As a family member of Recurrent Neural Networks and similar to Long-Short-Term Memory cells, Echo State Networks (ESNs) are capable of solving temporal tasks, but with a substantially easier training paradigm based on linear regression. However, optimizing hyper-parameters and efficiently implementing the training process might be somewhat overwhelming for the first-time users of ESNs. This paper aims to facilitate the understanding of ESNs in theory and practice. Treating ESNs as non-linear filters, we explain the effect of the hyper-parameters using familiar concepts such as impulse responses. Furthermore, the paper introduces the Python toolbox PyRCN (Python Reservoir Computing Network) for developing, training and analyzing ESNs on arbitrarily large datasets. The tool is based on widely-used scientific packages, such as numpy and scipy and offers an interface to scikit-learn. Example code and results for classification and regression tasks are provided.
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