状態空間モデルと先導空間活動パターンによる培養神経回路バースト活動の再構成

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/08/01

タイトル(英語): Reconstruction of Bursting Activity in Cultured Neuronal Network from State-space Model and Leader Spatial Activity Pattern

著者名: 矢田　祐一郎（東京大学 先端科学技術研究センター／東京大学 大学院情報理工学系研究科 知能機械情報学専攻／日本学術振興会），神崎　亮平（東京大学 先端科学技術研究センター／東京大学 大学院情報理工学系研究科 知能機械情報学専攻），高橋　宏知（東京大学 先端科学技術研究センター／東京大学 大学院情報理工学系研究科 知能機械情報学専攻）

著者名(英語): Yuichiro Yada (Research Center for Advanced Science and Technology, The University of Tokyo/Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo/Japanese Society for the Promotion of Science), Ryohei Kanzaki (Research Center for Advanced Science and Technology, The University of Tokyo/Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo), Hirokazu Takahashi (Research Center for Advanced Science and Technology, The University of Tokyo/Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo)

キーワード: 培養神経回路，ネットワークバースト，非負値行列因子分解，状態空間モデル　　Cultured neuronal network，network burst，non-negative matrix factorization，state-space model

要約(英語): A small subset of neurons, called “leader neurons,” has been assumed as the sources of network bursts in dissociated neuronal cultures. In this paper, we proposed a network burst generation model that a network burst is considered as a sequential transition of spatial activity patterns lead by a “leader pattern”. We recorded spontaneous activities of cultured cortical networks with high-density CMOS microelectrode arrays. Spatial patterns were extracted from the high dimensional recorded data using non-negative matrix factorization (NMF). Then, we hypothesized the state-space model where the leader pattern served as input and the others served as states, respectively. After estimating the model parameters from the training data, we attempted to restore the activities of test data with the estimated model. As a result, the spatio-temporal patterns in network bursts were successfully reconstructed from the model, suggesting that the leader pattern is a crucial predictor of the network burst.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.8 （2015） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 971-978 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/8/135_971/_article/-char/ja/