Two work segments might be out of scope due to time constraints. In order of importance:
1) Considering dependency grammar vs phrase grammar. This step requires creating the trees for the ECOG stimuli which can be time consuming. This step is not really necessary for the ECOG/fMRI tests on parsing but would importantly improve the claims regarding simulations on corpuses to select language stimuli. I still need to check if the phrase grammar used by Stan in the ECOG stimuli is comparable to the penntreebank or other corpuses. The point is that I could just settle in comparing parsing schemes assuming the phrase grammar.
2) Smolensky time series prediction would be very nice to accompany NBA, Nonetheless is is very complex to adapt the stochastic diffusion process optimization proposed by Smolensky to biological neuronal circuit simulations. A good start point to try would be circuits used for decision making processes that instantiate diffusion. Nonetheless most of the research in this case is related to pairs of attractors instead of the multidimensional structure necessary by Smolensky proposal with the extra theoretical modifications. I analyzed this at the beginning of the PHD but every time I go back to it I am more convinced this can be a whole PHD in itself. I think this would be a fun thing to try if everything else goes well.
The idea is to be able to get onset pairs difference maps and their z scores. z scores can be obtained from the p values of permutation tests on the onset pair comparison at the group level. The onset has to be related directly to the time to peak of the hemodynamic response to compare it with predictions from simulations.