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.

该论文回顾了受哺乳动物视觉皮层启发的神经模型研究，重点介绍了脉冲耦合神经网络（PCNN）的数学形式、变体和文献中的其他简化方法，并提出了在图像处理和计算机视觉领域的多个应用，包括图像分割、边缘检测、医学成像、图像融合、图像压缩、物体识别和遥感。这些应用的结果表明，PCNN 架构产生了对各种计算机视觉任务有用的感知信息。