  We review attempts that have been made towards understanding the
computational properties and mechanisms of input-driven dynamical systems like
RNNs, and reservoir computing networks in particular. We provide details on
methods that have been developed to give quantitative answers to the questions
above. Following this, we show how self-organization may be used to improve
reservoirs for better performance, in some cases guided by the measures
presented before. We also present a possible way to quantify task performance
using an information-theoretic approach, and finally discuss promising future
directions aimed at a better understanding of how these systems perform their
computations and how to best guide self-organized processes for their
optimization.
