Learning Stable Spike Representations for Robust ANN-to-SNN Conversion

Spiking neural networks (SNNs) enable event-driven computation with the potential for energy-efficient inference, but training deep SNNs remains costly. ANN-to-SNN conversion offers a practical alternative by transferring pretrained artificial neural networks (ANNs) to spiking models. Existing work has largely focused on preserving post-conversion accuracy on clean inputs, while the robustness of converted SNNs to noises remains less understood. In this paper, we ask which properties of the source ANN determine the stability of the resulting SNN. We show that post-conversion stability is governed by the interaction between perturbation amplification and the spacing of discrete spike-count states. This analysis yields a layerwise robustness surrogate and motivates **S**pike-**S**tate **S**tability **R**egularizer (SSSR), a conversion-aware source-training regularizer that requires neither noise injection nor modification of the conversion rule. Across VGG-16 and ResNet-18 on CIFAR-10/100, SSSR improves strong-noise accuracy over the strongest standard L1/L2 baseline by up to 50.17 percentage points, while remaining within 2.16 points of clean accuracy across all four settings. These results show that post-conversion robustness can be shaped directly during the training of source-ANN models. Our code is available at https://anonymous.4open.science/r/sssr-4E4C/.