Exploring and establishing artificial neural networks with electrophysiological characteristics and high computational efficiency is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by the working mechanism of the primary visual cortex, pulse-coupled neural networks (PCNNs) can exhibit the characteristics of synchronous oscillation, refractory period, and exponential decay. These characteristics empower the PCNN model to group pixels with similar spatiality and gray values and to process digital images without training. However, electrophysiological evidence shows that the neurons exhibit highly complex nonlinear dynamics when stimulated by external periodic signals. This chaos phenomenon, also known as the ‘butterfly effect,” cannot be explained by all PCNN models. In this work, we analyze the main obstacle preventing PCNN models from imitating a real primary visual cortex. We consider neuronal excitation as a stochastic process. We then propose a novel neural network of the primary visual cortex, called a continuous-coupled neural network (CCNN). Theoretical analysis indicates that the dynamic behavior of the CCNN is distinct from the PCNN. Numerical results show that the CCNN model exhibits periodic behavior under a DC stimulus, and exhibits chaotic behavior under an AC stimulus, which is consistent with the testing results of primary visual cortex neurons. Furthermore, the image and video processing mechanisms of the CCNN model are analyzed. For image processing tasks, this model encodes the pixel intensity as the frequency of output signals so that it can group pixels with similar gray values. This image processing method can reduce the local gray level difference of the image, and compensate for small local discontinuities in the image. For video processing tasks, the CCNN encodes changing pixels as non-periodic chaotic signals, and it encodes static pixels as periodic signals. It thus achieves the purpose of moving target object recognition by distinguishing the dynamic states corresponding to different neuron clusters in the video. Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry.

The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image’s standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection.

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.