  Research in neural models inspired by mammal's visual cortex has led to many
spiking neural networks such as pulse-coupled neural networks (PCNNs). These
models are oscillating, spatio-temporal models stimulated with images to
produce several time-based responses. This paper reviews PCNN's state of the
art, covering its mathematical formulation, variants, and other simplifications
found in the literature. We present several applications in which PCNN
architectures have successfully addressed some fundamental image processing and
computer vision challenges, including image segmentation, edge detection,
medical imaging, image fusion, image compression, object recognition, and
remote sensing. Results achieved in these applications suggest that the PCNN
architecture generates useful perceptual information relevant to a wide variety
of computer vision tasks.
