(A) Average degree centrality distribution of the modeled neuronal plane (32000 neurons, 2 areas, each divided into 5 subregions coverable by an array, 160 possible electrode position, and a maximum of 20 single units per electrode) with distant dependent random connectivity (Figure 3B). The distribution could be best described by a Gaussian model (adjusted R2 = 0.98). (B) Average degree centrality distribution of 12 different subsamplings of the modeled neuronal plane with exactly the same number of neurons as in the real datasets. Line shadings show standard error across subsamplings. Datasets were processed as in Figure 4A. Average degree distribution could be best described by a Gaussian model (adjusted R2 = 1) and only poorly by a power law model (adjusted R2 = 0.17). (C) Dependency of goodness of power law fit, the size of the largest component relative to the whole network, and the level of compartmentalization on average degree k. Different average degrees were generated by varying the distance-dependent connectivity density of the empirically gained data (Figure 3B) by factors of 1/5, 1/4, 1/3, 1/2, 1, 2, 3, 4, and 5 times to create a neuronal plane. Goodness of power law fit was highly correlated with the size of the largest component (adjusted R2 = 0.93) and the compartmentalization (adjusted R2 = 0.93).