  In event-based sensing, many sensors independently and asynchronously emit
events when there is a change in their input. Event-based sensing can present
significant improvements in power efficiency when compared to traditional
sampling, because (1) the output is a stream of events where the important
information lies in the timing of the events, and (2) the sensor can easily be
controlled to output information only when interesting activity occurs at the
input.
  Moreover, event-based sampling can often provide better resolution than
standard uniform sampling. Not only does this occur because individual
event-based sensors have higher temporal resolution, it also occurs because the
asynchrony of events allows for less redundant and more informative encoding.
We would like to explain how such curious results come about.
  To do so, we use ideal time encoding machines as a proxy for event-based
sensors. We explore time encoding of signals with low rank structure, and apply
the resulting theory to video. We then see how the asynchronous firing times of
the time encoding machines allow for better reconstruction than in the standard
sampling case, if we have a high spatial density of time encoding machines that
fire less frequently.
