Art

J-GLOBAL ID：201702210972944970   Reference number：17A1362851

深層学習を用いた高次視覚機能の理解にむけて

Author (1)： HAYASHI RYUSUKE (産業技術総合研)
Material： Vision  (Journal of the Vision Society of Japan)
Volume： 29  Issue： 3  Page： 77-85  Publication year： Jul. 20, 2017 
JST Material Number： L4228A  ISSN： 0917-1142  Document type： Article
Article type： 解説  Country of issue： Japan (JPN)  Language： JAPANESE (JA)

Thesaurus term： *neural network (biomodel) ,...
Semi thesaurus term： *deep neural network

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JST classification (2)： Central nervous system  (EJ12030F) ,  Artificial intelligence  (JE08000Z)

Reference (27)：

• A. Krizhevsky, I. Sutskever and G. E. Hinton: ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (NIPS 2012), 1106-1114, 2012.
• R. Hayashi and S. Nishimoto: Decoding visual information in monkey IT cortex using deep neural network. Proceedings of Life Engineering Symposium 2013 (LE2013): 511-514, 2013.
• R. Hayashi and S. Nishimoto: Image reconstruction from neural activity via higher-order visual features derived from deep convolutional neural networks, Neuroscience 2013 (The 43rd Annual Meeting of the Society for Neuroscience), San Diego, USA, Nov. 12, 2013.
• D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert and J. J. DiCarlo: Performance-optimized hierarchical models predict neural responses in higher visual cortex, Proceedings of National Academic Science U.S.A., 111, 8619-8624, 2014.
• N. Kriegeskorte, M. Mur and P. Bandettini: Representational similarity analysis-connecting the branches of systems neuroscience, Frontiers in Systems Neuroscience, 2, 1-28, 2008.

Terms in the title (2)： 深層学習 ,  理解