Figure 3
Distribution of autapse centrality for complex neuronal networks of distinct topologies. For a given network, the centrality measures can be calculated when a single autapse is attached to a node in the network. Varying the node across the network leads to a distribution of each centrality. (a) For a scale-free network, the distribution of Δλ2 and Δλ N , (b) the variations Δλ2 and Δλ N with respect to the neuron degree k, (c) Δλ2 with respect to the theoretical prediction Eq. (2) denoted as \(\Delta {\lambda }_{2}^{th}\), and (d) Δλ N versus \({\rm{\Delta }}{\lambda }_{N}^{th}\), the prediction given by Eq. (3). (e) For an ER random neuronal network, Δλ2 versus \({\rm{\Delta }}{\lambda }_{2}^{th}\). (f) For a small-world network generated by rewiring 5% of the links of a regular lattice, Δλ2 versus \({\rm{\Delta }}{\lambda }_{2}^{th}\). The insets in (e) and (f) show Δλ N versus \({\rm{\Delta }}{\lambda }_{N}^{th}\) for the respective networks. The networks have the same size (N = 100) and average degree (〈k〉 = 6). The parameters of the neuronal dynamics are the same as those in Fig. 2.
