Spatio-Temporal Margin Regularization for Mitigating Temporal Error Accumulation in Spiking Neural Networks

Spiking neural networks (SNNs) exploit spatio-temporal spike dynamics for low-power computation and temporal modeling, yet early misfired spikes trigger cumulative temporal errors that amplify across iterative dynamics and degrade generalization. To address this issue, we propose the dual Spatio-Temporal Margin Regularization (STMR) framework, which mitigates the temporal error accumulation from both the perspectives of spike dynamics and output representation. Temporal margin regularization quantifies class-specific spike latency via weight mapping, encouraging target-correlated neurons to fire earlier than negative-correlated ones to suppress error generation at its source. Spatial margin regularization further constrains the per-timestep confidence margin between the target and the non-target risk class, with timestep-coordinated adaptive weights that focus on highly erroneous timesteps to balance regularization strength across the temporal dimension. Extensive experiments on 1D, 2D, and 3D benchmarks with spiking VGG, ResNet, and Transformer architectures demonstrate consistent performance gains. Notably, STMR achieves 87.1% accuracy on CIFAR10-DVS, and visual analysis confirms that it effectively advances target-class spike activation and narrows negative confidence margins. This work provides new insights into spike dynamics-aware optimization for SNNs.