  Reservoir Computing is a bio-inspired computing paradigm for processing time
dependent signals. The performance of its analogue implementation are
comparable to other state of the art algorithms for tasks such as speech
recognition or chaotic time series prediction, but these are often constrained
by the offline training methods commonly employed. Here we investigated the
online learning approach by training an opto-electronic reservoir computer
using a simple gradient descent algorithm, programmed on an FPGA chip. Our
system was applied to wireless communications, a quickly growing domain with an
increasing demand for fast analogue devices to equalise the nonlinear distorted
channels. We report error rates up to two orders of magnitude lower than
previous implementations on this task. We show that our system is particularly
well-suited for realistic channel equalisation by testing it on a drifting and
a switching channels and obtaining good performances
