  As the limits of traditional von Neumann computing come into view, the
brain's ability to communicate vast quantities of information using low-power
spikes has become an increasing source of inspiration for alternative
architectures. Key to the success of these largescale neural networks is a
power-efficient spiking element that is scalable and easily interfaced with
traditional control electronics. In this work, we present a spiking element
fabricated from superconducting nanowires that has pulse energies on the order
of ~10 aJ. We demonstrate that the device reproduces essential characteristics
of biological neurons, such as a refractory period and a firing threshold.
Through simulations using experimentally measured device parameters, we show
how nanowire-based networks may be used for inference in image recognition, and
that the probabilistic nature of nanowire switching may be exploited for
modeling biological processes and for applications that rely on stochasticity.
