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

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Biologically Inspired Computing

Background:

• Artificial Neural Networks (ANNs) are increasingly inspired by biological brains.
• Existing research lacks a systematic framework for organizing diverse brain-inspired ANNs.
• Neuroscience provides crucial insights for developing advanced AI models.

Purpose of the Study:

• To systematically review and organize progress in brain-inspired Artificial Neural Networks (ANNs).
• To introduce a novel four-dimensional framework for analyzing brain-inspired ANNs.
• To explore applications and future directions in brain-inspired AI.

Main Methods:

• Systematic review of brain-inspired Artificial Neural Networks (ANNs) research.
• Organization of findings within a four-dimensional framework: structural modeling, functional modeling, structure-function coupling, and brain-inspired learning mechanisms.
• Analysis of insights from neuroscience model organisms (e.g., C. elegans, macaques).

Main Results:

• A comprehensive four-dimensional framework for classifying and understanding brain-inspired ANNs.
• Identification of key contributions from neuroscience to ANN architecture and energy efficiency.
• Summary of applications in medical image analysis, focusing on spatiotemporal patterns and multimodal fusion.

Conclusions:

• The proposed framework offers a clear perspective for comparing and integrating various brain-inspired models.
• Brain-inspired ANNs show significant potential in medical image analysis and other real-world applications.
• Future research should focus on deep integration of structure, function, and learning for advanced AI.