FIGURE 5.

Schematic representation of the algorithm pipeline. Starting from the TFCE-filtered time-series in panel (A), a probability distribution F of all possible normative TFCE values is computed in panel (B). For all percentiles λ_n of the distribution F, a threshold value F(λ_n) is defined and used to binarize the TFCE-filtered signal in panel (C). Each time point of the TFCE-filtered signal has associated with it the spatiotopic coordinates of gaze (p_x and p_y) and the stimulus positions (s_x and s_y), which are converted into retinotopic coordinates B_x and B_y. The resulting mapped retinotopic coordinates in panel (D) are associated with a target location T which contains the average of the aggregated B(t) values at that specific location. B(t) is the expected probability that that specific location is affected by a scotoma. An analogous back-projection algorithm is implemented for the reconstruction of the visual field using the recurrent neural network (RNN), where the binarized time-series in panel (C) is defined by the output of the model instead of the threshold F(λ_n).