Training Echo State Networks with Neuroscale

We create an artificial neural network which is a version of echo state machines, ESNs. ESNs are recurrent neural networks but unlike most recurrent networks, they come with an efficient training method. We adapt this method using ideas from neuroscale so that the network is optimal for projecting multivariate time series data onto a low dimensional manifold so that the structure in the time series can be identified by eye. We illustrate the resulting projections on real and artificial data.