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.

翻译：人脑是一个复杂系统，理解其工作机制一直是神经科学领域长期面临的挑战。功能连接组研究通过绘制不同脑区之间的功能连接图谱，借助多年来发展的各种先进分析技术，为理解大脑提供了宝贵的见解。类似地，受大脑结构启发的神经网络在多种应用中取得了显著成功，但其可解释性不足的问题也常被提及。本文提出一种新颖方法，通过借鉴类脑技术，在神经网络与人脑功能之间建立桥梁。该方法基于功能连接组的洞见，利用稳定的统计与机器学习技术，提供了可扩展的方式来表征大型神经网络的拓扑结构。我们的实证分析表明，该方法能够有效提升神经网络的可解释性，从而更深入地理解其内在运行机制。