Neuromorphic computing paradigms enhance robustness through spiking neural networks.

ABSTRACT: 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 netwo

SUBMITTER: Ding J 

PROVIDER: S-EPMC12630598 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature