## 2 Materials and Methods
### Network Structures
#### 2.2.1 Ams_Yolo  
Figure 1a illustrates the complete framework of our network, and the EMS_Block and AM_Block modules and the layer-wise channel balance (LCB) blocks are detailed in Figure 1b-d. In the detection head section, the input first passes through a CBAM attention mechanism module and undergoes neuron encoding through two LCBs and a series of operations including max-pooling before entering the MS_Block module, as shown in Figure 1c.  
AMS_YOLO primarily consists of a backbone and a head, where Figure 1a illustrates the overall framework of our proposed AMS_YOLO, showing its primary components (backbone and head). We use the PLIF neuron model in each of the LCB modules of the entire network with the PLIF neuron model. The PLIF neuron model is encapsulated within the LCB module and experimental validation has confirmed that it yields optimal results. For the backbone, the first convolutional layer transforms the inputs into peak potentials,and then the EMS modules are used to extract target features from different dimensions and number of channels to enhance the robustness of the network. When the network reaches the head section, it passes through the EMS_Module, and inside the module, it passes through the EMS_Block, and then enters the MS_Block. EMS_Module1(2) and EMS_Block1(2) correspond to each other, the solid and dashed boxed portions in Figure 1b, when the number of channels is constant or decreasing, EMS-Block1 is utilized. in order to increase the number of channels, EMS-Block2 can be used for feature reuse using tandem operations while decreasing the number of parameters. In particular, inside the EMS_block, the weighted inputs are integrated through the LCB module using PLIF neurons to generate a sequence of output peak potentials when the membrane potential exceeds the trained discharge threshold, in conjunction with the maximum pooling operation.  
The structures of EMS_Module are depicted in Figure 1b where improvements to the neuron modules are made--details of which will be elaborated in the subsequent subsection of this section. The entire network, as shown in the figure, extracts features through convolution combined with EMS_Module in the backbone section, followed by two branches in the head section performing classification head operations for detection.  
Ensuring high efficiency while maintaining low parameter and computational costs, Figure 1c illustrates the proposed AM_block module, which incorporates the most effective channel and spatial mixed CBAM attention mechanism into the AM_block. The AM_Block is located at the head of the entire network and utilized the CBAM mechanism module for feature extraction before outputting the classification results.  
Figure 1d shows the structure of the LCB block which used both in EMS_Blocks and MS_Blocks. Given that the membrane time constant is a learnable parameter rather than a pre-defined hyperparameter, it aligns more closely with the characteristics of biological brain neurons and has the potential to enhance their expressive power. Initially, the  
Figure 1: The proposed AMS_YOLO for object detection. (**a**) shows the overview of AMS_YOLO, including the backbone and the head. (**b**) shows the EMS_Block. The solid box indicates EMS_Block1 and the dashed box is EMS_Block2 instead the right part of the EMS_Block1, when the number of output channels more than 3, using EMS_block2; otherwise, EMS_block1. (**c**) the self-attention module AM_Blocks. First pass through a CBAM block and then enter LCB. (**d**) refers to the LCB block with PLIF neuron in (**b**,**c**). The inputs are first weighted by the PLIF model and then fed into the TDBN training module after being overloaded by a convolutional layer.  
data passes through the PLIF neuron model, followed by convolutional operations, before proceeding to the training steps of the network. Finally, we use the employ TDBN training method which allows for directly training deeper SNNs while maintaining lower energy consumption.