Non-dissipative Reservoir Computing Approaches for Time-Series Classification

Reservoir Computing (RC) is a consolidated framework for designing fastly trainable recurrent neural systems, where the dynamical component is fixed and initialized to implement a fading memory over the input signal. In this paper, we study the behavior of a recently introduced class of alternative RC approaches in which the fixed dynamical component implements a stable but non-dissipative system, so that the driving temporal signal can be propagated through multiple time steps effectively. We analyze the behavior of two classes of non-dissipative RC in terms of dynamical stability and show the resulting advantages in time-series classification tasks in comparison to conventional RC.