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In network models of spiking neurons, the joint impact of network structureand synaptic parameters on activity propagation is still an open problem. Herewe use an information-theoretical approach to investigate activity propagationin spiking networks with hierarchical modular topology. We observe thatoptimized pairwise information propagation emerges due to the increase ofeither (i) the global synaptic strength parameter or (ii) the number of modulesin the network, while the network size remains constant. At the populationlevel, information propagation of activity among adjacent modules is enhancedas the number of modules increases until a maximum value is reached and thendecreases, showing that there is an optimal interplay between synaptic strengthand modularity for population information flow. This is in contrast toinformation propagation evaluated among pairs of neurons, which attains maximumvalue at the maximum values of these two parameter ranges. By examining thenetwork behavior under increase of synaptic strength and number of modules wefind that these increases are associated with two different effects: (i)increase of autocorrelations among individual neurons, and (ii) increase ofcross-correlations among pairs of neurons. The second effect is associated withbetter information propagation in the network. Our results suggest roles thatlink topological features and synaptic strength levels to the transmission ofinformation in cortical networks.v

brain sciences

Optimal interplay between synaptic strengths and network structure  enhances activity fluctuations and information propagation in hierarchical  modular networks