  Multifunctionality is a well observed phenomenological feature of biological
neural networks and considered to be of fundamental importance to the survival
of certain species over time. These multifunctional neural networks are capable
of performing more than one task without changing any network connections. In
this paper we investigate how this neurological idiosyncrasy can be achieved in
an artificial setting with a modern machine learning paradigm known as
`Reservoir Computing'. A training technique is designed to enable a Reservoir
Computer to perform tasks of a multifunctional nature. We explore the critical
effects that changes in certain parameters can have on the Reservoir Computers'
ability to express multifunctionality. We also expose the existence of several
`untrained attractors'; attractors which dwell within the prediction state
space of the Reservoir Computer that were not part of the training. We conduct
a bifurcation analysis of these untrained attractors and discuss the
implications of our results.
