IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0041375.html
   My bibliography  Save this article

Identifying Controlling Nodes in Neuronal Networks in Different Scales

Author

Listed:
  • Yang Tang
  • Huijun Gao
  • Wei Zou
  • Jürgen Kurths

Abstract

Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats’ brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats’ brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.

Suggested Citation

  • Yang Tang & Huijun Gao & Wei Zou & Jürgen Kurths, 2012. "Identifying Controlling Nodes in Neuronal Networks in Different Scales," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0041375
    DOI: 10.1371/journal.pone.0041375
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041375
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0041375&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0041375?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
    ---><---

    References listed on IDEAS

    as
    1. Jesús Gómez-Gardeñes & Gorka Zamora-López & Yamir Moreno & Alex Arenas, 2010. "From Modular to Centralized Organization of Synchronization in Functional Areas of the Cat Cerebral Cortex," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-11, August.
    2. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    3. Jörg Reichardt & Roberto Alamino & David Saad, 2011. "The Interplay between Microscopic and Mesoscopic Structures in Complex Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    5. Magnus Egerstedt, 2011. "Degrees of control," Nature, Nature, vol. 473(7346), pages 158-159, May.
    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. Yin, Hongli & Zhang, Siying, 2016. "Minimum structural controllability problems of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 467-476.

    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. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2012. "Control Centrality and Hierarchical Structure in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    2. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    3. Noah J Cowan & Erick J Chastain & Daril A Vilhena & James S Freudenberg & Carl T Bergstrom, 2012. "Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-5, June.
    4. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    6. Wang, Jiqiang, 2019. "Disturbance attenuation of complex dynamical systems through interaction topology design," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 576-584.
    7. Ding, Jin & Lu, Yong-Zai & Chu, Jian, 2013. "Studies on controllability of directed networks with extremal optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6603-6615.
    8. Gequn, Liu & Wenhui, Li & Huijie, Yang & Knowles, Gareth, 2014. "The control gain region for synchronization in non-diffusively coupled complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 17-24.
    9. Babak Ravandi & Forough S. Ansari & Fatma Mili, 2020. "Controllability Analysis Of Complex Networks Using Statistical Random Sampling," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, January.
    10. Jacob D Feala & Jorge Cortes & Phillip M Duxbury & Andrew D McCulloch & Carlo Piermarocchi & Giovanni Paternostro, 2012. "Statistical Properties and Robustness of Biological Controller-Target Networks," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-11, January.
    11. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    12. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    13. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    14. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    15. T. Botmart & N. Yotha & P. Niamsup & W. Weera, 2017. "Hybrid Adaptive Pinning Control for Function Projective Synchronization of Delayed Neural Networks with Mixed Uncertain Couplings," Complexity, Hindawi, vol. 2017, pages 1-18, August.
    16. Wenle Zhang & Jianchang Liu, 2016. "Ultra-fast consensus of discrete-time multi-agent systems with multi-step predictive output feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1465-1479, April.
    17. Wang, Qingyun & Zheng, Yanhong & Ma, Jun, 2013. "Cooperative dynamics in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 19-27.
    18. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    19. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    20. Miao, Qingying & Rong, Zhihai & Tang, Yang & Fang, Jianan, 2008. "Effects of degree correlation on the controllability of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6225-6230.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0041375. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.