Analytical investigation of self-organized criticality in neural networks.

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 adaptive neural networks

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...

At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks

In this paper we analyze the relationship between the computational capabilities of randomly connected networks of threshold gates in the timeseries domain and their dynamical properties. In particular we propose a complexity measure which we find to assume its highest values near the edge of chaos, i.e. the transition from ordered to chaotic dynamics. Furthermore we show that the proposed comp...

Punctuated Equilibrium in Software Evolution

An approach based on the paradigm of self-organized criticality is proposed for experimental investigation and theoretical modeling of software evolution. The dynamics of modifications is studied for three free, open source programs MOZILLA, FREE-BSD, and EMACS using the data from version control systems. Scaling laws typical for self-organized criticality found. A model of software evolution p...