  Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent
works show that the non-linearity of their oscillation amplitude can be
leveraged to achieve waveform classification for an input signal encoded in the
amplitude of the input voltage. Here we show that the frequency and the phase
of the oscillator can also be used to recognize waveforms. For this purpose, we
phase-lock the oscillator to the input waveform, which carries information in
its modulated frequency. In this way we considerably decrease amplitude, phase
and frequency noise. We show that this method allows classifying sine and
square waveforms with an accuracy above 99% when decoding the output from the
oscillator amplitude, phase or frequency. We find that recognition rates are
directly related to the noise and non-linearity of each variable. These results
prove that spin-torque nano-oscillators offer an interesting platform to
implement different computing schemes leveraging their rich dynamical features.
