Robust Representation Learning for Spiking Neural Networks based on Conditional Information Bottleneck
Spiking Neural Networks (SNNs) have shown great potential for low-power neuromorphic computing due to their event-driven processing and spatiotemporal sparsity, but they remain vulnerable to input perturbations, hardware-intrinsic noise, and adversarial attacks in real-world deployment. We attribute this vulnerability...
Yu-Ling Zha, Wang Fan, Ying Dai et al.
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