  This paper proposes a novel neuronal current source localization method based
on Deep Prior that represents a more complicated prior distribution of current
source using convolutional networks. Deep Prior has been suggested as a means
of an unsupervised learning approach that does not require learning using
training data, and randomly-initialized neural networks are used to update a
source location using a single observation. In our previous work, a
Deep-Prior-based current source localization method in the brain has been
proposed but the performance was not almost the same as those of conventional
approaches, such as sLORETA. In order to improve the Deep-Prior-based approach,
in this paper, a depth weight of the current source is introduced for Deep
Prior, where depth weighting amounts to assigning more penalty to the
superficial currents. Its effectiveness is confirmed by experiments of current
source estimation on simulated MEG data.
