Reservoir Computing by Discretizing ODEs

We draw connections between Reservoir Computing (RC) and Ordinary Differential Equations, introducing a novel class of models called Euler State Networks (EuSNs). The proposed approach is featured by system dynamics that are both stable and non-dissipative, hence enabling an effective transmission of input signals over time. At the same time, EuSN is featured by untrained recurrent dynamics, preserving all the computational advantages of RC models. Through experiments on several benchmarks for time-series classification, we empirically show that EuSN can substantially narrow the performance gap between RC and fully trainable recurrent neural networks.