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Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

Author

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  • Yilmaz, Ergin
  • Baysal, Veli
  • Ozer, Mahmut
  • Perc, Matjaž

Abstract

We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman–Watts small-world network consisting of stochastic Hodgkin–Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin–Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.

Suggested Citation

  • Yilmaz, Ergin & Baysal, Veli & Ozer, Mahmut & Perc, Matjaž, 2016. "Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 538-546.
  • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:538-546
    DOI: 10.1016/j.physa.2015.10.054
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    References listed on IDEAS

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    1. Yilmaz, Ergin & Uzuntarla, Muhammet & Ozer, Mahmut & Perc, Matjaž, 2013. "Stochastic resonance in hybrid scale-free neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5735-5741.
    2. 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.
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    Cited by:

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    2. 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.
    3. Dai, Shiqi & Lu, Lulu & Wei, Zhouchao & Zhu, Yuan & Yi, Ming, 2022. "Influence of temperature and noise on the propagation of subthreshold signal in feedforward neural network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
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    6. Wu, Fuqiang & Wang, Ya & Ma, Jun & Jin, Wuyin & Hobiny, Aatef, 2018. "Multi-channels coupling-induced pattern transition in a tri-layer neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 54-68.
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    8. Yao, Chenggui & Yao, Yuangen & Qian, Yu & Xu, Xufan, 2022. "Temperature-controlled propagation of spikes in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    9. Lulu Lu & Ya Jia & Wangheng Liu & Lijian Yang, 2017. "Mixed Stimulus-Induced Mode Selection in Neural Activity Driven by High and Low Frequency Current under Electromagnetic Radiation," Complexity, Hindawi, vol. 2017, pages 1-11, October.
    10. Xu, Ying & Jia, Ya & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Synchronization between neurons coupled by memristor," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 435-442.
    11. Qin, Huixin & Wang, Chunni & Cai, Ning & An, Xinlei & Alzahrani, Faris, 2018. "Field coupling-induced pattern formation in two-layer neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 141-152.
    12. 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.
    13. 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.
    14. Peng, Lu & Tang, Jun & Ma, Jun & Luo, Jinming, 2022. "The influence of autapse on synchronous firing in small-world neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    15. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    16. Guo, Xinmeng & Yu, Haitao & Wang, Jiang & Liu, Jing & Cao, Yibin & Deng, Bin, 2017. "Local excitation–inhibition ratio for synfire chain propagation in feed-forward neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 308-316.
    17. 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.
    18. Li, Shanshan & Zhang, Guoshan & Wang, Jiang & Yi, Guosheng, 2019. "Effects of extracellular electric fields on electrical activities of two-compartment autaptic-neurons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    19. Qu, Lianghui & Du, Lin & Cao, Zilu & Hu, Haiwei & Deng, Zichen, 2021. "Pattern transition of neuronal networks induced by chemical autapses with random distribution," Chaos, Solitons & Fractals, Elsevier, vol. 144(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.
    21. 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.
    22. Aghababaei, Sajedeh & Balaraman, Sundarambal & Rajagopal, Karthikeyan & Parastesh, Fatemeh & Panahi, Shirin & Jafari, Sajad, 2021. "Effects of autapse on the chimera state in a Hindmarsh-Rose neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    23. Shengli Guo & Jun Tang & Jun Ma & Chunni Wang, 2017. "Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte," Complexity, Hindawi, vol. 2017, pages 1-13, June.

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