計画班C01大泉さんらの研究チームによる論文が、Journal of Neuroscience Methods誌に掲載されました。

　この研究はGromov-Wasserstein最適輸送を使って、異なる脳の情報表現を教師なしで揃える方法論をチュートリアルとしてまとめ、Pythonのtoolboxを作り、公開しました。

　この方法論は、本領域ではクオリア構造を個人間で比較する方法として使用されており、今後、クオリア構造の研究で広く使われることが期待されます。Open Accessで全文をお読みいただけます。ぜひご覧ください。

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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