avalanches was predicted(11) and later observed experimentally(6,12,13). However, whereas in the experiments generic critical avalanches were found, in the model of ref. 11 they only show up if the set of parameters is fine-tuned externally to a critical transition state. Here, we demonstrate analytically and numerically that by assuming (biologically more realistic) dynamical synapses(14) in a spiking neural network, the neuronal avalanches turn from an exceptional phenomenon into a typical and robust self-organized critical behaviour, if the total resources of neurotransmitter are sufficiently large.