Since the classical processing of information is reaching saturation in the next decades and the amount of data to be processed is growing rapidly, alternative ways of computation are of crucial importance. Among several emerging architectures, neuromorphic computing systems represent a promising option for achieving energy-efficient information processing. Several attempts were made so far, pointing at the practical feasibility of this paradigm.

Recently, we proposed an approach to combine the benefits of neuromorphic architectures with superconducting components in an information-processing system. As the signaling in single-flux-quantum (SFQ) - based superconducting digital logic strongly resembles that in biological systems, this kind of electronics is considered a promising candidate for implementing neuromorphic circuits in an especially effective manner. The adaption of this idea to superconducting electronics has already led to various neuron-like switching elements.
In particular, we have realized an activity-dependent control for an adaptive connection by changing the transmission rate right before the integration within the artificial superconducting neuron [1,2]. A modified version of the Josephson transmission line is used to realize an adaptable coupling between neuron cells.
Following basic neuroscientific principles, we demonstrate that a network of such neurons with a truncated synapse model can behave like a neural network of excitatory and inhibitory neurons in the human brain. We developed an extended soma model that includes multiple outward connections using single-flux-quantum building blocks. This neuron exhibits features typical for leaky integrate-and-fire neurons with equal-strength outward connections necessary to build all-to-all or sparsely connected networks – two crucial connectivity types to represent neural circuits in the human brain. The resulting network shows input-rate dependent state switching between asynchronous and synchronous firing. This is a first demonstration towards the study of large-scale brain dynamics with Josephson junction-based technology and bears the potential of brain network simulations with computation speeds orders of magnitude faster than both semiconductor realizations and biological neural networks.