At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
|Title||At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks|
|Booktitle||Proc. of NIPS 2004, Advances in Neural Information Processing Systems|
The computational capabilites of randomly connected networks of threshold gates in the time-series domain are analyzed. 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 capabilites 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.