IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v565y2021ics0378437120308530.html
   My bibliography  Save this article

Role of inhibitory neurons in temporal correlations of critical and supercritical spontaneous activity

Author

Listed:
  • Raimo, Dario
  • Sarracino, Alessandro
  • de Arcangelis, Lucilla

Abstract

Experimental and numerical results suggest that the brain can be viewed as a system acting close to a critical point, as confirmed by scale–free distributions of relevant quantities in a variety of different systems and models. Less attention has received the investigation of the temporal correlation functions in brain activity in different, healthy and pathological, conditions. Here we perform this analysis by means of a model with short and long-term plasticity which implements the novel feature of different recovery rates for excitatory and inhibitory neurons, found experimentally. We evidence the important role played by inhibitory neurons in the supercritical state: We detect an unexpected oscillatory behaviour of the correlation decay, whose frequency depends on the fraction of inhibitory neurons and their connectivity degree. This behaviour can be rationalized by the observation that bursts in activity become more frequent and with a smaller amplitude as inhibition becomes more relevant.

Suggested Citation

  • Raimo, Dario & Sarracino, Alessandro & de Arcangelis, Lucilla, 2021. "Role of inhibitory neurons in temporal correlations of critical and supercritical spontaneous activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308530
    DOI: 10.1016/j.physa.2020.125555
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120308530
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125555?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. Holger Finger & Marlene Bönstrup & Bastian Cheng & Arnaud Messé & Claus Hilgetag & Götz Thomalla & Christian Gerloff & Peter König, 2016. "Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modelin," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    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. Wang, Guowei & Wu, Yong & Xiao, Fangli & Ye, Zhiqiu & Jia, Ya, 2022. "Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(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. Dominik P. Koller & Michael Schirner & Petra Ritter, 2024. "Human connectome topology directs cortical traveling waves and shapes frequency gradients," Nature Communications, Nature, vol. 15(1), pages 1-20, 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:phsmap:v:565:y:2021:i:c:s0378437120308530. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.