  In spiking neural networks, neuron dynamics are described by the biologically
realistic integrate-and-fire model that captures membrane potential
accumulation and above-threshold firing behaviors. Among the hardware
implementations of integrate-and-fire neuron devices, one important feature,
reset, has been largely ignored. Here, we present the design and fabrication of
a magnetic domain wall and magnetic tunnel junction based artificial
integrate-and-fire neuron device that achieves reliable reset at the end of the
integrate-fire cycle. We demonstrate the domain propagation in the domain wall
racetrack (integration), reading using a magnetic tunnel junction (fire), and
reset as the domain is ejected from the racetrack, showing the artificial
neuron can be operated continuously over 100 integrate-fire-reset cycles. Both
pulse amplitude and pulse number encoding is demonstrated. The device data is
applied on an image classification task using a spiking neural network and
shown to have comparable performance to an ideal leaky, integrate-and-fire
neural network. These results achieve the first demonstration of reliable
integrate-fire-reset in domain wall-magnetic tunnel junction-based neuron
devices and shows the promise of spintronics for neuromorphic computing.
