Representation Similarity

Generalized Shape Metrics on Neural Representations

Review Summary The authors build on ideas from the statistical shape analysis literature to develop similarity measures that are metrics: that are positive, symmetric and respect the triangle inequality. They highlight how quantifying the similatiry between matrices of high dimensional vectors in $\mathbb R^n$ over a set of $m$ reference stimuli is complicated.