Natural gradient works efficiently in learning
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Online Model Selection Based on the Variational Bayes
Neural Computation
Journal of Cognitive Neuroscience
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Magnetoencephalography (MEG) can measure brain activity with millisecond-order temporal resolution, but its spatial resolution is poor, due to the ill-posed nature of the inverse problem, for estimating source currents from the electromagnetic measurement. Therefore, prior information on the source currents is essential to solve the inverse problem.We have proposed a new hierarchical Bayesian method to combine several sources of information. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using variational Bayes method. The fMRI information can be imposed as prior distribution rather than the variance itself so that it gives a soft constraint on the variance.It is shown that the hierarchical Bayesian method has better accuracy and spatial resolution than conventional linear inverse methods by evaluating the resolution curve. The proposed method also demonstrated good spatial and temporal resolution for estimating current activity in early visual area evoked by a stimulus in a quadrant of the visual field.