A local Echo State Property through the largest Lyapunov exponent. (April 2016)

• Record Type: Journal Article
• Authors: Wainrib, Gilles; Galtier, Mathieu N.
• Is Part Of: Neural networks. Volume 76(2016)
• Page Start: 39
• Page End: 45
• Publication Date: 2016-04
• Subjects: Reservoir computing -- Mean field theory -- Lyapunov exponents -- Echo State Networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.12.013
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Echo State Networks are efficient time-series predictors, which highly depend on the value of the spectral radius of the reservoir connectivity matrix. Based on recent results on the mean field theory of driven random recurrent neural networks, enabling the computation of the largest Lyapunov exponent of an ESN, we develop a cheap algorithm to establish a local and operational version of the Echo State Property.