Echo state network - Wikipedia, the free encyclopedia

http://en.wikipedia.org/wiki/Echo_state_network

The echo state network (ESN) is a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are randomly assigned and are fixed. The weights of output neurons can be learned so that the network can (re)produce specific temporal patterns.

The “echo state” approach to analysing and training recurrent neural networks – with an Erratum note

http://web.info.uvt.ro/~dzaharie/cne2013/proiecte/tehnici/ReservoirComputing/EchoStatesTechRep.pdf

This is a corrected version of the technical report H. Jaeger(2001): The ”echo state” approach to analysing and training recurrent neural networks. GMD Report 148, German National Research Center for Information Techno logy, 2001.

An overview of reservoir computing: theory, applications and implementations

https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2007-8.pdf

This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.

Frontiers | MACOP modular architecture with control primitives | Frontiers in Computational Neuroscience

http://journal.frontiersin.org/article/10.3389/fncom.2013.00099/full

2.4.2. Echo state networks
We use an Echo State Network (ESN) (Jaeger, 2001) as inverse model. An ESN is composed of a discrete-time recurrent neural network [commonly called the reservoir because ESNs belong to the class of Reservoir Computing techniques (Schrauwen et al., 2007)] and a linear readout layer which maps the state of the reservoir to the desired output.

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http://journal.frontiersin.org/article/10.3389/fncom.2013.00099/pdf