  Spiking neural networks are known to be superior over artificial neural
networks for their computational power efficiency and noise robustness. The
benefits of spiking coupled with the high-bandwidth and low-latency of
photonics can enable highly-efficient, noise-robust, high-speed neural
processors. The landscape of photonic spiking neurons consists of an
overwhelming majority of excitable lasers and a few demonstrations on nonlinear
optical cavities. The silicon platform is best poised to host a scalable
photonic technology given its CMOS-compatibility and low optical loss. Here, we
present a survey of existing photonic spiking neurons, and propose a novel
spiking neuron based on a hybrid graphene-on-silicon microring cavity. A
comparison among a representative sample of photonic spiking devices is also
presented. Finally, we discuss methods employed in training spiking neural
networks, their challenges as well as the application domain that can be
enabled by photonic spiking neural hardware.
