Table 2

Pseudo-code for nonlinear FC-to-SC completion (SC virtual duals to FC)

Algorithm non-linear FC-to-SC completion is
External input: empirical FC (FCemp)
Output: non-linear virtual SC (SCMFM)
Fixed parameters: FC* fitting quality (CCtarget), initial guess SC*(0), learning rate λ, noise level (σ), simulation time (T), range to scan Gstart ≤ G ≤ Gstop, range to scan τstart ≤ τ  ≤  τstop, other frozen Wong-Wang neural mass parameters
 1. FC*(0) = non-linear SC-to-FC completion starting from SC*(0)
 2. Dist = corr(FC*(0), FCemp)
 3. Iteration = 0
While (Dist ≤ CCtarget)
Iteration = iteration + 1
SC*(iteration) = SC*(iteration – 1) + λ*(FC*(iteration) – FC*(iteration))
FC*(iteration) = non-linear SC-to-FC completion starting from SC*(iteration)
Dist = corr(FC*(iteration), FCemp)
Return SCMFM = SC*(iteration)