IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v536y2019ics0378437119314426.html
   My bibliography  Save this article

Effects of external stimulations on transition behaviors in neural network with time-delay

Author

Listed:
  • Huang, Shoufang
  • Zhang, Jiqian
  • Hu, Chin-Kun

Abstract

We used the Hindmarsh–Rose(HR) model to study the effects of external stimulus on the time delay-induced firing behaviors and synchronization in neural networks with N coupled neurons. As time delay is changed, neural networks exhibit diversity of firing behaviors, we discussed the effect of intensity and frequency of external stimulus on these phenomena. Our results imply that both stimulus intensity and stimulus frequency have a non-trivial impact on firing patterns transitions (FPTs) behaviors and synchronization transitions in neural networks, including the change in the critical value required for time delay-induced FPT behavior and generating new modes of transition between synchronization and desynchronization. These findings provide new insight into the role of external stimulus in the firing activities of neural networks, and can help to better understand the firing phenomena in neural networks.

Suggested Citation

  • Huang, Shoufang & Zhang, Jiqian & Hu, Chin-Kun, 2019. "Effects of external stimulations on transition behaviors in neural network with time-delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119314426
    DOI: 10.1016/j.physa.2019.122517
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119314426
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122517?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. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5473-5480.
    2. Wang, Qingyun & Zheng, Yanhong & Ma, Jun, 2013. "Cooperative dynamics in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 19-27.
    3. Yu, Haitao & Wang, Jiang & Liu, Qiuxiang & Sun, Jianbing & Yu, Haifeng, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid synapses," Chaos, Solitons & Fractals, Elsevier, vol. 48(C), pages 68-74.
    4. Gong, Yubing & Xie, Yanhang & Lin, Xiu & Hao, Yinghang & Ma, Xiaoguang, 2010. "Ordering chaos and synchronization transitions by chemical delay and coupling on scale-free neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 43(1), pages 96-103.
    5. Huang, Shoufang & Zhang, Jiqian & Wang, Maosheng & Hu, Chin-Kun, 2018. "Firing patterns transition and desynchronization induced by time delay in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 88-97.
    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. Liu, Chen & Wang, Jiang & Wang, Lin & Yu, Haitao & Deng, Bin & Wei, Xile & Tsang, Kaiming & Chan, Wailok, 2014. "Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses," Chaos, Solitons & Fractals, Elsevier, vol. 59(C), pages 1-12.
    2. Huang, Shoufang & Zhang, Jiqian & Wang, Maosheng & Hu, Chin-Kun, 2018. "Firing patterns transition and desynchronization induced by time delay in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 88-97.
    3. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2014. "Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 25-34.
    4. Deng, Bin & Zhu, Zechen & Yang, Shuangming & Wei, Xile & Wang, Jiang & Yu, Haitao, 2016. "FPGA implementation of motifs-based neuronal network and synchronization analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 388-402.
    5. Wang, Jiang & Guo, Xinmeng & Yu, Haitao & Liu, Chen & Deng, Bin & Wei, Xile & Chen, Yingyuan, 2014. "Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses," Chaos, Solitons & Fractals, Elsevier, vol. 60(C), pages 40-48.
    6. Lü, Ling & Li, Chengren & Li, Gang & Sun, Ao & Yan, Zhe & Rong, Tingting & Gao, Yan, 2017. "Design of synchronization technique for uncertain discrete network group with diverse structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 543-551.
    7. Yu, Haitao & Guo, Xinmeng & Qin, Qing & Deng, Yun & Wang, Jiang & Liu, Jing & Cao, Yibin, 2017. "Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 674-687.
    8. Li, Chengren & Lü, Ling & Sun, Ying & Wang, Ying & Wang, Wenjun & Sun, Ao, 2016. "Parameter identification and synchronization for uncertain network group with different structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 624-631.
    9. Yu, Dong & Wu, Yong & Yang, Lijian & Zhao, Yunjie & Jia, Ya, 2023. "Effect of topology on delay-induced multiple resonances in locally driven systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    10. Zhang, Chunrui & Zhang, Xianhong & Zhang, Yazhou, 2018. "Dynamic properties of feed-forward neural networks and application in contrast enhancement for image," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 281-290.
    11. Shin, Ki-Hong & Baek, Woonhak & Kim, Kyungsik & You, Cheol-Hwan & Chang, Ki-Ho & Lee, Dong-In & Yum, Seong Soo, 2019. "Neural network and regression methods for optimizations between two meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 778-796.
    12. Zheng, Y.G. & Bao, L.J., 2017. "Effect of topological structure on synchronizability of network with connection delay," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 145-151.
    13. Gong, Yubing & Wang, Li & Xu, Bo, 2012. "Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 45(4), pages 548-553.
    14. Hao, Yinghang & Gong, Yubing & Wang, Li & Ma, Xiaoguang & Yang, Chuanlu, 2011. "Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 260-268.
    15. Mondal, Argha & Upadhyay, Ranjit Kumar, 2017. "Dynamics of a modified Hindmarsh–Rose neural model with random perturbations: Moment analysis and firing activities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 144-160.
    16. Fedaravičius, Augustinas Povilas & Cao, Maosen & Ragulskis, Minvydas, 2016. "Control of a dendritic neuron driven by a phase-independent stimulation," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 77-83.
    17. Upadhyay, Ranjit Kumar & Mondal, Argha, 2017. "Synchronization of bursting neurons with a slowly varying d. c. current," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 195-208.
    18. Djeundam, S.R. Dtchetgnia & Filatrella, G. & Yamapi, R., 2018. "Desynchronization effects of a current-driven noisy Hindmarsh–Rose neural network," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 204-211.
    19. Pham, Tuan D., 2014. "The butterfly effect in ER dynamics and ER-mitochondrial contacts," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 5-19.
    20. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5473-5480.

    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:phsmap:v:536:y:2019:i:c:s0378437119314426. 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: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.