Spiking Neural Networks (SNNs), as the third generation  of neural networks, have gained prominence  for their biological plausibility and computational  efficiency, especially in processing diverse  datasets. The integration of attention mechanisms,  inspired by advancements in neural network architectures,  has led to the development of Spiking Transformers. These have shown promise in  enhancing SNNs’ capabilities, particularly in the  realms of both static and neuromorphic datasets. Despite their progress, a discernible gap exists in  these systems, specifically in the Spiking Self Attention (SSA) mechanism’s effectiveness in leveraging  the temporal processing potential of SNNs. To address this, we introduce the Temporal Interaction Module (TIM), a novel, convolutionbased  enhancement designed to augment the temporal  data processing abilities within SNN architectures. TIM’s integration into existing SNN frameworks  is seamless and efficient, requiring minimal  additional parameters while significantly boosting  their temporal information handling capabilities. Through rigorous experimentation, TIM has  demonstrated its effectiveness in exploiting temporal  information, leading to state-of-the-art performance  across various neuromorphic datasets. The  code is available at https://github.com/BrainCog- X/Brain-Cog/tree/main/examples/TIM.