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神経活動データからの回路結合推定における位置情報とモジュール性に基づく制約
https://doi.org/10.15102/1394.00000808
https://doi.org/10.15102/1394.000008085cc15ec2-da50-477d-9550-bfae977c654a
名前 / ファイル | ライセンス | アクション |
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Final Exam Abstract (42.9 kB)
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Item type | 学位論文 / Thesis or Dissertation(1) | |||||||
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公開日 | 2019-05-21 | |||||||
タイトル | ||||||||
言語 | ja | |||||||
タイトル | 神経活動データからの回路結合推定における位置情報とモジュール性に基づく制約 | |||||||
タイトル | ||||||||
言語 | en | |||||||
タイトル | Spatial and Modular Regularization in Effective Connectivity Inference from Neural Activity Data | |||||||
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_db06 | |||||||
資源タイプ | doctoral thesis | |||||||
ID登録 | ||||||||
ID登録 | 10.15102/1394.00000808 | |||||||
ID登録タイプ | JaLC | |||||||
アクセス権 | ||||||||
アクセス権 | open access | |||||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
著者 (英) |
Schulze, Jessica Verena
× Schulze, Jessica Verena
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抄録 | ||||||||
内容記述タイプ | Other | |||||||
内容記述 | Previous studies of effective connectivity inference from neural activity data benefited from simple regularization approaches such as L1 regularization, which promotes sparseness of the connection matrix. In this thesis we investigate the incorporation of two novel physiologically plausible priors based on spatial and modular organization of the neural circuit in the framework of Bayesian inference. First we formulate a spatial prior which incorporates distance-dependent connectivity in the linear non-linear Poisson (LNP) model. We consider distance-dependent L1 and L2 regularization of connection weights as well as a hierarchical prior with distance-dependent connection probability. We derive maximum a posteriori (MAP) estimation algorithms by gradient descent, Newton method, and Metropolis-Hastings sampling. We test the effectiveness of these algorithms using synthetic data based on physiologically realistic distance-dependent connection weights and clarify the effects of the regularization parameter and data size, as well as the problems with highly synchronous firing and self-connections. The methods are also tested with calcium imaging data from the mouse posterior parietal cortex (PPC). Next we formulate a modularity prior which assumes multiple modules in a network and different within-module and between-module weight distributions. We formulate a MAP inference by combining Gibbs sampling for module membership and Newton’s method for connection weights. The method is validated by synthetic data with various modular structures, including spatially localized modules with distance-dependent connections. | |||||||
口頭試問日 | ||||||||
2018-11-26 | ||||||||
学位授与年月日 | ||||||||
学位授与年月日 | 2018-12-31 | |||||||
学位名 | ||||||||
学位名 | Doctor of Philosophy | |||||||
学位授与番号 | ||||||||
学位授与番号 | 甲第24号 | |||||||
学位授与機関 | ||||||||
学位授与機関識別子Scheme | kakenhi | |||||||
学位授与機関識別子 | 38005 | |||||||
学位授与機関名 | Okinawa Institute of Science and Technology Graduate University | |||||||
著者版フラグ | ||||||||
出版タイプ | VoR | |||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
権利 | ||||||||
権利情報 | © 2018 The Author. |