  Visual attention can be defined as the behavioral and cognitive process of
selectively focusing on a discrete aspect of sensory cues while disregarding
other perceivable information. This biological mechanism, more specifically
saliency detection, has long been used in multimedia indexing to drive the
analysis only on relevant parts of images or videos for further processing.
  The recent advent of silicon retinas (or event cameras -- sensors that
measure pixel-wise changes in brightness and output asynchronous events
accordingly) raises the question of how to adapt attention and saliency to the
unconventional type of such sensors' output. Silicon retina aims to reproduce
the biological retina behaviour. In that respect, they produce punctual events
in time that can be construed as neural spikes and interpreted as such by a
neural network.
  In particular, Spiking Neural Networks (SNNs) represent an asynchronous type
of artificial neural network closer to biology than traditional artificial
networks, mainly because they seek to mimic the dynamics of neural membrane and
action potentials over time. SNNs receive and process information in the form
of spike trains. Therefore, they make for a suitable candidate for the
efficient processing and classification of incoming event patterns measured by
silicon retinas. In this paper, we review the biological background behind the
attentional mechanism, and introduce a case study of event videos
classification with SNNs, using a biology-grounded low-level computational
attention mechanism, with interesting preliminary results.
