  Simulating and imitating the neuronal network of humans or mammals is a
popular topic that has been explored for many years in the fields of pattern
recognition and computer vision. Inspired by neuronal conduction
characteristics in the primary visual cortex of cats, pulse-coupled neural
networks (PCNNs) can exhibit synchronous oscillation behavior, which can
process digital images without training. However, according to the study of
single cells in the cat primary visual cortex, when a neuron is stimulated by
an external periodic signal, the interspike-interval (ISI) distributions
represent a multimodal distribution. This phenomenon cannot be explained by all
PCNN models. By analyzing the working mechanism of the PCNN, we present a novel
neuron model of the primary visual cortex consisting of a continuous-coupled
neural network (CCNN). Our model inherited the threshold exponential decay and
synchronous pulse oscillation property of the original PCNN model, and it can
exhibit chaotic behavior consistent with the testing results of cat primary
visual cortex neurons. Therefore, our CCNN model is closer to real visual
neural networks. For image segmentation tasks, the algorithm based on CCNN
model has better performance than the state-of-art of visual cortex neural
network model. The strength of our approach is that it helps neurophysiologists
further understand how the primary visual cortex works and can be used to
quantitatively predict the temporal-spatial behavior of real neural networks.
CCNN may also inspire engineers to create brain-inspired deep learning networks
for artificial intelligence purposes.
