In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence modeling. However, existing Spiking Transformers still lack a principled mechanism for effective temporal fusion, limiting their ability to fully exploit spatiotemporal dependencies. Inspired by feedforward-feedback modulation in the human visual pathway, we propose TEFormer, the first Spiking Transformer framework that achieves bidirectional temporal fusion by decoupling temporal modeling across its core components. Specifically, TEFormer employs a lightweight and hyperparameter-free forward temporal fusion mechanism in the attention module, enabling fully parallel computation, while incorporating a backward gated recurrent structure in the MLP to aggregate temporal information in reverse order and reinforce temporal consistency. Extensive experiments across a wide range of benchmarks demonstrate that TEFormer consistently and significantly outperforms strong SNN and Spiking Transformer baselines under diverse datasets. Moreover, through the first systematic evaluation of Spiking Transformers under different neural encoding schemes, we show that the performance gains of TEFormer remain stable across encoding choices, indicating that the improved temporal modeling directly translates into reliable accuracy improvements across varied spiking representations. These results collectively establish TEFormer as an effective and general framework for temporal modeling in Spiking Transformers.

翻译：近年来，脉冲神经网络（SNNs）取得了显著进展，其中脉冲Transformer作为一种具有前景的能效序列建模架构崭露头角。然而，现有的脉冲Transformer仍缺乏一种有效的时序融合机制，限制了其充分利用时空依赖关系的能力。受人类视觉通路中前馈-反馈调制机制的启发，我们提出了TEFormer，这是首个通过在其核心组件中解耦时序建模来实现双向时序融合的脉冲Transformer框架。具体而言，TEFormer在注意力模块中采用了一种轻量级且无需超参数的前向时序融合机制，实现了完全并行计算；同时在多层感知机（MLP）中引入了反向门控循环结构，以逆序聚合时序信息并增强时序一致性。在广泛基准测试上的大量实验表明，TEFormer在不同数据集下均能持续且显著地超越强SNN及脉冲Transformer基线模型。此外，通过对脉冲Transformer在不同神经编码方案下的首次系统性评估，我们发现TEFormer的性能提升在不同编码选择下保持稳定，这表明改进的时序建模能够直接转化为在不同脉冲表示下可靠且一致的精度提升。这些结果共同确立了TEFormer作为脉冲Transformer中一种有效且通用的时序建模框架。