Abstract
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 a 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 the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the 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.
Highlights
Neurons in the cerebral cortex are interconnected according to selective, i.e., non-random, patterns of connectivity
In order to characterize information flow in the network, we show in Figure 4f the behavior of h TEi in the parameter space spanned by J and H
An important problem in computational neuroscience is the investigation of different dynamics displayed by networks of spiking neurons [23,52,53,54] and in particular the ones that enhance information processing such as dynamics with slow fluctuations [26,42,55]
Summary
Neurons in the cerebral cortex are interconnected according to selective, i.e., non-random, patterns of connectivity. With the help of computational models, the improved connectivity maps are allowing the realization of the long-standing goal of understanding the interplay between structure and dynamics in cortical networks [9,10,11]. It is an open question whether the evolutionary process that generated such a complex cortical wiring is the result of a selection mechanism for optimized region-to-region communication or some higher order function [12,13,14]. Functional and effective connectivity, which respectively relate to statistical dependencies among neural activity in different brain regions and the causal influence of one brain region
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