Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine
A model for HRF learned directly from data not based on a priori assumptions. This allows the BOLD signal to be personalized and voxel specific. Voxels can be filtered as relevant based on their predictive ability. Temporal info stored in reservoir computing; trained feed forward NN produces HRF. Learning process robust both to noise and underlying shape of the true HRF signal.
temporal modeling liquid state machines reservoir computing hrf modeling fmri machine learning neural networks