[2510.20847] Integrated representational signatures strengthen specificity in brains and models

Quantitative Biology > Neurons and Cognition arXiv:2510.20847 (q-bio) [Submitted on 21 Oct 2025 (v1), last revised 3 Apr 2026 (this version, v2)] Title:Integrated representational signatures strengthen specificity in brains and models Authors:Jialin Wu, Shreya Saha, Yiqing Bo, Meenakshi Khosla View a PDF of the paper titled Integrated representational signatures strengthen specificity in brains and models, by Jialin Wu and 3 other authors View PDF Abstract:The extent to which different neural or artificial neural networks (models) rely on equivalent representations to support similar tasks remains a central question in neuroscience and machine learning. Prior work has typically compared systems using a single representational similarity metric, yet each captures only one facet of representational structure. To address this, we leverage a suite of representational similarity metrics-each capturing a distinct facet of representational correspondence, such as geometry, unit-level tuning, or linear decodability-and assess brain region or model separability using multiple complementary measures. Metrics that preserve geometric or tuning structure (e.g., RSA, Soft Matching) yield stronger region-based discrimination, whereas more flexible mappings such as Linear Predictivity show weaker separation. These findings suggest that geometry and tuning encode brain-region- or model-family-specific signatures, while linearly decodable information tends to be more globally shared across ...