This setup enables efficient, high-quality visual reconstructions across subjects from one experimental trial.

We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.

The23 proposed SSCM does coverthe case of non-zero variance, but currently the identifiability proof is only shown in a24 specific case. Inour simulations under non-zero variance settings, we neverobserved that the procedure converged25 to wrong solutions, suggesting that the non-zero-variance case is also identifiable. For the fMRI and cellular data, the null hypothesis was rejected at significance level 0.01. Regarding causal28 structure variation, for fMRI data, it is well-known that neural connectivities may change across different external29 stimuliorintrinsicstates. Forcellular32 data, causal structure may be different across conditions/interventions.(0)Theyare different.

Finally,an EEG forward model based onapre-estimated lead field matrix wasconstructed together with the ROIsource model togenerate scalp EEG observations.

Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.

Neural Information Processing Systems http://nips.cc/

However, the task is challenging due to the noisy nature of fMRI signals and the intricate pattern of brain visual representations.