  Delay-coupled electro-optical systems have received much attention for their
dynamical properties and their potential use in signal processing. In
particular it has recently been demonstrated, using the artificial intelligence
algorithm known as reservoir computing, that photonic implementations of such
systems solve complex tasks such as speech recognition. Here we show how the
backpropagation algorithm can be physically implemented on the same
electro-optical delay-coupled architecture used for computation with only minor
changes to the original design. We find that, compared when the backpropagation
algorithm is not used, the error rate of the resulting computing device,
evaluated on three benchmark tasks, decreases considerably. This demonstrates
that electro-optical analog computers can embody a large part of their own
training process, allowing them to be applied to new, more difficult tasks.
