Multi-Dimensional Modeling of Brain-Inspired Artificial Neural Networks and Its Application in Medical Image Analysis

Brain-inspired Artificial Neural Networks (ANNs) have become a prominent research focus in the field of artificial intelligence, and their modeling framework has gradually evolved into a multidimensional development paradigm. This article systematically reviews the progress in this field and organizes it within a four-dimensional framework: (1) Structural modeling, which primarily focuses on neuron-level modeling and network topology optimization; (2) Functional modeling, emphasizing the abstract implementation of mechanisms such as attention, memory, cognition, and emotion; (3) Structure-function coupling, exploring tightly integrated modeling pathways between structure and function; (4) Brain-inspired learning mechanisms, concentrating on various biologically inspired learning rules and their behavioral manifestations. Special attention is given to insights derived from classical neuroscience model organisms, such as Caenorhabditis elegans, macaques, and other specific biological nervous systems, which contribute to the architectural design and energy efficiency optimization of brain-inspired ANNs. This framework provides a clearer analytical perspective for systematically comparing different brain-inspired models and integrating multiple mechanisms. Finally, the article summarizes the innovative applications of brain-inspired ANNs in medical image analysis, emphasizing their potential in spatiotemporal pattern modeling and highlighting key challenges, such as multimodal fusion. Future research will focus on deep integration of structure, function, and learning mechanisms, and further expand the application of brain-inspired intelligence in clinical and other real-world domains.