Unifying quality metrics for reservoir networks

Several metrics for the quality of reservoirs have been proposed and linked to reservoir performance in Echo State Networks and Liquid State Machines. A method to visualize the quality of a reservoir, called the separation ratio graph, is developed from these existing metrics leading to a generalized approach to measuring reservoir quality. Separation ratio provides a method for estimating the components of a reservoir´s separation and visualizing the transition from stable to chaotic behavior. This new approach does not require a prior classification of input samples and can be applied to reservoirs trained with unsupervised learning. It can also be used to analyze systems made up of multiple reservoirs to determine performance between any two points in the system.