  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.
