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

　この研究は異なる脳の神経活動の表現同士をGromov-Wasserstein最適輸送を用いて教師なしで比較し、視覚野の情報表現の違いを調べた研究です。マウスのNeuropixelデータ、ヒトのfMRIデータで調べた結果、どちらのデータにおいても、同じ視覚領野の中では個体を超えて神経表現が教師なしで揃うことがわかりました。一方、異なる視覚領野の間では高次視覚野同士では情報表現が教師なしで揃うのに対し、低次の視覚野と高次視覚野では教師なしでは揃わないことがわかりました。

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Paper Information

Authors: Ken Takeda, Kota Abe, Jun Kitazono, Masafumi Oizumi

Title: Unsupervised alignment reveals structural commonalities and differences in neural representations of natural scenes across individuals and brain areas

Journal： iScience, Volume 28, Issue 5, 2025, 112427, ISSN 2589-0042

DOI: https://doi.org/10.1016/j.isci.2025.112427.

Abstract: 

Summary

 Neuroscience research aims to identify universal neural mechanisms underlying sensory information encoding by comparing neural representations across individuals, typically using Representational Similarity Analysis. However, traditional methods assume direct stimulus correspondence across individuals, limiting the exploration of other possibilities. To address this, we propose an unsupervised alignment framework based on Gromov-Wasserstein Optimal Transport, which identifies correspondences between neural representations solely from internal similarity structures, without relying on stimulus labels. Applying this method to Neuropixels recordings in mice and fMRI data in humans viewing natural scenes, we found that the neural representations in the same visual cortical areas can be well aligned across individuals in an unsupervised manner. Furthermore, alignment across different brain areas is influenced by factors beyond the visual hierarchy, with higher-order visual areas aligning well with each other, but not with lower-order areas. This unsupervised approach reveals more nuanced structural commonalities and differences in neural representations than conventional methods.