Pulse-coupled neural networks (PCNN) have an inherent ability to process the signals associated with the digital visual images because it is inspired from the neuronal activity in the primary visual area, V1, of the neocortex.  Many examples of processing mechanisms are provided to make it clear and concise.

figure_2.m     ---  The PCNN 1-D demo
figure_4.m     ---  The PCNN wave demo
figure_6.m     ---  The Image Histogram demo
figure_7.m     ---  The Image Segmentation demo
figure_8.m     ---  The Feature Extraction demo
figure_9.m     ---  The Image Enhancement demo
Algorithm_6.m  ---  image de-noising / restoration demo

If you use these demos, we appreciate it if you cite the following paper:

@Article{zhan2016computational,
  author =    {Zhan, K and Shi, J and Wang, H and Xie, Y and Li, Q},
  title =     {Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review},
  journal =   {Archives of Computational Methods in Engineering},
  year =      {2017},
  pages =     {1--16},
  doi =       {10.1007/s11831-016-9182-3},
  publisher = {Springer}
}

http://www.escience.cn/people/kzhan

If you have any questions on PCNN, Feel free to contact with me. (Email: ice.echo#gmail.com)