Burst Spiking Neural Networks
A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work further argues that realizing this ambition requires improving not only accuracy but also robustness, defined as the ability to maintain correct predictions under input perturbations. We identify two key issues in existing SNN methods that undermine robustness. First, binary spiking activations can produce large activation-state changes under small perturbations. Second, the lack of effective weight constraints makes network outputs more sensitive to input variations. To this end, we propose Burst Spiking Neural...
| Authors | Jiahong Zhang, Sijun Shen, Man Yao et al. |
|---|---|
| Primary category | cs.NE |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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