Art

J-GLOBAL ID：202002268582161371   Reference number：20A0429974

End-to-End Deep Image Reconstruction From Human Brain Activity

ヒト脳活動からの末端から端までの深部画像再構成【JST・京大機械翻訳】

Author (6)： Shen Guohua (Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan) ,  Dwivedi Kshitij (Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan) ,  Majima Kei (Graduate School of Informatics, Kyoto University, Kyoto, Japan) ,  Horikawa Tomoyasu (Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan) ,  Kamitani Yukiyasu (Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan) ,  Kamitani Yukiyasu (Graduate School of Informatics, Kyoto University, Kyoto, Japan)
Material： Frontiers in Computational Neuroscience (Web)  (Front Comput Neurosci (Web))
Volume： 13  Page： 21  Publication year： 2019 
JST Material Number： U7036A  ISSN： 1662-5188  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Deep neural networks (DNNs) ha...

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Thesaurus term： neural network (network) ,  *human ,  *image reconstruction ,  decoding ,  end-to-end signaling system ,  *reconstitution ,  ablation ,  constituted model ,  network ,  visual sense ,  Linear Models ,  *brain activity
Semi thesaurus term： 特徴空間 ,  自然画像 ,  訓練データ , 【Automatic Indexing@JST】

Author keywords (5)： brain decoding ,  visual image reconstruction ,  functional magnetic resonance imaging ,  deep neural networks ,  generative adversarial networks

JST classification (2)： Brain and nervous system models  (EL02050C) ,  Central nervous system  (EJ12030F)

Reference (28)：

• Agrawal P., Stansbury D., Malik J., Gallant J. L. (2014). Pixels to Voxels: Modeling Visual Representation in the Human Brain. arXiv [Preprint] arXiv:1407.5104. Available online at: https://arxiv.org/abs/1407.5104 (accessed October 23, 2017).
• Cichy R. M., Khosla A., Pantazis D., Torralba A., Oliva A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6:27755. doi: 10.1038/srep27755
• Cowen A. S., Chun M. M., Kuhl B. A. (2014). Neural portraits of perception: reconstructing face images from evoked brain activity. NeuroImage 94, 12-22. doi: 10.1016/j.neuroimage.2014.03.018
• Deng J., Dong W., Socher R., Li L., Li K., Fei-Fei L. (2009). "ImageNet: a large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition (Miami, FL), 248-255. doi: 10.1109/CVPR.2009.5206848
• Dosovitskiy A., Brox T. (2016a). "Inverting visual representations with convolutional networks," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV), 4829-4837. doi: 10.1109/CVPR.2009.5206848

Terms in the title (5)： ヒト ,  脳活動 ,  端 ,  深部 ,  画像再構成