  We consider the task of learning to extract motion from videos. To this end,
we show that the detection of spatial transformations can be viewed as the
detection of synchrony between the image sequence and a sequence of features
undergoing the motion we wish to detect. We show that learning about synchrony
is possible using very fast, local learning rules, by introducing
multiplicative "gating" interactions between hidden units across frames. This
makes it possible to achieve competitive performance in a wide variety of
motion estimation tasks, using a small fraction of the time required to learn
features, and to outperform hand-crafted spatio-temporal features by a large
margin. We also show how learning about synchrony can be viewed as performing
greedy parameter estimation in the well-known motion energy model.
