Optimizing Reservoir Computing for Reconstructing Ergodic Properties

Abstract

Reservoir computing is a powerful framework for modeling dynamical systems due to its universality and computational efficiency. However, a major challenge is achieving a forecast with accurate long-time statistics, or climate, which is essential for inferring ergodic properties such as Lyapunov exponents. A common approach is to optimize the reservoir's macroscopic parameters, such as the spectral radius, by maximizing prediction time. But here we show that even predictions accurate over multiple Lyapunov times do not guarantee the correct long-time statistics. Instead, we choose reservoir properties by minimizing the error in the reconstructed invariant distribution (or its projections), which is easily available from data. We demonstrate that this approach reproduces the Lyapunov exponents of model dynamical systems, including the logistic and standard maps, as well as the double pendulum, even with partial observations. We further show that recurrent connections, and resulting reservoir memory, are only required in the partially-observed case. We introduce a temporal scaling which reliably separates system and reservoir dynamics. In the posture time series of the nematode C. elegans we show that our approach quantitatively reproduces a chaotic behavioral attractor, but this requires a further constraint on the maximal conditional Lyapunov exponent to ensure the reservoir remains consistently synchronized to the complex biological input.