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