{"id":2197,"date":"2018-02-19T18:44:00","date_gmt":"2018-02-19T09:44:00","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?post_type=event&#038;p=2197"},"modified":"2022-10-04T18:46:28","modified_gmt":"2022-10-04T09:46:28","slug":"20180219_3086","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/event\/20180219_3086\/","title":{"rendered":"16th CiNet Monthly Seminar: Radoslaw Martin Cichy \u201cDynamics of visual cognition: A spatio-temporally resolved and algorithmically explicit account\u201d"},"content":{"rendered":"\n<p><strong>CiNet Monthly Seminar<\/strong><\/p>\n\n\n\n<p>2018\u5e743\u67088\u65e5\uff08\u6728\uff09<br>15:00 ~ 16:00<br>\u4f1a\u5834 \uff1a CiNet\u30001F\u3000\u5927\u4f1a\u8b70\u5ba4<\/p>\n\n\n\n<p>\u201cDynamics of visual cognition: A spatio-temporally resolved and algorithmically explicit account\u201d<\/p>\n\n\n\n<p>Radoslaw Martin Cichy<br>Research Group Leader<br>Department of Psychology and Education<br>Free Universit Berlin<\/p>\n\n\n\n<p>Host :\u00a0\u5929\u91ce \u85ab\u00a0(PI)<\/p>\n\n\n\n<p>Abstract:<br>Understanding visual cognition in the brain requires answering three questions: what is happening where and when in the human brain when we see? In this talk I will present recent work that addresses these questions in an integrated analysis framework combining human magnetoencephalography (MEG), functional magnetic resonance imaging(fMRI) and deep neural networks (DNNs). The talk has three parts. In the first part, I will show how fMRI and MEG can be combined using multivariate analysis techniques classification plus representational similarity analysis) to yield a spatio-temporally integrated view of human brain activity during object vision (Cichy et al., 2014 NatNeuro). In the second part I will show how DNNs can be used to understand the human visual system. In one study, we showed that DNNs predicted the spatial-temporal hierarchy of the human visual system. In another study, we showed that representations of abstract visual properties, such as scene size, find an analogue in DNNs. In the third, shorter part,I will describe ongoing work and future directions.<\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event\/2197"}],"collection":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event"}],"about":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/types\/event"}],"wp:attachment":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/media?parent=2197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}