Synchronization dynamics on the picosecond time scale in coupled Josephson junction neurons

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 antiphase. 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}J/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.

Fluxonic Processing of Photonic Synapse Events

“…The significance of light and superconductors for scaling was analyzed in Ref. 14. Other work has investigated photonics [15][16][17][18][19] and superconducting electronics [20][21][22][23][24][25] for neuromorphic computation, but to our knowledge none of this work has pursued dendritic processing beyond point neurons or the integration of photonics with superconducting electronics to leverage their complementary strengths for communication and computation.…”

Chaos and Relaxation Oscillations in Spin-Torque Windmill Spiking Oscillators

“…In many neuroscience and computer science models, neurons are abstracted as nonlinear oscillators [2][3][4][5]. Memristive oscillators (also called neuristors) [6], Josephson junctions [7], nanoelectromechanical systems [8], and magnetic nano-oscillators called spin-torque nano-oscillators [9][10][11] are interesting candidates for imitating neurons at the nanoscale. In particular, it has been shown experimentally that spin-torque nano-oscillators can implement hardware neural networks and perform cognitive tasks with high accuracy due to their large signal to noise ratio, their high non-linearity and enhanced ability to synchronize [12].…”

Neuromorphic computing with nanoscale spintronic oscillators

“…Indeed, a simple estimation indicates that, in order to fit a hundred million oscillators organized in a two-dimensional array inside a chip the size of a thumb, their lateral dimensions must be smaller than one micrometer. However, despite multiple theoretical proposals 2–5 , and several candidates such as memristive 6 or superconducting 7 oscillators, there is no proof of concept today of neuromorphic computing with nano-oscillators. Indeed, nanoscale devices tend to be noisy and to lack the stability required to process data in a reliable way.…”