Delta SNN: Learning Time-Variant Synaptic Weights in Spiking Neural Networks for Event Vision

Spiking neural networks (SNNs) naturally align with event cameras through sparse, event-driven computation, yet effectively modeling the spatiotemporal dynamics of event streams remains challenging. Typically, SNNs fully share synaptic weights across timesteps to maintain parameter and computational efficiency. However, event distributions and neuronal states evolve over time, causing different timesteps to exhibit heterogeneous features and potentially conflicting optimization demands, while a fixed transformation must serve them all. To address this issue, we propose Delta SNN, a time-variant synaptic framework that relaxes complete temporal weight sharing. Delta SNN augments a shared synaptic backbone with compact temporal modulation, enabling adaptive transformations while retaining cross-time knowledge sharing. We further introduce binary synaptic routing to realize temporal modulation with sparse, addition-based inference. Theoretically, we show that time-variant weights substantially enhance the expressive capacity of SNNs. Experiments across three challenging event-vision tasks consistently improve strong baselines with only marginal overhead.