RIKEN Center for Brain Science Mathematical Informatics Collaboration Unit

Unit Leader: Fumiyasu Komaki (Ph.D.)

Japanese Page

Research Summary

Acquiring knowledge based on data in neuroscience is becoming increasingly important. By analyzing data using appropriate mathematical and statistical models, we extract information that is difficult to obtain by directly applying existing statistical methods. Techniques such as analyzing neural spike and brain wave data using point process models, decomposition of periodic components, and phase estimation in time series data using state space models, statistical learning with graph embedding, and information geometric methods are employed. In collaboration with researchers within and outside CBS, we advance research in mathematical informatics in neuroscience.

Main Research Fields

Complex Systems

Related Research Fields

Engineering
Informatics
Interdisciplinary Science & Engineering
Mathematical & Physical Sciences
Mathematical informatics
Mathematical modelling
Statistics

Keywords

Statistical modelling
Information geometry
Time series analysis
Graphical model
Statistical learning

Selected Publications

1. Komaki, F.
"Bayesian prediction and estimation based on a shrinkage prior for a Poisson regression model"
Japanese Journal of Statistics and Data Science (2024).
2. Kurata, S., Kuroda, R., and Komaki, F.
"Statistical modeling for temporal dominance of sensations data incorporating individual characteristics of panelists: an application to data of milk chocolate"
Journal of Food Science and Technology, 59, pp. 2420-2428 (2022).
3. Matsuda, T., Homae, F., Watanabe, H., Taga, G., and Komaki, F.
"Oscillator decomposition of infant fNIRS data"
PLoS Computational Biology, 18, e1009985 (2022).
4. Komaki, F.
"Shrinkage priors for nonparametric Bayesian prediction of nonhomogeneous Poisson processes"
IEEE Transactions on Information Theory, 2021, pp. 5305-5317, (2021).
5. Oda, H., and Komaki, F.
"Shrinkage priors on complex-valued circular-symmetric autoregressive processes"
IEEE Transactions on Information Theory, 67, pp. 5318-5333 (2021).
6. Yano, K., Kaneko, R., and Komaki, F.
"Minimax predictive density for sparse count data
Bernoulli, 27, pp. 1212-1238 (2021).
7. Shibue, R., and Komaki, F.
"Deconvolution of calcium imaging data using marked point processes"
PLoS Computational Biology, 16, e1007650 (2020).
8. Shibue, R., and Komaki, F.
"Firing rate estimation using infinite mixture models and its application to neural decoding"
Journal of Neurophysiology, 118, pp. 2902-2913 (2017).
9. Matsuda, T., Kitajo, K., Yamaguchi, Y., and Komaki, F.
"A point process modeling approach for investigating the effect of online brain activity on perceptual switching"
NeuroImage, 152, pp. 50-59 (2017).
10. Matsuda, T., and Komaki, F.
"Time series decomposition into oscillation components and phase estimation"
Neural Computation, 29, pp. 332-367 (2017)

Lab Members

Principal investigator

Fumiyasu Komaki: Unit Leader

Core members

Xiyang Sun: Postdoctoral Researcher
Yuri Inoue: Administrative Part-time Worker I

Contact Information

Brain Science Central Bldg,
2-1 Hirosawa,
Wako, Saitama
351-0198, Japan
Tel: +81-(0)48-467-9688
Email: fumiyasu.komaki [at] riken.jp