Spiking neural networks (SNNs) offer promise for efficient and powerful neurally inspired computation. Common to other types of neural networks, however, SNNs face the severe issue of vulnerability to adversarial attacks. We present the first study that draws inspiration from neural homeostasis to develop a bio-inspired solution that counters the susceptibilities of SNNs to adversarial onslaughts. At the heart of our approach is a novel threshold-adapting leaky integrate-and-fire (TA-LIF) neuron model, which we adopt to construct the proposed adversarially robust homeostatic SNN (HoSNN). Distinct from traditional LIF models, our TA-LIF model incorporates a self-stabilizing dynamic thresholding mechanism, curtailing adversarial noise propagation and safeguarding the robustness of HoSNNs in an unsupervised manner. Theoretical analysis is presented to shed light on the stability and convergence properties of the TA-LIF neurons, underscoring their superior dynamic robustness under input distributional shifts over traditional LIF neurons. Remarkably, without explicit adversarial training, our HoSNNs demonstrate inherent robustness on CIFAR-10, with accuracy improvements to 72.6% and 54.19% against FGSM and PGD attacks, up from 20.97% and 0.6%, respectively. Furthermore, with minimal FGSM adversarial training, our HoSNNs surpass previous models by 29.99% under FGSM and 47.83% under PGD attacks on CIFAR-10. Our findings offer a new perspective on harnessing biological principles for bolstering SNNs adversarial robustness and defense, paving the way to more resilient neuromorphic computing.

翻译：脉冲神经网络（SNNs）在实现高效强大的神经启发计算方面展现出潜力。然而，与其他类型神经网络类似，SNNs也面临对抗攻击的严重脆弱性问题。本文首次从神经稳态机制汲取灵感，提出一种生物启发式解决方案，以应对SNNs对对抗攻击的易感性。该方法的核心是一种新颖的自适应阈值泄露整合发放（TA-LIF）神经元模型，我们采用该模型构建了所提出的抗对抗攻击稳态SNN（HoSNN）。与传统LIF模型不同，TA-LIF模型嵌入了自稳定动态阈值机制，能以无监督方式抑制对抗噪声传播，保障HoSNN的鲁棒性。本文通过理论分析揭示了TA-LIF神经元的稳定性与收敛特性，论证了其在输入分布偏移下相比传统LIF神经元具有更优的动态鲁棒性。值得注意的是，无需显式对抗训练，我们的HoSNN在CIFAR-10数据集上即展现出固有抗性：针对FGSM和PGD攻击，准确率从20.97%和0.6%分别提升至72.6%和54.19%。此外，经最小化FGSM对抗训练后，我们的HoSNN在CIFAR-10上相比先前模型实现性能突破——对FGSM攻击提升29.99%，对PGD攻击提升47.83%。我们的发现为利用生物原理增强SNN对抗鲁棒性与防御能力提供了新视角，为构建更具抗性的神经形态计算铺平了道路。