2024年9月27日　　Friday Lunch Seminar （英語で開催）
12:15 〜 13:00

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申込締め切り：9月26日 正午
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演題：Oscillations as the key to learn time series: A computational approach from simple timing to complex rhythms

大阪大学
先導的学際研究機構附属共生知能システム研究センター
准教授
河合 祐司

担当PI：浅田 稔

Abstract:
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.