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2007

Intrinsic plasticity for reservoir learning algorithms

8 years 10 months ago
Intrinsic plasticity for reservoir learning algorithms
One of the most difficult problems in using dynamic reservoirs like echo state networks for signal processing is the choice of reservoir network parameters like connectivity or spectral radius of the weight matrix. In this article, we investigate the properties of an unsupervised intrinsic plasticity rule for signal specific adaptive shaping of the reservoir, which is local in space and time and aims at maximizing input–to–output information transmission for each neuron. We show that the rule consistently regulates the neurons’ mean outputs and variances and is robust to learning parameter changes. Simulations reveals that this reservoir adaptation robustly enhances online learning of Backpropagation–Decorrelation recurrent learning for a tenth–order nonlinear NARMA benchmark problem.
Marion Wardermann, Jochen J. Steil
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Marion Wardermann, Jochen J. Steil
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