  Consistency is an extension to generalized synchronization which quantifies
the degree of functional dependency of a driven nonlinear system to its input.
We apply this concept to echo-state networks, which are an artificial-neural
network version of reservoir computing. Through a replica test we measure the
consistency levels of the high-dimensional response, yielding a comprehensive
portrait of the echo-state property.
