  Spiking neural networks (SNNs) have emerged as energy-efficient neural
networks with temporal information. SNNs have shown a superior efficiency on
neuromorphic devices, but the devices are susceptible to noise, which hinders
them from being applied in real-world applications. Several studies have
increased noise robustness, but most of them considered neither deep SNNs nor
temporal information. In this paper, we investigate the effect of noise on deep
SNNs with various neural coding methods and present a noise-robust deep SNN
with temporal information. With the proposed methods, we have achieved a deep
SNN that is efficient and robust to spike deletion and jitter.
