# Synchronization dynamics on the picosecond timescale in coupled Josephson junction neurons

arXiv: https://arxiv.org/abs/1608.04680 · HTML: https://mlsanity.com/paper/1608.04680/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [An active dendritic tree can mitigate fan-in limitations in superconducting neurons](https://mlsanity.com/paper/2107.05777/) (2021)
- [Deep Neuromorphic Networks with Superconducting Single Flux Quanta](https://mlsanity.com/paper/2311.10721/) (2023)
- [Self-training superconducting neuromorphic circuits using reinforcement learning rules](https://mlsanity.com/paper/2404.18774/) (2024)

## Abstract (arXiv)

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.