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.

中文翻译：

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具有时间信息的强噪声深刺神经网络

尖峰神经网络（SNN）已经成为具有时间信息的节能型神经网络。SNN在神经形态设备上显示出了较高的效率，但是这些设备易受噪声的影响，从而阻碍了它们在实际应用中的应用。有几项研究提高了噪声的鲁棒性，但是大多数研究都没有考虑深度SNN或时间信息。在本文中，我们使用各种神经编码方法研究了噪声对深度SNN的影响，并提出了一种具有时域信息的鲁棒性深度SNN。利用所提出的方法，我们已经获得了一种深度和深度的神经网络，可以有效且鲁棒地消除尖峰信号的缺失和抖动。