预印本批评神经科学中的相似性分析：RSA 与 CKA 对编码流形拓扑不敏感

arXiv:2605.05907v2 Announce Type: replace Abstract: RSA and CKA are standard metrics for comparing neural representations across brain regions, organisms, and deep learning models. We demonstrate a fundamental weakness: these decoding-based metrics are insensitive to encoding manifold topology---the internal functional organization of a neural population. In a controlled MNIST experiment, RSA, CKA, and Procrustes $R^2$ remain statistically unchanged when encoding topology is causally manipulated via an auxiliary clustering loss, while the two model populations differ significantly in attribution patterns, weight-graph assortativity, and out-of-distribution robustness. Across biological systems and machine learning models, similar decoding behavior can arise from small, non-representative subpopulations, and alignment metrics are insensitive to encoding manifold topology even when it is fundamentally altered. These findings bear directly on mechanistic interpretability: standard alignment metrics cannot distinguish whether two networks share the same computational circuits or merely produce indistinguishable aggregate outputs. We propose encoding manifolds and Gromov-Wasserstein distance as complementary diagnostics for any decoding-based similarity claim, and provide a Neural Manifold Explorer tool at https://johannesbertram.github.io/FNN_Manifolds/index.html for further experimentation.

中文翻译还没有生成，先显示原文。