2012 Fiscal Year Final Research Report
Mathematical analysis of locomotion, learning, and memory using stochastic models
| Project Area | Systems molecular ethology to understand the operating principle of the nervous system |
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| Project/Area Number |
20115009
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| Research Category |
Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
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| Allocation Type | Single-year Grants |
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| Review Section |
Biological Sciences
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MASUDA Naoki 東京大学, 大学院・情報理工学系研究科, 准教授 (40415295)
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| Co-Investigator(Kenkyū-buntansha) |
OHKUBO Jun 京都大学, 情報学研究科, 講師 (70451888)
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| Co-Investigator(Renkei-kenkyūsha) |
NOZAKI Daichi 東京大学, 教育学研究科, 教授 (70360683)
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| Project Period (FY) |
2008 – 2012
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| Keywords | ネットワーク / 統計物理学 / 確率過程 / ランダム・ウォーク / 走性 / 学習 |
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| Research Abstract |
First, we analyzed a data set obtained from locomotion of Caenorhabditis elegans and revealed its long-tailed nature. Second, we proposed a measure of importance of nodes in directed networks, corresponding to neurons, for example, in biological networks, using the so-called Laplacian matrix of the network. Third, we used a coupled oscillator model to quantify the precision of oscillatory rhythms in synchronous cell networks. Fourth, we applied the maximum entropy model to data of brain networks to estimate the network structure to find that the proposed method inferred the anatomical network with a higher accuracy than previous methods did. Fifth, we analyzed general temporal networks in which events were dynamical objects on links in networks.
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[Journal Article] A pairwise maximum entropy model accurately describes resting-state human brain networks2013
Author(s)
Watanabe, T., Hirose, S., Wada, H., Imai, Y., Machida, T., Shirouzu, I., Konishi, S., Miyashita, Y., and Masuda, N
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Journal Title
Nature Communications
Volume: 4
Pages: 1370
Peer Reviewed
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