  Spiking neural networks (SNNs), as one of the brain-inspired models, has
spatio-temporal information processing capability, low power feature, and high
biological plausibility. The effective spatio-temporal feature makes it
suitable for event streams classification. However, neuromorphic datasets, such
as N-MNIST, CIFAR10-DVS, DVS128-gesture, need to aggregate individual events
into frames with a new higher temporal resolution for event stream
classification, which causes high training and inference latency. In this work,
we proposed a spatio-temporal compression method to aggregate individual events
into a few time steps of synaptic current to reduce the training and inference
latency. To keep the accuracy of SNNs under high compression ratios, we also
proposed a synaptic convolutional block to balance the dramatic change between
adjacent time steps. And multi-threshold Leaky Integrate-and-Fire (LIF) with
learnable membrane time constant is introduced to increase its information
processing capability. We evaluate the proposed method for event streams
classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture
datasets. The experiment results show that our proposed method outperforms the
state-of-the-art accuracy on nearly all datasets, using fewer time steps.
