of setups as discussed in the supplementary material. Here we focus on the network structure of the cat cerebral cortex for which the mesoscopic connectivity has been probed and detailed [@zamora-lopez2010]. The regions and their connections were discovered by using datasets from tract-tracing experiments analyzed via nonmetric multidimensional scaling, an optimization approach that minimizes the distance between connected structures and maximizes between the unconnected ones [@scannell1993; @scannell1995].

The network contains 53 meso-regions arranged in four communities that closely follow functional subdivisions; namely, visual (16 nodes), auditory (7 nodes), somatomotor (16 nodes) and frontolimbic (14 nodes), as shown in Fig \[RC2\] (a). In addition to these cortical regions, some cortical areas (hubs) form a hidden layer called a *rich-club* and are densely connected to each other and the communities. A set of nodes form a rich-club if their level of connectivity exceeds what would be expected by chance alone. The maximum number of connections in this network is $\Delta = 37$. The network obtained is weighted [@scannell1993; @scannell1995]. For simplicity and to improve the performance in detecting communities, we turn the network into an undirected simple graph [@zamora-lopez2010]. We simulate each mesoregion as a neuron interacting via electrical synapses (see Methods 