用大脑连接图谱方法解析神经网络结构，提升可解释性
Functional connectomes of neural networks
- 借鉴脑功能连接图谱，用统计与机器学习分析网络拓扑
- 可稳定刻画大型神经网络的结构特征，增强模型可解释性
- 适合关注模型机理与可解释性的研究者
人类大脑是一个复杂系统，理解其工作机制是神经科学长期面临的挑战。功能连接图谱研究通过多年发展出的多种先进分析技术，揭示了不同脑区之间的功能关联。类似地，受大脑架构启发的神经网络在诸多应用中取得显著成功，但常因缺乏可解释性而受限。本文提出一种新方法，通过借鉴脑功能连接图谱的洞察，将脑启发技术应用于神经网络，提供可扩展的、基于稳定统计与机器学习的大型神经网络拓扑表征方式。实证分析表明，该方法能有效提升神经网络的可解释性，深化对模型内在机制的理解。
原文摘要 · Abstract (English)
The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed over the years. Similarly, neural networks, inspired by the brain's architecture, have achieved notable success in diverse applications but are often noted for their lack of interpretability. In this paper, we propose a novel approach that bridges neural networks and human brain functions by leveraging brain-inspired techniques. Our approach, grounded in the insights from the functional connectome, offers scalable ways to characterize topology of large neural networks using stable statistical and machine learning techniques. Our empirical analysis demonstrates its capability to enhance the interpretability of neural networks, providing a deeper understanding of their underlying mechanisms.
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