Integrated representational signatures strengthen specificity in brains and models

ArXi:2510.20847v2 Announce Type: replace-cross The extent to which different neural or artificial neural networks (models) rely on equivalent representations to 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.