「個性」を創発する脳システムの数理モデル開発と統計データ解析

• Project Area: Integrative research toward elucidation of generative brain systems for individuality
• Project/Area Number: 16H06533
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: The University of Tokyo
• Principal Investigator: 駒木 文保 東京大学, 大学院情報理工学系研究科, 教授 (70242039)
• Project Period (FY): 2016-06-30 – 2021-03-31
• Project Status: Granted (Fiscal Year 2020)
• Budget Amount: ¥58,630,000 (Direct Cost: ¥45,100,000、Indirect Cost: ¥13,530,000); Fiscal Year 2020: ¥10,920,000 (Direct Cost: ¥8,400,000、Indirect Cost: ¥2,520,000); Fiscal Year 2019: ¥10,920,000 (Direct Cost: ¥8,400,000、Indirect Cost: ¥2,520,000); Fiscal Year 2018: ¥10,920,000 (Direct Cost: ¥8,400,000、Indirect Cost: ¥2,520,000); Fiscal Year 2017: ¥10,920,000 (Direct Cost: ¥8,400,000、Indirect Cost: ¥2,520,000); Fiscal Year 2016: ¥14,950,000 (Direct Cost: ¥11,500,000、Indirect Cost: ¥3,450,000)
• Keywords: ベイズ統計 / 数理モデル / 統計モデル / 点過程 / 時系列解析 / スパイクソーティング

Outline of Annual Research Achievements

「個性」を適切に扱うための数理モデルを構築し、開発した数理モデルとベイズ統計に基づく脳科学関連データの解析手法の開発を進めた．また，開発した手法の適用により領域内の共同研究による実データ解析に取り組んだ． 昨年度までに開発に着手していた，カルシウムイメージングなどの画像データからノンパラメトリックベイズ法と無限混合モデルを用いることにより直接ニューラルデコーディングを行う手法についての研究をさらに進め，その成果についてまとめた論文を発表した．このような点過程モデルとノンパラメトリックベイズ法によるデータ解析手法に関連した数理統計学的な研究を進めた．特に，従来の点過程解析手法よりも精度の良い，昨年度までに開発したノンパラメトリックベイズ法による推定手法について，許容性などの数理統計学的に重要な性質についての研究を進めた．また，昨年度までに引き続き，複数の振動子の重ね合わせでデータが構成されていることを表現する統計数理モデルに基づく，時系列データから自動的に振動成分を抽出する統計解析手法を用いて，実データ解析による領域内での複数の共同研究を進めた．さらに，脳活動の光計測に基づくデータのベイズ統計学に基づく解析手法の開発を引き続き進め，領域内の共同研究により昨年度とは異なる実データへの適用を開始した．離散的な観測データから個性に関する基本的な次元を抽出するために，項目反応理論に基づくベイズ解析手法の開発を進めた．

Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

脳科学分野において，実データから知見を得るのに有用な数理モデルの開発とモデルに基づく統計データ解析手法についての研究を進めることができた．特に手法についての数理統計学的性質について新たな知見が得られた．また，開発した数理モデルに基づく手法を用いた共同研究を進めることができた．

Strategy for Future Research Activity

数理モデルとベイズ統計に基づくでデータ解析手法を道いた領域内での共同研究をさらに進めるとともに，領域がも含めた共同研を推進する．また，開発した手法をプログラムの開発・公開により広く利用可能にすることを目指す．

Research Products

• [Journal Article] Deconvolution of calcium imaging data using marked point processes (2020)
  • Author(s): Ryohei Shibue,Fumiyasu Komaki
  • Journal Title: PLOS Computational Biology Volume: 16(3) Issue: 3 Pages: e1007650-e1007650
  • DOI: 10.1371/journal.pcbi.1007650
  • Peer Reviewed / Open Access
• [Journal Article] Paired comparison models with age effects modeled as piecewise quadratic splines (2019)
  • Author(s): Araki Kenji、Hirose Yoshihiro、Komaki Fumiyasu
  • Journal Title: International Journal of Forecasting Volume: 35 Issue: 2 Pages: 733-740
  • DOI: 10.1016/j.ijforecast.2018.02.006
  • Peer Reviewed / Open Access
• [Journal Article] Empirical Bayes matrix completion (2019)
  • Author(s): T. Matsuda and F. Komaki
  • Journal Title: Computational Statistics & Data Analysis Volume: 137 Pages: 195-210
  • DOI: 10.1016/j.csda.2019.02.006
  • Peer Reviewed / Open Access
• [Journal Article] Scoring rules for statistical models on spheres (2018)
  • Author(s): Takasu Y., Yano K., Komaki, F.
  • Journal Title: Statistics & Probability Letters Volume: 138 Pages: 111-115
  • DOI: 10.1016/j.spl.2018.02.054
  • Peer Reviewed / Open Access
• [Journal Article] Second-order matching prior family parametrized by sample size and matching probability (2018)
  • Author(s): Toyoto Tanaka, Yoshihiro Hirose, Fumiyasu Komaki
  • Journal Title: Statistical Papers Volume: 印刷中 Issue: 4 Pages: 1701-1717
  • DOI: 10.1007/s00362-018-1001-5
  • Peer Reviewed / Open Access
• [Journal Article] Information criteria for prediction when the distributions of current and future observations differ (2017)
  • Author(s): Keisuke Yano and Fumiyasu Komaki
  • Journal Title: Statistica Sinica Volume: 印刷中 Pages: 1205-1223
  • DOI: 10.5705/ss.202015.0380
  • Peer Reviewed / Open Access / Acknowledgement Compliant
• [Journal Article] Multivariate time series decomposition into oscillation components (2017)
  • Author(s): Matsuda, T. and Komaki
  • Journal Title: Neural Computation Volume: 29 Pages: 2055-2075
  • Peer Reviewed / Open Access
• [Journal Article] Asymptotically minimax prediction in infinite sequence models (2017)
  • Author(s): Yano, K. and Komaki, F
  • Journal Title: Electronic Journal of Statistics Volume: 11 Issue: 2 Pages: 3165-3195
  • DOI: 10.1214/17-ejs1312
  • Peer Reviewed / Open Access
• [Journal Article] Firing rate estimation using infinite mixture models and its application to neural decoding. (2017)
  • Author(s): Shibue R, Komaki F:
  • Journal Title: Journal of Neurophysiology Volume: 118 Issue: 5 Pages: 2902-2913
  • DOI: 10.1152/jn.00818.2016
  • Peer Reviewed / Open Access
• [Journal Article] Minimax estimation of quantum states based on the latent information priors (2017)
  • Author(s): Koyama, T., Matsuda, T. and Komaki, F.
  • Journal Title: Entropy Volume: 19 Issue: 11 Pages: 618-618
  • DOI: 10.3390/e19110618
  • Peer Reviewed / Open Access
• [Journal Article] A point process modeling approach for investigating the effect of online brain activity on perceptual switching, (2017)
  • Author(s): Matsuda, T., Kitajo, K., Yamaguchi, Y., and Komaki, F.
  • Journal Title: NeuroImage Volume: 152 Pages: 50-59
  • DOI: 10.1016/j.neuroimage.2017.02.068
  • Peer Reviewed
• [Journal Article] Time series decomposition into oscillation components and phase estimation (2017)
  • Author(s): Matsuda, T. and Komaki, F.
  • Journal Title: Neural Computation Volume: 29 Issue: 2 Pages: 332-367
  • DOI: 10.1162/neco_a_00916
  • Peer Reviewed
• [Presentation] Shrinkage priors for nonparametric Bayesian prediction of nonhomogeneous Poisson processes (2019)
  • Author(s): Fumiyasu Komaki
  • Organizer: New and Evolving Roles of Shrinkage in Large-Scale Prediction and Inference (19w5188)
  • Int'l Joint Research / Invited
• [Presentation] A class of tractable predictive densities for curved exponential families (2019)
  • Author(s): Fumiyasu Komaki
  • Organizer: Symposium in Memory of Charles Stein
  • Int'l Joint Research
• [Presentation] Numerical computation of the higher order central moments of the multivariate normal distribution (2018)
  • Author(s): Fumiyasu Komaki
  • Organizer: Ecosta2018
  • Int'l Joint Research
• [Presentation] Statistical verification of the common oscillatory behaviors in oxy-Hb and deoxy-Hb time series (2018)
  • Author(s): Takeru Matsuda, Fumitaka Homae, Hama Watanabe, Gentaro Taga and Fumiyasu Komaki
  • Organizer: fNIRS2018
  • Int'l Joint Research
• [Presentation] Improper priors for nonparametric Bayes estimation of Poisson intensity functions (2018)
  • Author(s): Fumiyasu Komaki
  • Organizer: CMStatistics2018
  • Int'l Joint Research
• [Presentation] Information geometηof predictive densities and its application to Poisson regression Speaker (2017)
  • Author(s): Fumiyasu Komaki
  • Organizer: LICAS2017
  • Int'l Joint Research / Invited
• [Presentation] Neural decoding based on an infinite mixture model (2017)
  • Author(s): Ryohei Shibue, Fumiyasu Komaki
  • Organizer: EcoSta2017
  • Int'l Joint Research
• [Presentation] Shrinkage Priors for Poisson-Based Models and Their Applications (2017)
  • Author(s): Fumiyasu Komaki
  • Organizer: JSM2017
  • Int'l Joint Research
• [Presentation] A predictive approach to statistical problems with multiplicity (2017)
  • Author(s): Fumiyasu Komaki
  • Organizer: CMStatistics2017
  • Int'l Joint Research / Invited
• [Presentation] Information Geometry of predictive densities and its application to Poisson regression (2017)
  • Author(s): Fumiyasu Komaki
  • Organizer: Lilac International Conference of Application on Statistics (LICAS 2017)
  • Int'l Joint Research / Invited
• [Presentation] Shrinkage Priors for Poisson-Based Models and Their Applications (2017)
  • Author(s): Fumiyasu Komaki
  • Organizer: Joint Statistical Meetings 2017(JSM2017)
  • Int'l Joint Research
• [Remarks] 駒木文保／Fumiyasu Komaki
  • URL: http://www.stat.t.u-tokyo.ac.jp/~komaki/index-j.html
• [Remarks] H28年度採択新学術領域 「個性」創発脳
  • URL: http://www.koseisouhatsu.jp/index.html
• [Remarks] H28年度採択新学術領域 「個性」創発脳
  • URL: http://www.koseisouhatsu.jp/index.html