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Effects of temporally correlated noise on coherence resonance chimeras in FitzHugh-Nagumo neurons

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Abstract

Chimera state in neuronal network means the coexistence of coherent and incoherent firing patterns. In this paper, the FitzHugh-Nagumo (FHN) neuronal model is employed to investigate the coherent resonance chimeras induced by temporally correlated noises. We show that the oscillation state of neuronal system is influenced by the correlation time of noise, the reduction of correlation time can result in a smaller amplitude noise sufficient to maximize the coherent resonance. However, the coherent resonance chimeras can be observed by changing both noise intensity and correlation time in the nonlocally coupled FHN neural network. By virtue of statistical synchronization factor and coherence measurement, it is found that the region of noise intensity threshold for generating coherent resonance chimeras is increased with the increasing of correlation time. The collective behavior of the system is particularly sensitive to the variation of noise intensity when correlation time is greater than 0.1. Furthermore, we demonstrate that the variation of noise intensity or correlation time can cause coherent resonance multi-chimeras in the neural network.

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References

  1. D. Nozaki, D.J. Mar, P. Grigg, J.J. Collins, Phys. Rev. Lett. 82, 2402 (1999)

    ADS  Google Scholar 

  2. B. Jia, H. Gu, Int. J. Bifurc. Chaos 27, 1750113 (2017)

    Google Scholar 

  3. J. Wang, X. Guo, H. Yu, C. Liu, B. Deng, X. Wei, Y. Chen, Chaos Solitons Fractals 60, 40 (2014)

    ADS  MathSciNet  Google Scholar 

  4. C. Zhou, J. Kurths, Phys. Rev. Lett. 88, 230602 (2002)

    ADS  Google Scholar 

  5. Y. Wang, Y. Lai, Z. Zheng, Phys. Rev. E 79, 056210 (2009)

    ADS  Google Scholar 

  6. C. Zhou, J. Kurths, Phys. Rev. E 65, 040101 (2002)

    ADS  MathSciNet  Google Scholar 

  7. P. Lin, C. Wang, Z. Wu, Eur. Phys. J. B 92, 113 (2019)

    ADS  Google Scholar 

  8. G. Malescio, Phys. Rev. E 53, 6551 (1996)

    ADS  Google Scholar 

  9. M. Ozer, M. Perc, M. Uzuntarla, Phys. Lett. A 373, 964 (2009)

    ADS  Google Scholar 

  10. K. Wiesenfeld, F. Moss, Nature 373, 33 (1995)

    ADS  Google Scholar 

  11. Y. Xu, J. Li, F. Jing, H. Zhang, W. Xu, J. Duan, Eur. Phys. J. B 86, 198 (2013)

    ADS  Google Scholar 

  12. M.C. Gimenez, Eur. Phys. J. B 89, 83 (2016)

    ADS  Google Scholar 

  13. A.S. Pikovsky, J. Kurths, Phys. Rev. Lett. 78, 775 (1997)

    ADS  MathSciNet  Google Scholar 

  14. G. Hu, T. Ditzinger, C.Z. Ning, H. Haken, Phys. Rev. Lett. 71, 807 (1993)

    ADS  Google Scholar 

  15. Q. Wang, M. Perc, Z. Duan, G. Chen, Phys. Lett. A 372, 5681 (2008)

    ADS  Google Scholar 

  16. M. Perc, Chaos Solitons Fractals 31, 64 (2007)

    ADS  MathSciNet  Google Scholar 

  17. Y. Xu, Y. Jia, M. Ge, L. Lu, L. Yang, X. Zhan, Neurocomputing 283, 196 (2018)

    Google Scholar 

  18. J. Sawicki, I. Omelchenko, A. Zakharova, E. Schöll, Eur. Phys. J. B 92, 54 (2019)

    ADS  Google Scholar 

  19. S. Mangioni, R. Deza, H.S. Wio, R. Toral, Phys. Rev. Lett. 79, 2389 (1997)

    ADS  Google Scholar 

  20. P. Hänggi, P. Jung, C. Zerbe, F. Moss, J. Stat. Phys. 70, 25 (1993)

    ADS  Google Scholar 

  21. F. Duan, F. Chapeau-Blondeau, D. Abbott, PLoS ONE 9, e91345 (2014)

    ADS  Google Scholar 

  22. H. Busch, M.-Th. Hütt, F. Kaiser, Phys. Rev. E 64, 021105 (2001)

    ADS  Google Scholar 

  23. Y. Xu, J. Ma, H. Wang, Y. Li, J. Kurths, Eur. Phys. J. B 90, 194 (2017)

    ADS  Google Scholar 

  24. H. L, Y. Xu, J. Kurths, X. Yue, Eur. Phys. J. B 92, 76 (2019)

    ADS  Google Scholar 

  25. Y. Xu, H. Ying, Y. Jia, J. Ma, T. Hayat, Sci. Rep. 7, 43452 (2017)

    ADS  Google Scholar 

  26. L. Lu, Y. Jia, J.B. Kirunda, Y. Xu, M. Ge, Q. Pei, L. Yang, Nonlinear Dyn. 95, 1673 (2019)

    Google Scholar 

  27. X. Sun, Z. Liu, M. Perc, Nonlinear Dyn. 96, 2145 (2019)

    Google Scholar 

  28. R. Wang, J. Li, M. Du, J. Lei, Y. Wu, Commun. Nonlinear Sci. 40, 80 (2016)

    Google Scholar 

  29. R. Wang, Y. Zhu, Cogn. Neurodyn. 10, 1 (2016)

    MathSciNet  Google Scholar 

  30. Y. Xu, Y. Jia, J. Ma, T. Hayat, A. Alsaedi, Sci. Rep. 8, 1349 (2018)

    ADS  Google Scholar 

  31. E. Yilmaz, M. Ozer, V. Baysal, M. Perc, Sci. Rep. 6, 30914 (2016)

    ADS  Google Scholar 

  32. Z. Yao, J. Ma, Y. Yao, C. Wang, Nonlinear Dyn. 96, 205 (2019)

    Google Scholar 

  33. Z. Rostamia, V-T. Pham, S. Jafari, F. Hadaeghic, J. Ma, Appl. Math. Comput. 338, 141 (2018)

    MathSciNet  Google Scholar 

  34. M. Ge, Y. Jia, Y. Xu, L. Lu, H. Wang, Y. Zhao, Appl. Math. Comput. 352, 136 (2019)

    MathSciNet  Google Scholar 

  35. M. Ge, Y. Jia, B.K. John, Y. Xu, J. Shen, L. Lu, Y. Liu, Q. Pei, X. Zhan, L. Yang, Neurocomputing 320, 60 (2018)

    Google Scholar 

  36. L. Chua, V. Sbitnev, H. Kim, Int. J. Bifurc. Chaos 22, 1230011 (2012)

    Google Scholar 

  37. B. Bao, A. Hu, H. Bao, Q. Xu, M. Chen, H. Wu, Complexity 2018, 3872573 (2018)

    Google Scholar 

  38. S. Wen, R. Hu, Y. Yang, Z. Zeng, T. Huang, Y. Song, IEEE Trans. Syst. Man Cybern. Syst. 49, 1787 (2019)

    Google Scholar 

  39. S. Wang, Y. Cao, T. Huang, S. Wen, Appl. Math. Comput. 361, 294 (2019)

    MathSciNet  Google Scholar 

  40. S. Wen, S. Xiao, Y. Yang, Z. Yan, Z. Zeng, T. Huang, IEEE Trans. Comput. Aid. Des. 38, 1084 (2019)

    Google Scholar 

  41. Y. Xu, Y. Jia, H. Wang, Y. Liu, P. Wang, Y. Zhao, Nonlinear Dyn. 95, 3237 (2019)

    Google Scholar 

  42. Y. Yao, M. Yi, D. Hou, Int. J. Mod. Phys. B 33, 1950053 (2019)

    ADS  Google Scholar 

  43. L. Lu, Y. Jia, Y. Xu, M. Ge, L. Yang, X. Zhan, Sci. China Technol. Sci. 62, 427 (2019)

    Google Scholar 

  44. Y. Yao, H. Deng, M. Yi, J. Ma, Sci. Rep. 7, 43151 (2017)

    ADS  Google Scholar 

  45. Z. Wei, F. Parastesh, H. Azarnoush, S. Jafari, D. Ghosh, M. Perc, M. Slavinec, Europhys. Lett. 123, 48003 (2018)

    Google Scholar 

  46. J. Tang, J. Zhang, J. Ma, J. Luo, Sci. China Inf. Sci. 62, 1134 (2019)

    Google Scholar 

  47. B.K. Bera, S. Majhi, D. Ghosh, M. Perc, Europhys. Lett. 118, 10001 (2017)

    ADS  Google Scholar 

  48. M. Shafiei, F. Parastesh, M. Jalili, S. Jafari, M. Perc, M. Slavinec, Eur. Phys. J. B 92, 36 (2019)

    ADS  Google Scholar 

  49. B.K. Bera, D. Ghosh, M. Lakshmanan, Phys. Rev. E 93, 012205 (2016)

    ADS  MathSciNet  Google Scholar 

  50. S. Rakshit, Z. Faghani, F. Parastesh, S. Panahi, S. Jafari, D. Ghosh, M. Perc, Phys. Rev. E 100, 012315 (2019)

    ADS  Google Scholar 

  51. S. Majhi, B.K. Bera, D. Ghosh, M. Perc, Phys. Life Rev. 28, 100 (2019)

    ADS  Google Scholar 

  52. M.L. Kelly, R.A. Peters, R.K. Tisdale, J.A. Lesku, J. Exp. Biol. 218, 3175 (2015)

    Google Scholar 

  53. L.G. Dominguez, R.A. Wennberg, W. Gaetz, D. Cheyne, O.C. Snead, J.L.P. Velazquez, J. Neurosci. 25, 8077 (2005)

    Google Scholar 

  54. R. FitzHugh, Biophys. J. 1, 445 (1961)

    ADS  Google Scholar 

  55. N. Semenova, A. Zakharova, V. Anishchenko, E. Schöll, Phys. Rev. Lett. 117, 014102 (2016)

    ADS  Google Scholar 

  56. R.F. Fox, I.R. Gatland, R. Roy, G. Vemuri, Phys. Rev. A 38, 5938 (1988)

    ADS  Google Scholar 

  57. I. Omelchenko, Y.L. Maistrenko, P. Hövel, E. Schöll, Phys. Rev. Lett. 106, 234102 (2011)

    ADS  Google Scholar 

  58. M. Wolfrum, O.E. Omelchenko, S. Yanchuk, Y.L. Maistrenko, Chaos 21, 013112 (2011)

    ADS  MathSciNet  Google Scholar 

  59. M. Yi, L. Yang, Phys. Rev. E 81, 061924 (2010)

    ADS  Google Scholar 

  60. I. Franovic, K. Todorovic, N. Vasovic, N. Buric, Phys. Rev. Lett. 108, 094101 (2012)

    ADS  Google Scholar 

  61. S. Brandstetter, M.A. Dahlem, E. Schöll, Philos. Trans. R. Soc. London A 28, 391 (2010)

    ADS  Google Scholar 

  62. S. Majhi, D. Ghosh, Chaos 28, 083113 (2018)

    ADS  MathSciNet  Google Scholar 

  63. Y. Xu, Y. Jia, J.B. Kirunda, J. Shen, M. Ge, L. Lu, Q. Pei, Complexity 2018, 3012743 (2018)

    Google Scholar 

  64. X. Hu, C. Liu, L. Liu, J. Ni, Y. Yao, Nonlinear Dyn. 91, 1541 (2017)

    Google Scholar 

  65. Y. Xu, J. Ma, X. Zhan, L. Yang, Y. Jia, Cogn. Neurodyn. (2019), https://doi.org/10.1007/s11571-019-09547-8

    ADS  Google Scholar 

  66. Y. Liu, J. Ma, Y. Xu, Y. Jia, Int. J. Bifurc. Chaos 29, 19501562 (2019)

    Google Scholar 

  67. J. Ma, G. Zhang, T. Hayat, G. Ren, Nonlinear Dyn. 95, 1585 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Department of Physics, Central China Normal University, Wuhan, 430079, P.R. China

    Ying Xu, Lulu Lu, Mengyan Ge & Ya Jia

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  1. Ying Xu
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  2. Lulu Lu
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  3. Mengyan Ge
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  4. Ya Jia
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Correspondence to Ya Jia.

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Xu, Y., Lu, L., Ge, M. et al. Effects of temporally correlated noise on coherence resonance chimeras in FitzHugh-Nagumo neurons. Eur. Phys. J. B 92, 245 (2019). https://doi.org/10.1140/epjb/e2019-100413-0

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