At the Edge of Chaos: Real-time Computations and Self-Organized
Criticality in Recurrent Neural Networks
T. Natschlaeger, N. Bertschinger, and R. Legenstein
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 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 a simple synaptic
scaling rule for self-organized criticality is presented and analyzed.
Reference: T. Natschlaeger, N. Bertschinger, and R. Legenstein.
At the edge of chaos: Real-time computations and self-organized criticality in
recurrent neural networks.
In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural
Information Processing Systems 17, pages 145-152. MIT Press, Cambridge, MA,