In network models of spiking neurons, the coupled impact of network structure and synaptic parameters on activity propagation is still an open problem. For spiking networks with hierarchical modular topology , we show that slow spike-train fluctuations emerge due to the increase of either  the global synaptic strength parameter or the network hierarchical level , while the network size remains constant. Through an information-theoretical approach we show that information propagation of activity among adjacent modules is enhanced as the number of modules increases until an optimal value is reached and then decreases . This suggests that there is an optimal interplay between hierarchical level and synaptic strengths for information propagation among modules, but we also found that information transfer measured from the spike-trains differs from this one indicating that URLanization restructures information communicated in the mesoscopic level . By examining the  increase of synaptic strengths and number of modules we find that the network behavior changes following different mechanisms: (1 ) increase of autocorrelations among individual neurons, and ( 2 ) increase of cross-correlations among pairs of neurons , respectively. The latter being better for information propagation .  Our results have important implications and suggest roles that link topological features and synaptic  levels to the transmission of information in cortical networks. 
In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here we use an information-theoretical approach to investigate activity propagation in spiking networks with hierarchical modular topology . We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network , while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases , showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges . By examining the network behavior under increase of synaptic strength and number of modules we find that these increases are associated with two different effects: (i ) increase of autocorrelations among individual neurons, and ( ii ) increase of cross-correlations among pairs of neurons . The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks. v
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