  How to effectively and efficiently deal with spatio-temporal event streams,
where the events are generally sparse and non-uniform and have the microsecond
temporal resolution, is of great value and has various real-life applications.
Spiking neural network (SNN), as one of the brain-inspired event-triggered
computing models, has the potential to extract effective spatio-temporal
features from the event streams. However, when aggregating individual events
into frames with a new higher temporal resolution, existing SNN models do not
attach importance to that the serial frames have different signal-to-noise
ratios since event streams are sparse and non-uniform. This situation
interferes with the performance of existing SNNs. In this work, we propose a
temporal-wise attention SNN (TA-SNN) model to learn frame-based representation
for processing event streams. Concretely, we extend the attention concept to
temporal-wise input to judge the significance of frames for the final decision
at the training stage, and discard the irrelevant frames at the inference
stage. We demonstrate that TA-SNN models improve the accuracy of event streams
classification tasks. We also study the impact of multiple-scale temporal
resolutions for frame-based representation. Our approach is tested on three
different classification tasks: gesture recognition, image classification, and
spoken digit recognition. We report the state-of-the-art results on these
tasks, and get the essential improvement of accuracy (almost 19\%) for gesture
recognition with only 60 ms.
