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Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli

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  • Christian Schmeltzer
  • Alexandre Hiroaki Kihara
  • Igor Michailovitsch Sokolov
  • Sten Rüdiger

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.

Suggested Citation

  • Christian Schmeltzer & Alexandre Hiroaki Kihara & Igor Michailovitsch Sokolov & Sten Rüdiger, 2015. "Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    • Handle: RePEc:plo:pone00:0121794
    DOI: 10.1371/journal.pone.0121794
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    Cited by:

    1. Qin, Ying-Mei & Che, Yan-Qiu & Zhao, Jia, 2018. "Effects of degree distributions on signal propagation in noisy feedforward neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 763-774.

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