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Collective behaviors of neural network regulated by the spatially distributed stimuli

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  • Xie, Ying
  • Huang, Weifang
  • Jia, Ya
  • Ye, Zhiqiu
  • Wu, Yong

Abstract

Most external stimuli, including sound, temperature, and illumination, exhibit spatially heterogeneous, and different amplitudes of the same signal are received by neurons at different positions in the neural network. To address this issue, we constructed a grid-like neural network using memristive FitzHugh-Nagumo neurons. The neuronal responses depend on the spatially distributed stimuli, with the stimulus amplitudes being determined by the distance from the central area. Consequently, complete synchronization occurs in the network comprising periodic neurons, chaotic neurons, and their hybrid forms. Periodic patterns maintain the highest Hamilton energy whereas the lowest Hamilton energy appears in chaotic neurons. In a network consisting of chaotic neurons, the synchronization threshold is larger compared to the other types. In particular, the periodic neurons with the highest energy oscillations can regulate the low-energy chaotic neurons into periodic patterns. Similar conclusions are drawn in a chain-like network. The results advance the understanding of the synchronization mechanisms in the presence of spatial heterogeneity.

Suggested Citation

  • Xie, Ying & Huang, Weifang & Jia, Ya & Ye, Zhiqiu & Wu, Yong, 2024. "Collective behaviors of neural network regulated by the spatially distributed stimuli," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).
  • Handle: RePEc:eee:phsmap:v:651:y:2024:i:c:s0378437124005466
    DOI: 10.1016/j.physa.2024.130037
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    References listed on IDEAS

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    1. Xie, Ying & Zhou, Ping & Yao, Zhao & Ma, Jun, 2022. "Response mechanism in a functional neuron under multiple stimuli," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. 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).
    3. Yao, Zhao & Sun, Kehui & Wang, Huihai, 2024. "Collective behaviors of fractional-order FithzHugh–Nagumo network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    4. Fengling Jia & Peiyan He & Lixin Yang, 2024. "A Novel Coupled Memristive Izhikevich Neuron Model and Its Complex Dynamics," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
    5. Yao, Zhao & Sun, Kehui & He, Shaobo, 2024. "Energy variation rate synchronization for coupled chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
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    Cited by:

    1. Lei, Zhao & Zhang, Zixuan & Guo, Qun & Zhu, Zhigang, 2026. "A double–membrane neuron and circuit containing a memcapacitor," Chaos, Solitons & Fractals, Elsevier, vol. 202(P1).
    2. Tang, Yirui & Cheng, Ziyi & Zhou, Xiuying & Jiang, Long & Pei, Qiming & Yu, Dong, 2026. "Edge of chaos shapes synchronization transitions and critical slowing down in coupled auditory neurons," Chaos, Solitons & Fractals, Elsevier, vol. 205(C).
    3. Irankhah, Reza & Mehrabbeik, Mahtab & Parastesh, Fatemeh & Ghassemi, Farnaz & Jafari, Sajad & Chen, Guanrong & Kurths, Jürgen, 2026. "Network Synchronization with an Adaptive Blinking Coupling Scheme: Topological and Dynamical Generalization," Applied Mathematics and Computation, Elsevier, vol. 513(C).
    4. Xie, Ying & Ye, Zhiqiu & Wang, Xueqin & Jia, Ya & Hu, Xueyan & Li, Xuening, 2025. "Temperature effects on the neuronal dynamics and Hamilton energy," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    5. Chunni Wang & Binchi Wang & Zhao Lei & Jun Ma, 2026. "A physical approach to local energy injection control of neural networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 99(3), pages 1-16, March.

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