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Statistical Estimation of Phase Response Curves
https://ir.soken.ac.jp/records/2477
https://ir.soken.ac.jp/records/2477b7bd39ef-3976-4566-841b-dfa4a5d89f86
| 名前 / ファイル | ライセンス | アクション |
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要旨・審査要旨 (184.3 kB)
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本文 (3.6 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||
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| 公開日 | 2011-12-27 | |||||||
| タイトル | ||||||||
| タイトル | Statistical Estimation of Phase Response Curves | |||||||
| タイトル | ||||||||
| タイトル | Statistical Estimation of Phase Response Curves | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||||
| 資源タイプ | thesis | |||||||
| 著者名 |
中江, 健
× 中江, 健
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| フリガナ |
ナカエ , ケン
× ナカエ , ケン
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| 著者 |
NAKAE, Ken
× NAKAE, Ken
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| 学位授与機関 | ||||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||||
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| 学位名 | 博士（統計科学） | |||||||
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| 内容記述タイプ | Other | |||||||
| 内容記述 | 総研大甲第1420号 | |||||||
| 研究科 | ||||||||
| 値 | 複合科学研究科 | |||||||
| 専攻 | ||||||||
| 値 | 15 統計科学専攻 | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2011-03-24 | |||||||
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| 値 | 2010 | |||||||
| 要旨 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Phase response curve (PRC) describes the response of an oscillator to external perturbation; it is useful to predict and understand synchronized dynamics of oscillators. In recent years, neuroscientists have focused on neurons’ PRCs, and measured them experimentally. This originates from the leading hypotheses that the synchronization of neurons has a functional meaning in the brain.<br/> In this thesis, we propose two statistical methods for estimating PRCs from data; it allows for the correlation of errors in explanatory and response variables of the PRC. This correlation is neglected in previous studies.<br/> The ?rst method is implemented with a replica exchange Monte Carlo technique; this avoids local minima and enables ef?cient calculation of posterior averages. A test with arti?cial data generated by noisy Morris-Lecar equations shows that, in terms of accuracy, this method outperforms conventional regression that ignores errors in the explanatory variable. Experimental data from the pyramidal cells in the rat motor cortex is also analyzed; a case is found where the result with the ?rst method is considerably different from that obtained by conventional regression.<br/> The second method is developed using a transformation that mixes the variables; it effectively removes the correlation. The computation time of this method is considerably less than that of the ?rst method. The same test using the noisy Morris-Lecar equations shows that the second method also outperforms than convectional regression in terms of accuracy.<br/> |
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| 値 | 有 | |||||||
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| 内容記述タイプ | Other | |||||||
| 内容記述 | application/pdf | |||||||
