ダウンロード数: 149
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
---|---|---|---|---|
sdata.2019.12.pdf | 697.59 kB | Adobe PDF | 見る/開く |
タイトル: | Characterization of deep neural network features by decodability from human brain activity |
著者: | Horikawa, Tomoyasu Aoki, Shuntaro C. Tsukamoto, Mitsuaki Kamitani, Yukiyasu https://orcid.org/0000-0002-9300-8268 (unconfirmed) |
著者名の別形: | 神谷, 之康 |
キーワード: | Statistics, Probability and Uncertainty Statistics and Probability Education Library and Information Sciences Information Systems Computer Science Applications |
発行日: | 12-Feb-2019 |
出版者: | Springer Science and Business Media LLC |
誌名: | Scientific data |
巻: | 6 |
論文番号: | 190012 |
抄録: | Achievements of near human-level performance in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent study found that human brain activity patterns measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into DNN feature values given the same inputs. However, not all DNN features are equally decoded, indicating a gap between the DNN and human vision. Here, we present a dataset derived from DNN feature decoding analyses, which includes fMRI signals of five human subjects during image viewing, decoded feature values of DNNs (AlexNet and VGG19), and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual features were highly correlated between subjects, suggesting the systematic differences between the brain and DNNs. We hope the present dataset will contribute to revealing the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications. |
著作権等: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
URI: | http://hdl.handle.net/2433/243567 |
DOI(出版社版): | 10.1038/sdata.2019.12 |
PubMed ID: | 30747910 |
出現コレクション: | 学術雑誌掲載論文等 |
このリポジトリに保管されているアイテムはすべて著作権により保護されています。