  Neuromorphic hardware as a non-Von Neumann architecture has better energy
efficiency and parallelism than the conventional computer. Here, with numerical
modeling spin-orbit torque (SOT) device using current-induced SOT and Joule
heating effects, we acquire its magnetization switching probability as a
function of the input current pulses and use it to mimic the
spike-timing-dependent plasticity learning behavior like actual brain working.
We further demonstrate that the artificial spiking neural network (SNN) built
by this SOT device can perform unsupervised handwritten digit recognition with
the accuracy of 80% and logic operation learning. Our work provides a new clue
to achieving SNN-based neuromorphic hardware using high-energy efficiency and
nonvolatile spintronics nanodevices
