Temporal-Aware Learning: Coordinating Supervision with the Temporal Evolution of Spiking Neural Networks

The complex spatiotemporal dynamics of Spiking Neural Networks (SNNs) pose substantial challenges to direct training. In this work, we revisit this challenge from a temporal-evolution perspective by analyzing multi-granularity spiking activity, per-step predictions, and gradient relationships. We observe that spike firing fluctuations decrease, spatial firing patterns become more refined, and predictions become more accurate over time. Meanwhile, early-step gradients are less consistent with later-step gradients, whereas gradients among later steps become increasingly consistent. These observations suggest that temporal states contribute differently to optimization and uniform aggregation may underutilize coherent update tendency emerging at later steps. Unlike widely used objectives that supervise temporal states uniformly, we propose Temporal-Aware Learning (TAL), which considers the evolving states of SNNs. Our method incorporates quadratic temporal reweighting, later-step consistency regularization, and temporal target smoothing, to increase the contribution of locally coherent late-step gradients while softening early-step supervision. Experiments on static and neuromorphic datasets demonstrate that our method increases early–late gradient consistency, improves accuracy and accelerates convergence without additional inference overhead, with clear gains as temporal window increases within the evaluated range.