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Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons

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  • Baysal, Veli
  • Solmaz, Ramazan
  • Ma, Jun

Abstract

The signal encoding capacity of the nervous system is significantly related to the spiking regime of neurons, and the firing behaviors of neurons are vibrant due to their complex structure. Chaotic fluctuations emerge at both micro and macro levels in the neuronal medium. It is thought that chaotic oscillations are the beneficial component, rather than disruptive, for cognitive functions of the nervous system. It is found that external chaotic activity at a suitable level enhances the weak signal encoding performance of neurons, particularly when the weak signal frequency is chosen close to that of the chaotic fluctuations-induced sub-threshold oscillations frequencies. This manipulation of the signal encoding performance of neurons by chaotic fluctuations is explained by the “chaotic resonance” phenomenon. In the current work, we systematically examine the impacts of the extrinsic chaotic stimulus from the Lorenz system on the subthreshold periodic signal encoding performance of Type-I and Type-II Morris-Lecar neurons. Our results show that weak signal encoding performance of both Type-I and Type-II Morris-Lecar neurons exhibits resonance behavior and its resonance mainly depends on external chaotic current intensity. Also, we reveal that suitable chaotic current level for the best encoding in Morris-Lecar neurons of the sub-threshold signal changes with signal frequency. In addition, we indicate that there is a frequency range in which Morris-Lecar neurons are sensitive to signals. We also demonstrate the existence of a chaotic current intensity range at these frequencies, enabling neurons to improve weak signal encoding performance. Finally, it is observed that the frequency ranges in which Type-I and Type-II neurons are sensitive to weak signals are quite different. In this regard, our research provides important insights into the possible roles of chaotic resonance in the weak signal encoding of different types of neurons.

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  • 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).
    • Handle: RePEc:eee:apmaco:v:448:y:2023:i:c:s0096300323001091
    DOI: 10.1016/j.amc.2023.127940
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Erkan, Erdem, 2023. "Signal encoding performance of astrocyte-dressed Morris Lecar neurons," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Dang, Shihong & Bayani, Atiyeh & Tian, Huaigu & Wang, Zhen & Parastesh, Fatemeh & Nazarimehr, Fahimeh, 2024. "Investigating the route to synchronization in real-world neuronal networks of autaptic photosensitive neurons," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    4. Kaijun Wu & Zhaoxue Huang & Mingjun Yan, 2024. "Dynamic behavior of memristor ML neurons and its application in secure communication," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(7), pages 1-21, July.
    5. Calim, Ali & Baysal, Veli, 2023. "Chaotic resonance in an astrocyte-coupled excitable neuron," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    6. 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).

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