Brain-Inspired Photonic Signal Processor for Generating Periodic Patterns and Emulating Chaotic Systems

Reservoir computing is a bioinspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks. In previous experiments, the output was uncoupled from the system and, in most cases, simply computed off-line on a postprocessing computer. However, numerical investigations have shown that feeding the output back into the reservoir opens the possibility of long-horizon time-series forecasting. Here, we present a photonic reservoir computer with output feedback, and we demonstrate its capacity to generate periodic time series and to emulate chaotic systems. We study in detail the effect of experimental noise on system performance. In the case of chaotic systems, we introduce several metrics, based on standard signal-processing techniques, to evaluate the quality of the emulation. Our work significantly enlarges the range of tasks that can be solved by hardware reservoir computers and, therefore, the range of applications they could potentially tackle. It also raises interesting questions in nonlinear dynamics and chaos theory.