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In this work, we introduce an optoelectronic spiking artificial neuroncapable of operating at ultrafast rates ($\approx$ 100 ps/optical spike) andwith low energy consumption ($<$ pJ/spike). The proposed system combines anexcitable resonant tunnelling diode (RTD) element exhibiting negativedifferential conductance, coupled to a nanoscale light source (forming a masternode) or a photodetector (forming a receiver node). We study numerically thespiking dynamical responses and information propagation functionality of aninterconnected master-receiver RTD node system. Using the key functionality ofpulse thresholding and integration, we utilize a single node to classifysequential pulse patterns and perform convolutional functionality for imagefeature (edge) recognition. We also demonstrate an optically-interconnectedspiking neural network model for processing of spatiotemporal data at over 10Gbps with high inference accuracy. Finally, we demonstrate an off-chipsupervised learning approach utilizing spike-timing dependent plasticity forthe RTD-enabled photonic spiking neural network. These results demonstrate thepotential and viability of RTD spiking nodes for low footprint, low energy,high-speed optoelectronic realization of neuromorphic hardware.

Resonant tunnelling diode nano-optoelectronic spiking nodes for neuromorphic information processing