  Selective attention is an essential mechanism to filter sensory input and to
select only its most important components, allowing the capacity-limited
cognitive structures of the brain to process them in detail. The saliency map
model, originally developed to understand the process of selective attention in
the primate visual system, has also been extensively used in computer vision.
Due to the wide-spread use of frame-based video, this is how dynamic input from
non-stationary scenes is commonly implemented in saliency maps. However, the
temporal structure of this input modality is very different from that of the
primate visual system. Retinal input to the brain is massively parallel, local
rather than frame-based, asynchronous rather than synchronous, and transmitted
in the form of discrete events, neuronal action potentials (spikes). These
features are captured by event-based cameras. We show that a computational
saliency model can be obtained organically from such vision sensors, at minimal
computational cost. We assess the performance of the model by comparing its
predictions with the distribution of overt attention (fixations) of human
observers, and we make available an event-based dataset that can be used as
ground truth for future studies.
