Hiroshi Yokoyama

This study uses a computational model and machine learning to analyze monkey decision-making in risk-taking tasks, revealing dynamic optimism and pessimism states and a unique control rule for bias.

This paper introduces j-VAR-LiNGAM, a causal discovery method for synchronous neural oscillations, and shows it identifies causal hierarchies in brain networks using synthetic and empirical data.

This paper extends a data assimilation method to multivariate EEGs, enabling parallel estimation of sensor-level network structures and E/I balance changes.

This study validates a data assimilation-based method for estimating prefrontal excitation-inhibition balance using TMS-EEG comparisons.

This paper introduces a data assimilation method to estimate time-varying excitation-inhibition balance from EEG signals and validates its ability to track sub-second changes in human EEG data.

This paper introduces a method to detect changes in dynamic brain network structures using probabilistic inference and applies it to numerical and EEG data, showing increased Kullback-Leibler divergence during structural changes.