  With the rising societal demand for more information-processing capacity with
lower power consumption, alternative architectures inspired by the parallelism
and robustness of the human brain have recently emerged as possible solutions.
In particular, spiking neural networks (SNNs) offer a bio-realistic approach,
relying on pulses analogous to action potentials as units of information. While
software encoded networks provide flexibility and precision, they are often
computationally expensive. As a result, hardware SNNs based on the spiking
dynamics of a device or circuit represent an increasingly appealing direction.
Here, we propose to use superconducting nanowires as a platform for the
development of an artificial neuron. Building on an architecture first proposed
for Josephson junctions, we rely on the intrinsic nonlinearity of two coupled
nanowires to generate spiking behavior, and use electrothermal circuit
simulations to demonstrate that the nanowire neuron reproduces multiple
characteristics of biological neurons. Furthermore, by harnessing the
nonlinearity of the superconducting nanowire's inductance, we develop a design
for a variable inductive synapse capable of both excitatory and inhibitory
control. We demonstrate that this synapse design supports direct fanout, a
feature that has been difficult to achieve in other superconducting
architectures, and that the nanowire neuron's nominal energy performance is
competitive with that of current technologies.
