  The recent demonstration of neuromorphic computing with spin-torque
nano-oscillators has opened a path to energy efficient data processing. The
success of this demonstration hinged on the intrinsic short-term memory of the
oscillators. In this study, we extend the memory of the spin-torque
nano-oscillators through time-delayed feedback. We leverage this extrinsic
memory to increase the efficiency of solving pattern recognition tasks that
require memory to discriminate different inputs. The large tunability of these
non-linear oscillators allows us to control and optimize the delayed feedback
memory using different operating conditions of applied current and magnetic
field.
