Recovery of computation capability for neural networks from damaged states using self-organized criticality

Self-organized criticality in adaptive neural networks

Analytical investigation of self-organized criticality in neural networks.

Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this syst...

Dynamical synapses causing self-organized criticality in neural networks

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 thermodyna...

Self-organized criticality in neural network models

Information processing by a network of dynamical elements is a delicate matter: Avalanches of activity can die out if the network is not connected enough or if the elements are not sensitive enough; on the other hand, activity avalanches can grow and spread over the entire network and override information processing as observed in epilepsy. Therefore, it has long been argued that neural network...

Controlling self-organized criticality in complex networks

A control scheme to reduce the size of avalanches of the Bak-TangWiesenfeld model on complex networks is proposed. Three network types are considered: those proposed by Erdős-Renyi, Goh-KahngKim, and a real network representing the main connections of the electrical power grid of the western United States. The control scheme is based on the idea of triggering avalanches in the highest degree no...