  Introduction. Reservoir Computing is a bio-inspired computing paradigm for
processing time-dependent signals. The performance of its hardware
implementation is comparable to state-of-the-art digital algorithms on a series
of benchmark tasks. The major bottleneck of these implementation is the readout
layer, based on slow offline post-processing. Few analogue solutions have been
proposed, but all suffered from notice able decrease in performance due to
added complexity of the setup. Methods. Here we propose the use of online
training to solve these issues. We study the applicability of this method using
numerical simulations of an experimentally feasible reservoir computer with an
analogue readout layer. We also consider a nonlinear output layer, which would
be very difficult to train with traditional methods. Results. We show
numerically that online learning allows to circumvent the added complexity of
the analogue layer and obtain the same level of performance as with a digital
layer. Conclusion. This work paves the way to high-performance fully-analogue
reservoir computers through the use of online training of the output layers.
