The properties of the network topologies explored in the current study is not mutually exclusive. For instance, in the neural networks of the brain, SW and SF properties coexist as a result of cost-efficiency trade-off (Chen et al., 2013). In contrast, Watts-Strogatz SW network models generally lack SF properties, and the Barabási-Albert SF network models lack SW properties. In an attempt to model subnetworks bearing a topology that more closely resembles the real neural system, we further consider a hierarchically modular network with a rich-club organization (Zhou et al., 2006; Meunier et al., 2010; Wu et al., 2012; van den Heuvel and Sporns, 2013; Samu et al., 2014; Hilgetag and Goulas, 2016; Zamora-López et al., 2016; Gal et al., 2017). Such a subnetwork is generated by interconnecting two sub-subnetworks (each with N = 25 nodes and pin × N × k/2 intra-connections) with (1 − pin) × N × k/2 inter-connections, where pin designates the probability of intra-connections. The two nodes to be connected are chosen with a probability Πi = kiα/∑jkjα, where ki is the degree of node i,
