IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v176y2023ics0960077923010068.html
   My bibliography  Save this article

Chaotic resonance in an astrocyte-coupled excitable neuron

Author

Listed:
  • Calim, Ali
  • Baysal, Veli

Abstract

We study the chaotic resonance phenomenon whereby the response of a neuron to a weak signal is amplified with the help of chaotic current stemming from background activity in the brain. This resonance behavior exhibits a bell-shaped curve in terms of detection quality due to increasing chaotic current intensity. Recent experimental studies have shown that astrocytes, which are the most abundant types of glial cells, may be responsible for the regulation of electrophysiological events in neuronal medium. Hence, we consider here a realistic neuronal system which is constituted by a bipartite network consisting of an excitable neuron and an astrocyte. Our analysis reveals that signal detection quality can be greatly enhanced with the astrocyte contribution obtained by appropriate neuronal and astrocytic cell dynamics. We find that depolarization-induced astrocytic glutamate release is able to improve chaotic resonance performance considerably in the presence of an adequately strong interaction between the astrocyte and the excitable neuron receiving a weak signal with a relatively higher frequency. We also show that a moderate production rate of gliotransmitters is required for the astrocyte to affect resonance performance of the neuron. Except for those conditions where the facilitating effect of astrocyte is observed, it can also reduce signal detection performance in the neuron. Furthermore, we demonstrated that intrinsic neuronal excitability is regulated by the astrocyte, via a comparison of resonance behaviors under effects of bias and astrocytic current separately. Taken together, our findings provide a novel insight into the functioning of astrocyte-neuron circuits, in particular the encoding weak signals via chaotic resonance, and suggest that astrocytes play a key role in intrinsic regulation and selectivity in neuronal information processing.

Suggested Citation

  • Calim, Ali & Baysal, Veli, 2023. "Chaotic resonance in an astrocyte-coupled excitable neuron," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010068
    DOI: 10.1016/j.chaos.2023.114105
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923010068
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.114105?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. Palabas, Tugba & Torres, Joaquín J. & Perc, Matjaž & Uzuntarla, Muhammet, 2023. "Double stochastic resonance in neuronal dynamics due to astrocytes," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Baysal, Veli & Solmaz, Ramazan & Ma, Jun, 2023. "Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    3. Li, Tianyu & Wu, Yong & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2022. "Spike-timing-dependent plasticity enhances chaotic resonance in small-world network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    Full references (including those not matched with items on IDEAS)

    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. Li, Tianyu & Wu, Yong & Yang, Lijian & Fu, Ziying & Jia, Ya, 2023. "Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Erkan, Erdem, 2023. "Signal encoding performance of astrocyte-dressed Morris Lecar neurons," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Wu, Huagan & Gu, Jinxiang & Guo, Yixuan & Chen, Mo & Xu, Quan, 2024. "Biphasic action potentials in an individual cellular neural network cell," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    4. Kaijun Wu & Jiawei Li, 2023. "Effects of high–low-frequency electromagnetic radiation on vibrational resonance in FitzHugh–Nagumo neuronal systems," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(9), pages 1-19, September.
    5. Ding, Qianming & Wu, Yong & Hu, Yipeng & Liu, Chaoyue & Hu, Xueyan & Jia, Ya, 2023. "Tracing the elimination of reentry spiral waves in defibrillation: Temperature effects," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    6. Wang, Xueqin & Yu, Dong & Li, Tianyu & Jia, Ya, 2023. "Logistic stochastic resonance in the Hodgkin–Huxley neuronal system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Li, Tianyu & Wu, Yong & Ding, Qianming & Xie, Ying & Yu, Dong & Yang, Lijian & Jia, Ya, 2024. "Social contagion in high-order network with mutation," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    8. Shadizadeh, S. Mohadeseh & Nazarimehr, Fahimeh & Jafari, Sajad & Rajagopal, Karthikeyan, 2022. "Investigating different synaptic connections of the Chay neuron model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. He, Lifang & Jiang, Zhiyuan & Chen, Yezi, 2024. "Unveiling the principles of stochastic resonance and complex potential functions for bearing fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    10. Baysal, Veli & Solmaz, Ramazan & Ma, Jun, 2023. "Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    11. Xu, Quan & Wang, Kai & Chen, Mo & Parastesh, Fatemeh & Wang, Ning, 2024. "Bursting and spiking activities in a Wilson neuron circuit with memristive sodium and potassium ion channels," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    12. Guo, Yitong & Xie, Ying & Ma, Jun, 2023. "Nonlinear responses in a neural network under spatial electromagnetic radiation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    13. Li, Xuening & Xie, Ying & Ye, Zhiqiu & Huang, Weifang & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2024. "Chimera-like state in the bistable excitatory-inhibitory cortical neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 180(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:chsofr:v:176:y:2023:i:c:s0960077923010068. 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.