Fundamental performance bounds on time-series generation using reservoir computing

Abstract

Reservoir computing (RC) harnesses the intrinsic dynamics of a chaotic system, called the reservoir, to perform various time-varying functions. An important use-case of RC is the generation of target temporal sequences via a trainable output-to-reservoir feedback loop. Despite the promise of RC in various domains, we lack a theory of performance bounds on RC systems. Here, we formulate an existence condition for a feedback loop that produces the target sequence. We next demonstrate that, given a sufficiently chaotic neural network reservoir, two separate factors are needed for successful training: global network stability of the target orbit, and the ability of the training algorithm to drive the system close enough to the target, which we term `reach'. By computing the training phase diagram over a range of target output amplitudes and periods, we verify that reach-limited failures depend on the training algorithm while stability-limited failures are invariant across different algorithms. We leverage dynamical mean field theory (DMFT) to provide an analytical amplitude-period bound on achievable outputs by RC networks and propose a way of enhancing algorithm reach via forgetting. The resulting mechanistic understanding of RC performance can guide the future design and deployment of reservoir networks.