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Reservoir computing is a bio-inspired computing paradigm for processingtime-dependent signals. Its hardware implementations have received muchattention because of their simplicity and remarkable performance on a series ofbenchmark tasks. In previous experiments the output was uncoupled from thesystem and in most cases simply computed offline on a post-processing computer.However, numerical investigations have shown that feeding the output back intothe reservoir would open the possibility of long-horizon time seriesforecasting. Here we present a photonic reservoir computer with outputfeedback, and demonstrate its capacity to generate periodic time series and toemulate chaotic systems. We study in detail the effect of experimental noise onsystem performance. In the case of chaotic systems, this leads us to introduceseveral metrics, based on standard signal processing techniques, to evaluatethe quality of the emulation. Our work significantly enlarges the range oftasks that can be solved by hardware reservoir computers, and therefore therange of applications they could potentially tackle. It also raises novelquestions in nonlinear dynamics and chaos theory.

Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation