  Recent advancements in neuroscience research have propelled the development
of Spiking Neural Networks (SNNs), which not only have the potential to further
advance neuroscience research but also serve as an energy-efficient alternative
to Artificial Neural Networks (ANNs) due to their spike-driven characteristics.
However, previous studies often neglected the multiscale information and its
spatiotemporal correlation between event data, leading SNN models to
approximate each frame of input events as static images. We hypothesize that
this oversimplification significantly contributes to the performance gap
between SNNs and traditional ANNs. To address this issue, we have designed a
Spiking Multiscale Attention (SMA) module that captures multiscale
spatiotemporal interaction information. Furthermore, we developed a
regularization method named Attention ZoneOut (AZO), which utilizes
spatiotemporal attention weights to reduce the model's generalization error
through pseudo-ensemble training. Our approach has achieved state-of-the-art
results on mainstream neural morphology datasets. Additionally, we have reached
a performance of 77.1% on the Imagenet-1K dataset using a 104-layer ResNet
architecture enhanced with SMA and AZO. This achievement confirms the
state-of-the-art performance of SNNs with non-transformer architectures and
underscores the effectiveness of our method in bridging the performance gap
between SNN models and traditional ANN models.
