IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v315y2017icp203-210.html
   My bibliography  Save this article

Influences of autapse and channel blockage on multiple coherence resonance in a single neuron

Author

Listed:
  • Uzun, Rukiye

Abstract

We study how the spiking regularity of a single stochastic Hodgkin–Huxley neuron is effected in the presence of ion channel blocking and autaptic connection. In this study, we consider a chemical autapse expressed by its coupling strength and delay time. It is found that the neuron exhibits multiple coherence resonance (MCR) behavior induced by autaptic time delay at an appropriate level of ion channel blocking and autaptic coupling strength. This MCR behavior increases with the decrement of working potassium ion channels, whereas it decreases or completely disappears with the increment of a fraction of sodium ion channels blocking, regardless of autaptic coupling strength. Furthermore, this behavior is more explicit at intermediate autaptic coupling strength regardless of the ion channel blocking type. We briefly discuss the obtained results with the underlying reasons in terms of ion channel blocking type and autapse parameters. We also showed that ion channel noise, thus membrane patch size, should be at an optimal level to obtain MCR behavior otherwise, this behavior would be destroyed. The obtained results also showed that autaptic time delay is more operative on regularity than its coupling strength regardless of ion channel blocking. Considering the importance of spiking regularity on neuronal information processing, our results may help to understand the intersection of ion channel blocking and autaptic connections of a single neuron.

Suggested Citation

  • Uzun, Rukiye, 2017. "Influences of autapse and channel blockage on multiple coherence resonance in a single neuron," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 203-210.
  • Handle: RePEc:eee:apmaco:v:315:y:2017:i:c:p:203-210
    DOI: 10.1016/j.amc.2017.07.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300317305167
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2017.07.055?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. Schmid, Gerhard & Goychuk, Igor & Hänggi, Peter, 2004. "Controlling the spiking activity in excitable membranes via poisoning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(3), pages 665-670.
    2. Wang, Hengtong & Chen, Yong, 2016. "Response of autaptic Hodgkin–Huxley neuron with noise to subthreshold sinusoidal signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 321-329.
    3. Uzun, Rukiye & Yilmaz, Ergin & Ozer, Mahmut, 2017. "Effects of autapse and ion channel block on the collective firing activity of Newman–Watts small-world neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 386-396.
    4. Yilmaz, Ergin & Ozer, Mahmut, 2015. "Delayed feedback and detection of weak periodic signals in a stochastic Hodgkin–Huxley neuron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 455-462.
    5. Uzuntarla, Muhammet & Uzun, Rukiye & Yilmaz, Ergin & Ozer, Mahmut & Perc, Matjaž, 2013. "Noise-delayed decay in the response of a scale-free neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 202-208.
    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. Ma, Jun & Xu, Wenkang & Zhou, Ping & Zhang, Ge, 2019. "Synchronization between memristive and initial-dependent oscillators driven by noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Ni Zhang & Dongxi Li & Yanya Xing, 2021. "Autapse-induced multiple inverse stochastic resonance in a neural system," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    3. Zhou, Ping & Yao, Zhao & Ma, Jun & Zhu, Zhigang, 2021. "A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    4. Xiao, Fangli & Fu, Ziying & Jia, Ya & Yang, Lijian, 2023. "Resonance effects in neuronal-astrocyte model with ion channel blockage," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    5. Wang, Xianjun & Gu, Huaguang & Jia, Yanbing, 2023. "Nonlinear mechanism for enhanced and reduced bursting activity respectively induced by fast and slow excitatory autapse," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. Wu, Fuqiang & Gu, Huaguang & Jia, Yanbing, 2021. "Bifurcations underlying different excitability transitions modulated by excitatory and inhibitory memristor and chemical autapses," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    7. Njitacke, Zeric Tabekoueng & Takembo, Clovis Ntahkie & Awrejcewicz, Jan & Fouda, Henri Paul Ekobena & Kengne, Jacques, 2022. "Hamilton energy, complex dynamical analysis and information patterns of a new memristive FitzHugh-Nagumo neural network," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. 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. Uzun, Rukiye & Yilmaz, Ergin & Ozer, Mahmut, 2017. "Effects of autapse and ion channel block on the collective firing activity of Newman–Watts small-world neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 386-396.
    2. Ni Zhang & Dongxi Li & Yanya Xing, 2021. "Autapse-induced multiple inverse stochastic resonance in a neural system," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    3. Chunni Wang & Shengli Guo & Ying Xu & Jun Ma & Jun Tang & Faris Alzahrani & Aatef Hobiny, 2017. "Formation of Autapse Connected to Neuron and Its Biological Function," Complexity, Hindawi, vol. 2017, pages 1-9, February.
    4. Yu, Haitao & Galán, Roberto F. & Wang, Jiang & Cao, Yibin & Liu, Jing, 2017. "Stochastic resonance, coherence resonance, and spike timing reliability of Hodgkin–Huxley neurons with ion-channel noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 263-275.
    5. Njitacke, Zeric Tabekoueng & Takembo, Clovis Ntahkie & Awrejcewicz, Jan & Fouda, Henri Paul Ekobena & Kengne, Jacques, 2022. "Hamilton energy, complex dynamical analysis and information patterns of a new memristive FitzHugh-Nagumo neural network," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Li, Jiajia & Wang, Rong & Du, Mengmeng & Tang, Jun & Wu, Ying, 2016. "Dynamic transition on the seizure-like neuronal activity by astrocytic calcium channel block," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 702-708.
    7. Xiao, Fangli & Fu, Ziying & Jia, Ya & Yang, Lijian, 2023. "Resonance effects in neuronal-astrocyte model with ion channel blockage," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    8. Baysal, Veli & Calim, Ali, 2023. "Stochastic resonance in a single autapse–coupled neuron," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    9. 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).
    10. Liu, Yaru & Liu, Shenquan & Zhan, Feibiao & Zhang, Xiaohan, 2020. "Firing patterns of the modified Hodgkin–Huxley models subject to Taylor ’s formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    11. Ma, Jun & Xu, Wenkang & Zhou, Ping & Zhang, Ge, 2019. "Synchronization between memristive and initial-dependent oscillators driven by noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. Wang, Qingyun & Zheng, Yanhong & Ma, Jun, 2013. "Cooperative dynamics in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 19-27.
    13. Lu, Lulu & Ge, Mengyan & Xu, Ying & Jia, Ya, 2019. "Phase synchronization and mode transition induced by multiple time delays and noises in coupled FitzHugh–Nagumo model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    14. Wang, QiuBao & Yang, YueJuan & Zhang, Xing, 2020. "Weak signal detection based on Mathieu-Duffing oscillator with time-delay feedback and multiplicative noise," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    15. Duan, Wei-Long & Fang, Hui & Zeng, Chunhua, 2019. "Second-order algorithm for simulating stochastic differential equations with white noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 491-497.
    16. 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).
    17. Duan, Wei-Long & Lin, Ling, 2021. "Noise and delay enhanced stability in tumor-immune responses to chemotherapy system," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    18. Duan, Wei-Long, 2020. "The stability analysis of tumor-immune responses to chemotherapy system driven by Gaussian colored noises," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    19. 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).
    20. Xie, Huijuan & Gong, Yubing & Wang, Baoying, 2018. "Spike-timing-dependent plasticity optimized coherence resonance and synchronization transitions by autaptic delay in adaptive scale-free neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 1-7.

    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:apmaco:v:315:y:2017:i:c:p:203-210. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.