Abstract
The echo state network (ESN) has recently been proposed for modeling complex dynamic systems. The ESN is a sparsely connected recurrent neural network with most of its weights fixed a priori to randomly chosen values. The only trainable weights are those on links connected to the outputs. The ESN can demonstrate remarkable performance after seemingly effortless training. This brief paper discusses ESN in a broader context of applications of recurrent neural networks (RNN) and highlights challenges on the road to practical applications.
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