  The reservoir computing scheme is a machine learning mechanism which utilizes
the naturally occuring computational capabilities of dynamical systems. One
important subset of systems that has proven powerful both in experiments and
theory are delay-systems. In this work, we investigate the reservoir computing
performance of hybrid network-delay systems systematically by evaluating the
NARMA10 and the Sante Fe task.. We construct 'multiplexed networks' that can be
seen as intermediate steps on the scale from classical networks to the 'virtual
networks' of delay systems. We find that the delay approach can be extended to
the network case without loss of computational power, enabling the construction
of faster reservoir computing substrates.
