Improving VisNet for Object Recognition

The study on VisNet, published on November 13, 2025, explores enhancements to this biologically inspired neural network model aimed at improving object recognition. By incorporating radial basis function neurons, Mahalanobis distance-based learning, and retinal-like preprocessing, the research demonstrates that these modifications lead to substantial improvements in recognition accuracy compared to the baseline model. Experimental results across multiple datasets, including MNIST and CIFAR10, validate the effectiveness of these enhancements. The findings emphasize the adaptability and biological relevance of VisNet-inspired architectures, suggesting that they can effectively mimic human visual processing capabilities. This research not only contributes to the field of artificial intelligence but also opens avenues for developing more interpretable and efficient neural network models.