{"id":1459,"date":"2019-03-06T09:43:00","date_gmt":"2019-03-06T00:43:00","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?p=1459"},"modified":"2022-09-20T14:21:39","modified_gmt":"2022-09-20T05:21:39","slug":"20190228_3279","status":"publish","type":"news","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/news\/20190228_3279\/","title":{"rendered":"\u8b1b\u6f14\u3092\u4e00\u90e8\u63b2\u8f09\u3057\u307e\u3057\u305f\uff1aThe 5th CiNet Conference: Computation and representation in brains and machines"},"content":{"rendered":"\n
\u8b1b\u6f14:<\/strong> Shun-ichi Amari, RIKEN David Cox, MIT-IBM Watson AI Lab\/Harvard University<\/strong> Aapo Hyv\u00e4rinen, University College London\/University of Helsinki<\/strong> Odelia Schwartz, University of Miami<\/strong> Taro Toyoizumi, RIKEN <\/strong> Kai Wang, NEC Corporation<\/strong> \u30b9\u30e9\u30a4\u30c9:<\/strong><\/p>\n\n\n\n Ana Lu\u00edsa Pinho, Inria, CEA, Paris-Saclay University<\/strong>
(\u4e00\u90e8\u306e\u8b1b\u6f14\u306b\u304a\u3044\u3066\u3001\u958b\u59cb\u66421\u30012\u5206\u9593\u306e\u97f3\u58f0\u304c\u53d6\u308c\u3066\u3044\u306a\u3044\u90e8\u5206\u304c\u3042\u308a\u307e\u3059)<\/p>\n\n\n\n
<\/strong>\u201cStatistical Neurodynamics of Deep Networks: Signal Propagation and Fisher Information\u201d<\/em><\/a><\/p>\n\n\n\n
\u201cPredictive Coding Models of Perception\u201d<\/em><\/a><\/p>\n\n\n\n
<\/strong>\u201cNonlinear independent component analysis: A principled framework for unsupervised learning\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cImage statistics and cortical visual processing: V1, V2, and deep learning\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cAn Optimization Approach to Understand Biological Search\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cExperimental Platform for brain function model design\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cIndividual Brain Charting, a high-resolution fMRI dataset for cognitive mapping of the human brain\u201d<\/a><\/em><\/p>\n\n\n\n