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 ofrandomly 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 complexity measure predicts the computational capabilities very well: only near the edge of chaos are such networks able to perform complex computations on time series. Additionally asimple synaptic scaling rule for self-organized criticality is presented and analyzed.