IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v142y2021ics096007792030905x.html
   My bibliography  Save this article

Reconstruction of coupling structure in network of neuron-like oscillators based on a phase-locked loop

Author

Listed:
  • Sysoeva, Marina V.
  • Sysoev, Ilya V.
  • Prokhorov, Mikhail D.
  • Ponomarenko, Vladimir I.
  • Bezruchko, Boris P.

Abstract

We study the problem of reconstructing the model equations for the network of 3rd order neuron-like oscillators from time series. The nodes of the network are phase-locked loop systems, which are able to exhibit different dynamical regimes including quasiharmonic oscillations, spiking, bursting, and chaotic behavior. Different network topologies are considered, including star, ring, chain, and random architectures.

Suggested Citation

  • Sysoeva, Marina V. & Sysoev, Ilya V. & Prokhorov, Mikhail D. & Ponomarenko, Vladimir I. & Bezruchko, Boris P., 2021. "Reconstruction of coupling structure in network of neuron-like oscillators based on a phase-locked loop," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s096007792030905x
    DOI: 10.1016/j.chaos.2020.110513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792030905X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110513?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jose Casadiego & Mor Nitzan & Sarah Hallerberg & Marc Timme, 2017. "Model-free inference of direct network interactions from nonlinear collective dynamics," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Luis A. Aguirre & Christophe Letellier, 2009. "Modeling Nonlinear Dynamics and Chaos: A Review," Mathematical Problems in Engineering, Hindawi, vol. 2009, pages 1-35, June.
    3. Bezruchko, Boris P. & Smirnov, Dmitry A. & Sysoev, Ilya V., 2006. "Identification of chaotic systems with hidden variables (modified Bock’s algorithm)," Chaos, Solitons & Fractals, Elsevier, vol. 29(1), pages 82-90.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bezruchko, B.P. & Ponomarenko, V.I. & Smirnov, D.A. & Sysoev, I.V. & Prokhorov, M.D., 2021. "Class-oriented techniques for reconstruction of dynamics from time series," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aguirre, Luis A. & Letellier, Christophe, 2016. "Controllability and synchronizability: Are they related?," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 242-251.
    2. Artur Karimov & Erivelton G. Nepomuceno & Aleksandra Tutueva & Denis Butusov, 2020. "Algebraic Method for the Reconstruction of Partially Observed Nonlinear Systems Using Differential and Integral Embedding," Mathematics, MDPI, vol. 8(2), pages 1-22, February.
    3. Li, Zhongyang & Lu, Fei & Maggioni, Mauro & Tang, Sui & Zhang, Cheng, 2021. "On the identifiability of interaction functions in systems of interacting particles," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 135-163.
    4. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    6. Chunheng Jiang & Zhenhan Huang & Tejaswini Pedapati & Pin-Yu Chen & Yizhou Sun & Jianxi Gao, 2024. "Network properties determine neural network performance," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    8. Mangiarotti, Sylvain & Le Jean, Flavie & Huc, Mireille & Letellier, Christophe, 2016. "Global modeling of aggregated and associated chaotic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 82-96.
    9. Bezruchko, B.P. & Ponomarenko, V.I. & Smirnov, D.A. & Sysoev, I.V. & Prokhorov, M.D., 2021. "Class-oriented techniques for reconstruction of dynamics from time series," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    10. Mohammed A. El-Shorbagy & Fatma M. Al-Drees, 2023. "Studying the Effect of Introducing Chaotic Search on Improving the Performance of the Sine Cosine Algorithm to Solve Optimization Problems and Nonlinear System of Equations," Mathematics, MDPI, vol. 11(5), pages 1-25, March.
    11. Strebel, Oliver, 2013. "A preprocessing method for parameter estimation in ordinary differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 93-104.
    12. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    13. Ahmadi, Mohamadreza & Mojallali, Hamed, 2012. "Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1108-1120.
    14. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    15. Ting-Ting Gao & Baruch Barzel & Gang Yan, 2024. "Learning interpretable dynamics of stochastic complex systems from experimental data," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s096007792030905x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.