set of nodes $C_0$, estimating in particular the number of repetitions necessary to obtain statistically meaningful values for the evaluation functions. If conclusions drawn from one case were highly susceptible to small changes in the initial layout, then the model would obviously have less significance than if there was some invariance with respect to abstract topological features (in particular the distribution of centers’ activities). The optimization heuristics would have to be designed very differently in these two cases.

Toward this assessment, we ran a large number of simulations under the same parameter values but starting from different initial $C_0$ configurations, and collected statistics on the output. For each of the 15 binary combinations of $\alpha_k$’s (excluding all zero), standard deviations were calculated over 500 runs. We obtained narrow peak distributions in most cases, with Gaussian widths typically at 10% of the mean function value (Fig. \[fig\_hists\]). In order to ensure that these were the typical widths on all the parameter space and not only on extreme binary points, we also explored the grid $\{0;0.5;1\}^{4}$ with 100 runs per point, assessing in a more representative subspace the relative spread of distributions. This confirmed that the evaluation functions were significantly less sensitive 