Dynamical synapses causing self-organized criticality in neural networks

TL;DR

This paper demonstrates that neural networks with realistic dynamical synapses naturally evolve to a critical state exhibiting avalanche activity, aligning with experimental cortical neuron observations.

Contribution

It provides analytical insights into how dynamical synapses induce self-organized criticality in neural networks, explaining experimental avalanche phenomena.

Findings

Networks with dynamical synapses exhibit critical avalanche dynamics.

Criticality is achieved for a wide range of interaction parameters.

The network becomes critical in the thermodynamical limit for large coupling.

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.