Spike-Driven Neighborhood Attention for Vision Transformers

Spiking Neural Networks (SNNs) promise ultra-low-power, event-driven computation; however, current efforts to integrate self-attention into spiking transformers predominantly rely on global attention mechanisms, introducing growing token complexity when scaling and diluting discriminative feature representations. We introduce Spike-Driven Neighborhood Attention (SDNA) over the full k×k neighborhood in two forms: Pairwise SDNA, which gates each query–neighbor interaction before aggregating, and Linear SDNA, which exploits the absence of a softmax in spiking attention to pool the key–value interaction over the window and gate it once, evaluating the spiking gate per position rather than per query–neighbor pair. Both operators are placed in a fully local hierarchical backbone that increases the receptive field without any global attention layer. Across static and neuromorphic benchmarks the two operators are competitive with global spike-driven transformers at comparable or lower parameter counts. Pairwise SDNA achieves the highest accuracy among the compared spike-driven transformers on CIFAR-10 at 97.24%, while Linear SDNA achieves the highest accuracy on N-Caltech101 at 89.65%. These results show that competitive performance can be achieved using a fully local hierarchical attention design, without requiring an explicit global-attention stage.