Neuromorphic computing paradigms enhance robustness through spiking neural networks

The success of deep learning methods over the past decade has been partially shrouded in the shadow of adversarial attacks. Even a tiny undetectable deformation can lead to vicious misleading targeted at safety-critical applications. In contrast, the brain is far more robust when performing complex cognitive tasks. Nevertheless, the underlying mechanisms that contribute to the brain's high reliability remain largely unexplored. At the intersection of neuroscience and artificial intelligence, we show that neuromorphic paradigms offer a promising solution to the dilemma brought by deep learning's inherent vulnerabilities. Specifically, we exploit the temporal processing capabilities of spiking neural networks (SNNs) to achieve robustness surpassing that of traditional artificial neural networks (ANNs). We demonstrate that prioritizing task-critical information in the encoded sequence and employing early exit decoding to ignore later perturbations significantly enhance SNN robustness. Further improvements in robustness are achieved by accurately capturing temporal dependencies through specialized training algorithms. Additionally, we introduce a fusion encoding strategy to balance SNN generalization on natural data with robustness against adversarial input. Experimental results on the CIFAR-10 dataset show that SNNs trained with these combined methods achieve twice the robustness of ANNs. Overall, our work demonstrates that neuromorphic computing, leveraging the temporal processing capabilities of SNNs, not only provides superior robustness compared to ANNs but also retains the benefit of low energy consumption. These advancements pave the way for developing next-generation, environmentally friendly, and reliable spike-based intelligent systems.