of the model with respect to the spatial initialization, and validates its internal consistency. This also allowed us to rely on a smaller number of runs in our experiments.[]{data-label="fig_hists"}](goodHists2){width="\columnwidth"}

![Assessing the influence of the update scheme on the morphologies. In the $(D,I)$ classification plane, each point corresponds to 3 runs of a given combination of $\alpha_k$ parameters, repeated under a sequential ($n=1$) and under a parallel ($n=20$) update scheme. For each run, the symmetric difference $\Delta$ between the two patterns is computed and its average over the 3 runs is projected on the map. The color of a point highlights its “significance”, defined as the product of its local density (clustered points represent more frequent configurations) and its pattern size, $|\Delta|$ (large patterns are more significant). The scattered points indicate that the model is sensitive to the update scheme for certain parameters. On the other hand, the concentration of significant points near the origin and $D=0.5$ means that corner cases, such as $(1,0,0,0)$, are more robust.[]{data-label="fig_imprint"}](imprintColorordered){width="\columnwidth"}

Sensitivity analysis and parameter space exploration: internal validation
-------------------------------------------------------------------------

#### Sensitivity to initial conditions

To ensure the validity of the results, we investigated the sensitivity of the model to the spatial conditions, the initial 