Kenji KONDO Mr.
The focus of our laboratory is to study information processing mechanisms in the brain from the viewpoint of statistical science.
Statistical science is the science of methods for dealing with data via probabilistic and statistical models.
More specifically, we study data with uncertainty such as neuronal spike trains by using statistical models such as point process models, state-space models, and Bayesian networks combined with model selection methods.
Another topic of study is Bayesian modeling of information processing systems in the brain and its applications.
・Statistical modeling of neuronal spike trains
・Bayesian modeling of information processing in the brain
"Bayesian predictive densities based on superharmonic priors for the 2-dimensional Wishart model"
Journal of Multivariate Analysis, vol. 100, pp. 2137-2154.(2009).
"Shrinkage priors for Bayesian prediction."
The Annals of Statistics, vol. 34, pp. 808-819.(2006).
Kobayashi, K., and Komaki, F.:
"Information criteria for kernel machines."
IEEE Transactions on Neural Networks, vol. 17, pp. 571-577. (2006).
"Simultaneous prediction of independent Poisson observables."
The Annals of Statistics, vol. 32, pp. 1744-1769. (2004).
"A shrinkage predictive distribution for multivariate normal observables."
Biometrika, vol. 88, pp. 859-864.(2001).