  A reservoir computer is a dynamical system that may be used to perform
computations. A reservoir computer usually consists of a set of nonlinear nodes
coupled together in a network so that there are feedback paths. Training the
reservoir computer consists of inputing a signal of interest and fitting the
time series signals of the reservoir computer nodes to a training signal that
is related to the input signal. It is believed that dynamical systems function
most efficiently as computers at the "edge of chaos", the point at which the
largest Lyapunov exponent of the dynamical system transitions from negative to
positive. In this work I simulate several different reservoir computers and ask
if the best performance really does come at this edge of chaos. I find that
while it is possible to get optimum performance at the edge of chaos, there may
also be parameter values where the edge of chaos regime produces poor
performance. This ambiguous parameter dependance has implications for building
reservoir computers from analog physical systems, where the parameter range is
restricted.
