Generalized Shape Metrics on Neural Representations.

ABSTRACT: Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interested in surveying large collections of networks that are trained on, or biologically adapted to, similar tasks. A standardized set of analysis tools is now needed to identify how network-level covariates-such as architecture, anatomical brain region, and model organism-impact neural representations (hidden layer activations). Here, we provide a rigorous foundation for these analyses by defining a broad family of metric spaces that quantify representational dissimilarity. Using this framework, we modify existing representational similarity measures based on canonical correlation analysis and centered kernel alignmen

SUBMITTER: Williams AH 

PROVIDER: S-EPMC10760997 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature