神经科学中的无监督对齐:引入Gromov-Wasserstein最佳运输工具箱
Ken Takeda1, Masaru Sasaki1, Kota Abe1
1Graduate School of Arts and Science, The University of Tokyo, Meguro-ku, Tokyo, Japan.
Journal of neuroscience methods
|April 16, 2025
概括
我们介绍了Gromov-Wasserstein最佳传输 (GWOT),这是一种无监督的方法,用于在大脑和模型中比较神经表征. 在不需要直接刺激标签的情况下,GWOT揭示了复杂的结构对应,比传统的监督方法提供了更深入的见解.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 在大脑,物种和人工神经网络之间比较神经表征至关重要.
- 传统的监督对齐方法假设直接刺激对应,当这种假设无效时,限制了它们的应用.
研究的目的:
- 开发和验证用于比较神经表示的无监督对齐方法.
- 解决神经科学研究中监督对齐的局限性.
主要方法:
- 提出了一个基于格罗莫夫-瓦瑟斯坦最佳运输 (GWOT) 的无监督对齐方法.
- GWOT利用代理机构内部的内部关系来识别没有外部标签的对应关系.
- 开发了一个工具箱,GWTune,以促进GWOT在神经科学中的应用.
主要成果:
- GWOT成功地揭示了复杂的结构对应 (一对一,群对群,转移映射).
- 在行为数据,神经活动记录和人工神经网络模型中展示了成功的无监督对齐.
- GWOT确定了详细的结构区别,这些区别通常被监督方法 (如表示性相似性分析) 忽视.
结论:
- GWOT提供了一种细微的方法来分析表示的相似性,处理细粒度和粗的信件.
- 该GWTune工具箱和方法指南旨在提高神经科学中无监督对齐的可访问性和采用性.
- 这项工作为跨大脑和跨物种表示分析提供了新的视角.
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