  Spiking Neural Networks (SNNs), as one of the algorithmic models in
neuromorphic computing, have gained a great deal of research attention owing to
temporal information processing capability, low power consumption, and high
biological plausibility. The potential to efficiently extract spatio-temporal
features makes it suitable for processing the event streams. However, existing
synaptic structures in SNNs are almost full-connections or spatial 2D
convolution, neither of which can extract temporal dependencies adequately. In
this work, we take inspiration from biological synapses and propose a
spatio-temporal synaptic connection SNN (STSC-SNN) model, to enhance the
spatio-temporal receptive fields of synaptic connections, thereby establishing
temporal dependencies across layers. Concretely, we incorporate temporal
convolution and attention mechanisms to implement synaptic filtering and gating
functions. We show that endowing synaptic models with temporal dependencies can
improve the performance of SNNs on classification tasks. In addition, we
investigate the impact of performance vias varied spatial-temporal receptive
fields and reevaluate the temporal modules in SNNs. Our approach is tested on
neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST,
CIFAR10-DVS (image classification), and SHD (speech digit recognition). The
results show that the proposed model outperforms the state-of-the-art accuracy
on nearly all datasets.
