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

Vinicius Lima, Renan O. Shimoura, João Paulo Novato, Antonio C. Roque

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number228
JournalBrain Sciences
Volume10
Issue number4
DOIs
StatePublished - Apr 2020

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • Cortical network models
  • Delayed transfer entropy
  • Hierarchical modular networks
  • Neural activity fluctuations
  • Neural information processing

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