  Neuromorphic computing is poised to further the success of software-based
neural networks by utilizing improved customized hardware. However, the
translation of neuromorphic algorithms to hardware specifications is a problem
that has been seldom explored. Building superconducting neuromorphic systems
requires extensive expertise in both superconducting physics and theoretical
neuroscience. In this work, we aim to bridge this gap by presenting a tool and
methodology to translate algorithmic parameters into circuit specifications. We
first show the correspondence between theoretical neuroscience models and the
dynamics of our circuit topologies. We then apply this tool to solve linear
systems by implementing a spiking neural network with our superconducting
nanowire-based hardware.
