Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.

翻译：脉冲神经网络（SNN）作为处理时间序列中时空模式（如动态视觉传感器采集的地址事件表示数据）的高效模型之一。尽管卷积SNN凭借其卷积结构卓越的空间特征提取能力在AER数据集上取得了显著性能，却忽略了与序列时间点相关的时间特征。本文提出一种嵌入先进脉冲卷积块注意力模块的递归脉冲神经网络模型，旨在融合时空模式的时空特征。该模型通过SCBAM自适应调用空间和时间通道中的历史信息，兼具高效记忆调用与历史冗余消除的优势。我们在DVS128-Gesture数据集及其他时间序列数据集上评估了模型性能。实验结果表明，所提出的SRNN-SCBAM模型能以更少内存空间更充分地利用时空维度的历史信息，并在准确率上优于其他模型。