Unsupervised alignment in neuroscience: Introducing a toolbox for Gromov-Wasserstein optimal transport

Understanding how sensory stimuli are represented across different brains, species, and artificial neural networks is a critical topic in neuroscience. Traditional methods for comparing these representations typically rely on supervised alignment, which assumes direct correspondence between stimuli representations across brains or models. However, it has limitations when this assumption is not valid, or when validating the assumption itself is the goal of the research.

To address the limitations of supervised alignment, we propose an unsupervised alignment method based on Gromov-Wasserstein optimal transport (GWOT). GWOT optimally identifies correspondences between representations by leveraging internal relationships without external labels, revealing intricate structural correspondences such as one-to-one, group-to-group, and shifted mappings.

We provide a comprehensive methodological guide and introduce a toolbox called GWTune for using GWOT in neuroscience. Our results show that GWOT can reveal detailed structural distinctions that supervised methods may overlook. We also demonstrate successful unsupervised alignment in key data domains, including behavioral data, neural activity recordings, and artificial neural network models, demonstrating its flexibility and broad applicability.

Unlike traditional supervised alignment methods such as Representational Similarity Analysis, which assume direct correspondence between stimuli, GWOT provides a nuanced approach that can handle different types of structural correspondence, including fine-grained and coarse correspondences. Our method would provide richer insights into the similarity or difference of representations by revealing finer structural differences.

We anticipate that our work will significantly broaden the accessibility and application of unsupervised alignment in neuroscience, offering novel perspectives on complex representational structures. By providing a user-friendly toolbox and a detailed tutorial, we aim to facilitate the adoption of unsupervised alignment techniques, enabling researchers to achieve a deeper understanding of cross-brain and cross-species representation analysis.