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

TL;DR

This paper introduces a photonic reservoir computer with output feedback capable of generating periodic signals and emulating chaotic systems, advancing hardware-based time series processing and nonlinear dynamics research.

Contribution

It presents the first implementation of a photonic reservoir computer with output feedback for complex time series generation and chaotic system emulation.

Findings

Successfully generated periodic time series.

Emulated chaotic systems with high fidelity.

Analyzed noise effects on system performance.

Abstract

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.