Functional connectomes of neural networks
This work addresses the lack of interpretability in neural networks for researchers and practitioners, but it appears incremental as it builds on existing brain-inspired techniques.
The paper tackles the problem of neural network interpretability by proposing a brain-inspired approach based on functional connectomes, resulting in enhanced interpretability and deeper understanding of neural network mechanisms.
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.