## 3 Results
### Ablation Study  
Ablation experiments were conducted to further investigate and validate the effectiveness of the proposed method and analyze the impact of attention modules and neurons on the network performance. The experimental results on the COCO2017 dataset are presented in Table 2.  
By observing [EMAIL_ADDRESS], AMS_YOLO1 refers to the network that only integrated with the PLIF [5] model, which achieved a 0.006 improvement compared to the state-of-the-art baseline. Meanwhile, AMS_YOLO2 refers to the structure that only combined the CBAM [25] mechanism, which increased 0.004 in accuracy, less than AMS_YOLO1. We can  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Method** & **Neurons** & **Dataset** & **Params** & **mAP @(0.5)** & **mAP @(0.95)** \\ \hline Spiking-YOLO & Tiny-YOLO & Leaky-ReLu & COCO2017 & / & 0.257 & / \\ Bayesian Optimization & Tiny-YOLO & LIF & COCO2017 & / & 0.211 & / \\ EMS\_YOLO & ResNet10 & LIF & COCO2017 & 6.2M & 0.328 & 0.162 \\
**AMS\_YOLO** & **ResNet10 + CBAM** & **PLIF** & **COCO2017** & **5.9M** & **0.335** & **0.173** \\ \hline Spiking-VGG & VGG-11 + SSD & LIF & GEN1 & 12.2M & / & 0.158 \\
**AMSpiking-VGG** & **VGG-11 + SSD + CBAM** & **PLIF** & **GEN1** & **12.7M** & / & **0.200** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The result on static and dynamic dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Method** & **Neurons** & **Dataset** & **mAP @ (0.5)** & **mAP @(0.95)** \\ \hline EMS\_YOLO & ResNet10 & LIF & COCO2017 & 0.328 & 0.162 \\ AMS\_YOLO1 & ResNet10 & PLIF & COCO2017 & 0.332 & 0.168 \\ AMS\_YOLO2 & ResNet10 + CBAM & LIF & COCO2017 & 0.333 & 0.166 \\
**AMS\_YOLO** & **ResNet10 + CBAM** & **PLIF** & **COCO2017** & **0.335** & **0.173** \\ \hline Spiking-VGG & VGG-11 + SSD & LIF & GEN1 & / & 0.158 \\ AMSpiking-VGG1 & VGG-11 + SSD & PLIF & GEN1 & / & 0.174 \\
**AMSpiking-VGG** & **VGG-11 + SSD + CBAM** & **PLIF** & **GEN1** & / & **0.194** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness of self-attention module and different neurons.  
deduce that the neuronal model has a more significant effect on the network than the attention mechanism. Nevertheless, simultaneously introducing both the attention mechanism and optimized neuron models caused an out-performance leading to an increase of 0.011 in [EMAIL_ADDRESS].  
Likewise, as shown in Table 2, AMSpiking-VGG1 represent the optimized network that integrated PLIF only, which achieved a 0.016 improvement. But after combining the CBAM mechanism with the network, it increased 0.036 compared to the baseline Spiking-VGG [16].  
It can be concluded that both the diversity of neuron expression and the fusion of spatial channel dimension information contribute to the improved network performance. The introduction of the CBAM module effectively integrates spatial and channel information, and significantly improves the performance of object detection. Additionally, the use of PLIF neurons also positively impacts detection results. The performance on the GEN1 dataset is significant in further validating the effectiveness and necessity of the self-attention mechanism module.