Do Different Artificial Neural Networks Think Alike? On the Feasibility of Cataloging Representations

Do different artificial neural networks (ANNs) “think” alike? And can their shared internal representations be cataloged? This paper explores the latter questions by as- sessing the feasibility of creating an Artificial Neural Repository (ANR)—a library of “universal” neural representations that are reusable across different ANN archi - tectures. To assess the potential of such a repository, we examine three hypotheses: H1 (Uniqueness), suggesting representations are architecture-specific; H2 (Identity), proposing representations are identical across architectures; and H3 (Similarity), as - serting partial representational overlap. Reviewing existing theoretical arguments and empirical findings—including studies employing Representational Similar - ity Analysis (RSA), Canonical Correlation Analysis (CCA), and Centered Kernel Alignment (CKA)—we find the strongest support for H3. This indicates substantial but not total representational overlap among diverse ANNs. We conclude that while a fully universal repository remains challenging, an ANR is viable if it includes mechanisms to translate between architecture-specific representations.