The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Utilizing the advantage that neurons in the brain communicate via spikes, spiking neural networks (SNNs) are emerging as a new type of neural network model, boosting the frontier of theoretical investigation and empirical application of artificial neural networks and deep learning. Neuroscience research proposes that the precise timing of neural spikes plays an important role in the information coding and sensory processing of the biological brain. However, the role of spike timing in SNNs is less considered and far from understood. Here we systematically explored the timing mechanism of spike coding in SNNs, focusing on the robustness of the system against various types of attacks. We found that SNNs can achieve higher robustness improvement using the coding principle of precise spike timing in neural encoding and decoding, facilitated by different learning rules. Our results suggest that the utility of spike timing coding in SNNs could improve the robustness against attacks, providing a new approach to reliable coding principles for developing next-generation brain-inspired deep learning.

翻译：深度学习在过去十年的成功，部分被对抗性攻击的阴影所笼罩。相比之下，大脑在执行复杂的认知任务时展现出更强的鲁棒性。利用大脑神经元通过脉冲进行通信的优势，脉冲神经网络（SNNs）作为一种新型神经网络模型正在兴起，推动了人工神经网络和深度学习的理论探索与实证应用前沿。神经科学研究表明，神经脉冲的精确时序在生物大脑的信息编码和感觉处理中发挥重要作用。然而，脉冲时序在SNNs中的作用尚未得到充分关注，且远未得到深入理解。本文系统探索了SNNs中脉冲编码的时序机制，重点关注系统对各种类型攻击的鲁棒性。我们发现，借助不同的学习规则，SNNs可利用神经编码与解码中的精确脉冲时序编码原理实现更高的鲁棒性提升。研究结果表明，在SNNs中应用脉冲时序编码可增强抗攻击鲁棒性，为开发下一代类脑深度学习提供了可靠编码原理的新途径。