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Nonlinear Dynamics

, Volume 98, Issue 2, pp 1029–1039 | Cite as

Extended analysis of stochastic resonance in a modular neuronal network at different scales

  • XiaoLi YangEmail author
  • Na Li
  • ZhongKui Sun
Original paper
  • 37 Downloads

Abstract

Based on the firing dynamics at three different levels of microscale, mesoscale and macroscale, this study presents an extended analysis of stochastic resonance in a modular neuronal network in the spatially correlated white noise environment. Two well-defined modules of small-world subnetwork and scale-free subnetwork constitute the modular neuronal network in a hierarchical way. When a subthreshold periodic input is incorporated into this network, numerical results illustrate that a collective pattern of stochastic resonance emerges at macroscopic scale when the intensity of the correlated noise is appropriately tuned. Through extended analysis, one can detect that the firing rhythms of individual neurons gradually follow those of the periodic input at microscale. In addition, the occurrence of stochastic resonance at mesoscale in the small-world subnetwork is earlier than that in the scale-free subnetwork, and the peak height of resonance curve in the former subnetwork is remarkably higher than that in the latter one. These combined results indicate that the small-world subnetwork is more favorable than the scale-free subnetwork to induce stochastic resonance in this constructed modular network. The robustness of the extended analysis of stochastic resonance against variations in noise correlation coefficient and intra-module probability is also unveiled. This study provides a new perspective and tool to understand the collective phenomenon of stochastic resonance in realistic neuronal systems.

Keywords

Stochastic resonance Modular neuronal network Macroscale Mesoscale Microscale 

Notes

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 11572180, 11972217, 11671243), the Fundamental Funds Research for the Central Universities (Grant Nos. GK201901008, GK201701001).

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Mathematics and Information ScienceShaanxi Normal UniversityXi’anPeople’s Republic of China
  2. 2.Department of Applied MathematicsNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

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