Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals (Jaeger and Haas 2004; Maass, Natschläger, and Markram 2002). It can be easily implemented in hardware. The performance of these analogue devices matches digital algorithms on a series of benchmark tasks (see e.g. (Soriano et al. 2015) for a review). Their capacities could be extended by feeding the output signal back into the reservoir, which would allow them to be applied to various signal generation tasks (Antonik et al. 2016b). In practice, this requires a highspeed readout layer for real-time output computation. Here we achieve this by means of a field-programmable gate array (FPGA), and demonstrate the first photonic reservoir computer with output feedback. We test our setup on the Mackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement. Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications for photonic reservoir computing.