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タイトル: | Generic decoding of seen and imagined objects using hierarchical visual features. |
著者: | Horikawa, Tomoyasu Kamitani, Yukiyasu https://orcid.org/0000-0002-9300-8268 (unconfirmed) |
著者名の別形: | 堀川, 友慈 神谷, 之康 |
発行日: | 22-May-2017 |
出版者: | Springer Nature |
誌名: | Nature communications |
巻: | 8 |
論文番号: | 15037 |
抄録: | Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval. |
記述: | 脳から深層ニューラルネットワークへの信号変換による脳内イメージ解読--「脳-機械融合知能」の実現に向けて--. 京都大学プレスリリース. 2017-05-22. |
著作権等: | ©The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
URI: | http://hdl.handle.net/2433/224942 |
DOI(出版社版): | 10.1038/ncomms15037 |
PubMed ID: | 28530228 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2017-05-22-4 |
出現コレクション: | 学術雑誌掲載論文等 |
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