  Efficiently selecting an appropriate spike stream data length to extract
precise information is the key to the spike vision tasks. To address this
issue, we propose a dynamic timing representation for spike streams. Based on
multi-layers architecture, it applies dilated convolutions on temporal
dimension to extract features on multi-temporal scales with few parameters. And
we design layer attention to dynamically fuse these features. Moreover, we
propose an unsupervised learning method for optical flow estimation in a
spike-based manner to break the dependence on labeled data. In addition, to
verify the robustness, we also build a spike-based synthetic validation dataset
for extreme scenarios in autonomous driving, denoted as SSES dataset. It
consists of various corner cases. Experiments show that our method can predict
optical flow from spike streams in different high-speed scenes, including real
scenes. For instance, our method gets $15\%$ and $19\%$ error reduction from
the best spike-based work, SCFlow, in $\Delta t=10$ and $\Delta t=20$
respectively which are the same settings as the previous works.
