2022/03/24 by Anna Zou, Zhiyuan Li, Zou, Anna +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Biological sciences #FOS: Computer and information sciences #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #cs.AI #cs.NE #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2203.13262
Updated a new version
openalex publication_date 2022/03/24 · arxiv created 2022/06/02 · arxiv updated 2022/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep learning continues to play as a powerful state-of-art technique that has achieved extraordinary accuracy levels in various domains of regression and classification tasks, including images, video, signal, and natural language data. The original goal of proposing the neural network model is to improve the understanding of complex human brains using a mathematical expression approach. However, recent deep learning techniques continue to lose the interpretations of its functional process by being treated mostly as a black-box approximator. To address this issue, such an AI model needs to be biological and physiological realistic to incorporate a better understanding of human-machine evolutionary intelligence. In this study, we compare neural networks and biological circuits to discover the similarities and differences from various perspective views. We further discuss the insights into how neural networks learn from data by investigating human biological behaviors and understandable justifications.