Spatio-Temporal Channel Attention and Membrane Potential Modulation for Efficient Spiking neural network

Xingming Tang, Tao Chen, Qian Cheng, Hangchi Shen, Shukai Duan, Lidan Wang

Abstract: Spiking Neural Networks (SNNs) are an energy-efficient alternative to Artificial Neural Networks (ANNs) due to their event-driven nature. However, common coding methods such as direct coding struggle to capture critical spatio-temporal dynamics. To address this, we propose a Spatio-Temporal Channel Attention (STCA) module to improve feature extraction during spike encoding. In addition, we introduce a Membrane Potential Modulator (MPM) to reduce information loss due to binary quantization. Together, STCA and MPM form the Gated Attention Coding Mechanism (GACM), which improves SNN training on both static and neuromorphic datasets. Experiments at the Canadian Institute for Advanced Research 10/100 (CIFAR10/100) and CIFAR10-Dynamic Vision Sensor (DVS) show that GACM has higher accuracy and significant efficiency over direct coding. In particular, we improved accuracy by 1.73% on the CIFAR100 and 0.81% on the CIFAR10 in fewer steps.