Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation
This work expands the range of tasks for hardware reservoir computers, potentially broadening their applications in signal processing and nonlinear dynamics.
The authors tackled the challenge of enabling hardware reservoir computers to perform long-horizon time series forecasting by implementing a photonic reservoir computer with output feedback, demonstrating its ability to generate periodic patterns and emulate chaotic systems while studying noise effects.
Reservoir computing is a bio-inspired 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 offline on a post-processing computer. However, numerical investigations have shown that feeding the output back into the reservoir would open the possibility of long-horizon time series forecasting. Here we present a photonic reservoir computer with output feedback, and 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, this leads us to 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 novel questions in nonlinear dynamics and chaos theory.