A paper by the research team of Planning Group A01, Dr. Oizumi, has been published in the Journal of Neuroscience Methods.

 In this study, we have compiled a tutorial on the methodology for unsupervised alignment of different brain representations of information using Gromov-Wasserstein optimal transport, created a Python toolbox, and made it publicly available.

 This methodology has been used in this area as a method for comparing qualia structures between individuals, and is expected to be widely used in future studies of qualia structures. The full text is available to read in Open Access. We would be very happy if you did.

 Please see also here for more detailed explanations.

Paper Information

Authors: Ken Takeda, Masaru Sasaki, Kota Abe, Masafumi Oizumi

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

Journal： Journal of Neuroscience Methods, Volume 419, 2025, 110443, ISSN 0165-0270

DOI: https://doi.org/10.1016/j.jneumeth.2025.110443

Abstract: 

Background:

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.

New method:

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.

Results:

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.

Comparison with existing methods:

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.

Conclusion:

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.

Keywords: Gromov–Wasserstein optimal transport; Unsupervised alignment; Neuroscience