  We propose a framework inspired by biological vision systems to produce
saliency maps of digital images. Well-known computational models for receptive
fields of areas in the visual cortex that are specialized for color and
orientation perception are used. To model the connectivity between these areas
we use the CARLsim library which is a spiking neural network(SNN) simulator.
The spikes generated by CARLsim, then serve as extracted features and input to
our saliency detection algorithm. This new method of saliency detection is
described and applied to benchmark images.
