Adding a Spiking Neuron-Astrocyte Network to a Convolutional Neural Network Improves Classification of Noisy Images

This paper investigates the effectiveness of classifying noisy images using a combined network model that integrates a spiking neuron-astrocyte network with a convolutional neural network. This research is relevant and novel in the context of developing more efficient and robust computer vision systems. Noisy images are a realistic scenario for many computer vision applications, such as image recognition, autonomous driving, and medical diagnosis. The combined model, integrating a spiking neuron-astrocyte network with a convolutional neural network, presents a novel approach to image classification. Investigating the effectiveness of such a model in the context of noisy images opens up new possibilities for enhancing classification accuracy and robustness, as well as gaining a deeper understanding of the interactions between neurons and astrocytes in the context of image processing.