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Spiking Neural Networks (SNNs), as the third generation of neural networks,have gained prominence for their biological plausibility and computationalefficiency, especially in processing diverse datasets. The integration ofattention mechanisms, inspired by advancements in neural network architectures,has led to the development of Spiking Transformers. These have shown promise inenhancing SNNs' capabilities, particularly in the realms of both static andneuromorphic datasets. Despite their progress, a discernible gap exists inthese systems, specifically in the Spiking Self Attention (SSA) mechanism'seffectiveness in leveraging the temporal processing potential of SNNs. Toaddress this, we introduce the Temporal Interaction Module (TIM), a novel,convolution-based enhancement designed to augment the temporal data processingabilities within SNN architectures. TIM's integration into existing SNNframeworks is seamless and efficient, requiring minimal additional parameterswhile significantly boosting their temporal information handling capabilities.Through rigorous experimentation, TIM has demonstrated its effectiveness inexploiting temporal information, leading to state-of-the-art performance acrossvarious neuromorphic datasets.

TIM: An Efficient Temporal Interaction Module for Spiking Transformer