title: Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli.
abstract: Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information.
authors.affiliation: Institut für Physik, Humboldt-Universität zu Berlin, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Schmeltzer
authors.affiliation: Universidade Federal do ABC, Santo André, Brazil.
authors.forename: Alexandre Hiroaki
authors.initials: AH
authors.lastname: Kihara
authors.affiliation: Institut für Physik, Humboldt-Universität zu Berlin, Germany.
authors.forename: Igor Michailovitsch
authors.initials: IM
authors.lastname: Sokolov
authors.affiliation: Institut für Physik, Humboldt-Universität zu Berlin, Germany.
authors.forename: Sten
authors.initials: S
authors.lastname: Rüdiger
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission