—Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption character- istics. However, the event streams output from event cameras have asynchronous, sparse characteristics that cannot be handled by existing computer vision algorithms. Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks. However, direct training of deep SNNs suffers from degradation problems. This work addresses these problems by proposing a spiking nerual network architecture with novel residual block designed and multi-dimension attention modules combined, focusing on the problem of depth prediction. In addition, a novel event stream representation method is proposed specifically for SNNs. This model outperforms previous ANN networks of the same size on the MVSEC dataset and shows great computational efficiency.