Artificial Neural Networks/Echo State Networks
Echo state networks are recurrent networks where the hidden layer neurons are not completely connected to all input neurons. Such networks where all possible connections are not made are known as sparsely connected networks. Only the weights from the hidden layer to the output layer may be altered during training.
Echo state networks are useful for matching and reproducing specific input patterns. Because the only tap weights modified during training are the output layer tap weights, training is typically quick and computationally efficient in comparison to other multi-layer networks that are not sparsely connected.