Synchronization dynamics on the picosecond timescale in coupled Josephson junction neurons

TL;DR

This paper demonstrates superconducting Josephson junction circuits that emulate neuronal dynamics, achieving synchronization on picosecond timescales with low energy use, promising advancements in neuromorphic computing.

Contribution

The authors fabricate and experimentally validate a superconducting neuromorphic circuit modeling coupled neurons with ultrafast synchronization capabilities.

Findings

Neurons exhibit desynchronized and synchronized states, including in-phase and anti-phase.

Synchronization states can be toggled via synaptic delay and strength adjustments.

Firing synchronization is computed over 70,000 times faster than digital methods.

Abstract

Conventional digital computation is rapidly approaching physical limits for speed and energy dissipation. Here we fabricate and test a simple neuromorphic circuit that models neuronal somas, axons and synapses with superconducting Josephson junctions. The circuit models two mutually coupled excitatory neurons. In some regions of parameter space the neurons are desynchronized. In others, the Josephson neurons synchronize in one of two states, in-phase or anti-phase. An experimental alteration of the delay and strength of the connecting synapses can toggle the system back and forth in a phase-flip bifurcation. Firing synchronization states are calculated >70,000 times faster than conventional digital approaches. With their speed and low energy dissipation (10-17 Joules/spike), this set of proof-of- concept experiments establishes Josephson junction neurons as a viable approach for improvements in neuronal computation as well as applications in neuromorphic computing.