IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v596y2022ics0378437122001558.html
   My bibliography  Save this article

Temperature-optimized propagation of synchronous firing rate in a feed-forward multilayer neuronal network

Author

Listed:
  • Yao, Chenggui
  • Xu, Fei
  • Shuai, Jianwei
  • Li, Xiang

Abstract

The environmental temperature plays a critical role in the system functioning. In biological organisms, there often exists an optimal temperature for the most effective functions. In this work, we investigate the effect of temperature on the propagation of firing rate in a feed-forward multilayer neural network in which neurons in the first layer are stimulated by stochastic noises. We then show that the firing rate can be transmitted through the network within a temperature range. We also show that the propagation of the firing rate by synchronization is optimized at an appropriate temperature. Our findings provide new insights and improve our understanding of the optimal temperature observed in the experiments in the living biological systems.

Suggested Citation

  • Yao, Chenggui & Xu, Fei & Shuai, Jianwei & Li, Xiang, 2022. "Temperature-optimized propagation of synchronous firing rate in a feed-forward multilayer neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001558
    DOI: 10.1016/j.physa.2022.127139
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122001558
    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.2022.127139?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.

    Citations

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


    Cited by:

    1. Yao, Chenggui & Yao, Yuangen & Qian, Yu & Xu, Xufan, 2022. "Temperature-controlled propagation of spikes in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Yao, Chenggui & Sun, JianQiang & Jin, Jun & Shuai, Jianwei & Li, Xiang & Yao, Yuangen & Xu, Xufan, 2023. "The power law statistics of the spiking timing in a neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

    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:596:y:2022:i:c:s0378437122001558. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.