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.

翻译：受哺乳动物视觉皮层启发的神经模型研究催生了多种脉冲神经网络，如脉冲耦合神经网络（PCNN）。这类模型属于振荡型时空模型，通过图像刺激产生多种基于时间的响应。本文综述了PCNN的研究现状，涵盖其数学表述、变体结构及文献中提出的简化模型。我们展示了PCNN架构在多个应用领域成功解决基础图像处理与计算机视觉难题的案例，包括图像分割、边缘检测、医学成像、图像融合、图像压缩、目标识别和遥感监测。这些应用成果表明，PCNN架构能够生成适用于各类计算机视觉任务的有效感知信息。