{"id":4498,"date":"2024-08-21T10:06:27","date_gmt":"2024-08-21T01:06:27","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?post_type=event&p=4498"},"modified":"2024-11-18T09:32:24","modified_gmt":"2024-11-18T00:32:24","slug":"20240927_1751","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/event\/20240927_1751\/","title":{"rendered":"\uff1c\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u958b\u50ac\uff1eFriday Lunch Seminar \u6cb3\u5408 \u7950\u53f8 \uff1a\u201cOscillations as the key to learn time series: A computational approach from simple timing to complex rhythms\u201d"},"content":{"rendered":"\n
2024\u5e749\u670827\u65e5\u3000\u3000Friday Lunch Seminar \uff08\u82f1\u8a9e\u3067\u958b\u50ac\uff09 \u6240\u5c5e\u304c\u7565\u79f0\/\u672a\u8a18\u5165\u306a\u3069\u306b\u3088\u308a\u78ba\u8a8d\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001\u3054\u53c2\u52a0\u3044\u305f\u3060\u3051\u306a\u3044\u5834\u5408\u304c\u3054\u3056\u3044\u307e\u3059\u3002 \u6f14\u984c\uff1aOscillations as the key to learn time series: A computational approach from simple timing to complex rhythms<\/p>\n\n\n\n \u5927\u962a\u5927\u5b66 Abstract: <\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event\/4498"}],"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=4498"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
12:15 \u301c 13:00
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\u4f1a\u5834\u53c2\u52a0\u306e\u65b9\u306f\u76f4\u63a5CiNet\u68df1F\u306e\u5927\u4f1a\u8b70\u5ba4\u306b\u304a\u8d8a\u3057\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
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\u62c5\u5f53PI\uff1a\u6d45\u7530 \u7a14<\/a><\/p>\n\n\n\n
Understanding how the brain learns, generates, and generalizes time series, ranging from simple timing to complex rhythms, is a fundamental question in neuroscience. This talk explores the computational mechanisms underlying these processes, with a focus on how reservoir computing, a type of artificial recurrent neural network, replicates and generalizes long-term temporal patterns. The perception and generation of timing and rhythms involve some brain areas including the basal ganglia and cerebellum. We propose oscillation-driven reservoir computing (ODRC) as a principal computational model for these areas, where oscillatory signals are fed into a random recurrent neural network to stabilize network activity and induce complex neural dynamics. These stable and complex dynamics enable the ODRC to learn long-term motor timing. The ODRC not only replicates target time series but also generalizes them. For example, when the ODRC learns chaotic Lorenz time series for a specific period, it can replicate the series during that period and generate similar time series afterward. This capability of the ODRC was applied to the learning of complex rhythms. Professional drumming performances were encoded into time series and learned by the ODRC. The results showed that the ODRC not only reproduced the performances but also generated similar performances, potentially including improvisations.<\/p>\n\n\n\n