Integrated representational signatures strengthen specificity in brains and models

TL;DR

This study evaluates multiple representational similarity metrics to better understand neural and artificial systems, integrating them with Similarity Network Fusion to improve regional and model differentiation.

Contribution

It introduces an integrated approach combining diverse metrics with SNF, enhancing the discrimination of brain regions and models beyond individual measures.

Findings

Metrics capturing geometry and tuning yield stronger regional discrimination.

Linearly decodable information is more globally shared across regions.

SNF integration produces sharper separation and clearer hierarchical organization.

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 regions or models. To integrate these complementary representational facets, we adapt Similarity Network Fusion (SNF), a framework originally developed for multi-omics data integration. SNF produces substantially sharper regional and model family-level separation than any single metric and yields robust composite similarity profiles. Moreover, clustering cortical regions using SNF-derived similarity scores reveals a clearer hierarchical organization that aligns closely with established anatomical and functional hierarchies of the visual cortex-surpassing the correspondence achieved by individual metrics.