# A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing

arXiv: https://arxiv.org/abs/2406.00239 · HTML: https://mlsanity.com/paper/2406.00239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 78 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)

## Abstract (arXiv)

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.