  Reservoir computing is a bio-inspired computing paradigm for processing time
dependent signals. The performance of its analogue implementations matches
other digital algorithms on a series of benchmark tasks. Their potential can be
further increased by feeding the output signal back into the reservoir, which
would allow to apply the algorithm to time series generation. This requires, in
principle, implementing a sufficiently fast readout layer for real-time output
computation. Here we achieve this with a digital output layer driven by a FPGA
chip. We demonstrate the first opto-electronic reservoir computer with output
feedback and test it on two examples of time series generation tasks: frequency
and random pattern generation. We obtain very good results on the first task,
similar to idealised numerical simulations. The performance on the second one,
however, suffers from the experimental noise. We illustrate this point with a
detailed investigation of the consequences of noise on the performance of a
physical reservoir computer with output feedback. Our work thus opens new
possible applications for analogue reservoir computing and brings new insights
on the impact of noise on the output feedback.
