  Artificial Recurrent Neural Networks are a powerful information processing
abstraction, and Reservoir Computing provides an efficient strategy to build
robust implementations by projecting external inputs into high dimensional
dynamical system trajectories. In this paper, we propose an extension of the
original approach, a local unsupervised learning mechanism we call Phase
Transition Adaptation, designed to drive the system dynamics towards the `edge
of stability'. Here, the complex behavior exhibited by the system elicits an
enhancement in its overall computational capacity. We show experimentally that
our approach consistently achieves its purpose over several datasets.
