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Pattern formation induced by gradient field coupling in bi-layer neuronal networks

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Abstract

The pattern formation in heterogeneous excitable media is a common phenomenon for spatiotemporal systems. In this paper, a bi-layer neuronal network is studied in which the two layers are connected using electromagnetic field coupling with two types of coupling gradients (i.e., step-like and cone-like). It is observed that when the central intensity of a gradient fieid is small, cone-like has less destructive effect on the target wave of the first layer compared to step-like. To further study the influence of environmental factors on pattern formation, the central intensity and external stimulation in the gradient field are continuously increased. The results show that the larger central intensity of the gradient field destroys the the target wave of the first layer and may form a spiral wave, and a larger external stimulation is more effective for inducing spiral waves in the second layer. Finally, synchronization factors are used to predict the pattern and formation mechanism of the spiral wave, and it is found that spiral waves are more likely to occur at the second layer with smaller values and the opposite at the first layer.

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: All data are included in the paper.]

References

  1. A.L. Hodgkin, A.F. Huxley, J. Physiol. 117, 500 (1952)

    Google Scholar 

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

    ADS  Google Scholar 

  3. C. Morris, H. Lecar, Biophys. J. 35, 193 (1981)

    ADS  Google Scholar 

  4. H.R. Wilson, J. Theor. Biol. 200, 375 (1999)

    ADS  Google Scholar 

  5. E.M. Izhikevich, IEEE Trans. Neural Netw. 14, 1569 (2003)

    Google Scholar 

  6. J.L. Hindmarsh, R.M. Rose, Proc. R. Soc. Lond. Ser. 221, 87 (1984)

    ADS  Google Scholar 

  7. T.Y. Li, L.L. Lu, Y. Jia, Int. J. Mod. Phys. B 35, 2150117 (2021)

    ADS  Google Scholar 

  8. G.W. Wang, D. Yu, Q.M. Ding et al., Chaos. Soliton Fract. 150, 111210 (2021)

    Google Scholar 

  9. M.Y. Ge, Y. Jia, Y. Xu et al., Appl. Math. Comput. 352, 136 (2019)

    MathSciNet  Google Scholar 

  10. F. Parastesh, M. Mehrabbeik, K. Rajagopal et al., Chaos 32, 013125 (2022)

    ADS  Google Scholar 

  11. K. Rajagopal, A.J.M. Khalaf, F. Parastesh et al., Nonlinear Dyn. 98, 477 (2019)

    Google Scholar 

  12. A. Bandyopadhyay, S. Kar, Appl. Math. Comput. 333, 194 (2018)

    MathSciNet  Google Scholar 

  13. L.L. Lu, Y. Jia, J.B. Kirunda et al., Nonlinear Dyn. 95, 1673 (2019)

    Google Scholar 

  14. Y. Xu, L.L. Lu, M.Y. Ge et al., Eur. Phys. J. B 92, 245 (2019)

    ADS  Google Scholar 

  15. D. Yu, L.L. Lu, G.W. Wang et al., Chaos. Soliton Fract. 147, 111000 (2021)

    Google Scholar 

  16. Z. Hou, J. Ma, X. Zhan et al., Chaos. Soliton Fract. 142, 110522 (2021)

    Google Scholar 

  17. L.L. Lu, J.B. Kirunda, Y. Xu et al., Eur. Phys. J. Spec. Top. 227, 767 (2018)

    Google Scholar 

  18. Y. Xu, J. Ma, X. Zhan et al., Cogn. Neurodyn. 13, 601 (2019)

    Google Scholar 

  19. Q.M. Ding, Y. Jia, Chaos 3, 053102 (2021)

    ADS  Google Scholar 

  20. Z. Rostami, S. Jafari, Cogn. Neurodyn. 12, 235 (2018)

    Google Scholar 

  21. M. Lv, J. Ma, Neurocomputing 205, 375 (2016)

    Google Scholar 

  22. M.Y. Ge, Y. Jia, Y. Xu et al., Nonlinear Dyn. 91, 515 (2018)

    Google Scholar 

  23. L.L. Lu, Y. Jia, Y. Xu et al., Sci. China Technol. Sc. 62, 427 (2019)

    Google Scholar 

  24. Z. Rostami, V.-T. Pham, S. Jafari et al., Appl. Math. Comput. 338, 141 (2018)

    MathSciNet  Google Scholar 

  25. K. Rajagopal, A. Karthikeyan, S. Jafari et al., Int. J. Mod. Phys. B 34, 2050157 (2020)

    ADS  Google Scholar 

  26. Y. Liu, J. Ma, Y. Xu et al., Int. J. Bifurc. Chaos 29, 1950156 (2019)

    Google Scholar 

  27. Y. Xu, Y. Jia, J.B. Kirunda et al., Complexity 2018, 3012743 (2018)

    Google Scholar 

  28. L. Chua, IEEE Trans. Circuit Theory 18, 507 (1971)

    Google Scholar 

  29. D.B. Strukov, G.S. Snider, D.R. Stewart et al., Nature 453, 80 (2008)

    ADS  Google Scholar 

  30. M. Lv, C. Wang, G. Ren et al., Nonlinear Dyn. 85, 1479 (2016)

    Google Scholar 

  31. F. Wu, C. Wang, Y. Xu et al., Sci. Rep. 6, 28 (2016)

    ADS  Google Scholar 

  32. Y. Xu, H.P. Ying, Y. Jia et al., Sci. Rep. 7, 43452 (2017)

    ADS  Google Scholar 

  33. F. Parastesh, K. Rajagopal, F.E. Alsaadi et al., Appl. Math. Comput. 354, 377 (2019)

    MathSciNet  Google Scholar 

  34. N. Kopell, B. Ermentrout, PNAS 101, 15482 (2004)

    ADS  Google Scholar 

  35. D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)

    ADS  Google Scholar 

  36. A.L. Barabási, R. Albert, Science 286, 509 (1999)

    ADS  MathSciNet  Google Scholar 

  37. X. Sun, M. Perc, J. Kurths, Chaos 27, 053113 (2017)

    ADS  MathSciNet  Google Scholar 

  38. Y. Xu, Y. Jia, H.W. Wang et al., Nonlinear Dyn. 95, 3237 (2019)

    Google Scholar 

  39. M.Y. Ge, Y. Jia, L.L. Lu et al., Nonlinear Dyn. 99, 2355 (2020)

    Google Scholar 

  40. G. Wang, L. Yang, X. Zhan et al., Nonlinear Dyn. 107, 3945 (2022)

    Google Scholar 

  41. M. Ge, L. Lu, Y. Xu et al., Chaos Soliton Fract. 133, 109645 (2020)

    Google Scholar 

  42. A.V. Andreev, N.S. Frolov, A.N. Pisarchik et al., Phys. Rev. E 100, 022224 (2019)

    ADS  Google Scholar 

  43. F. Wu, Y. Wang, J. Ma et al., Physica A 493, 54 (2018)

    ADS  MathSciNet  Google Scholar 

  44. H. Qin, C. Wang, N. Cai et al., Physica A 501, 141 (2018)

    ADS  MathSciNet  Google Scholar 

  45. H. Qin, J. Ma, G. Ren, Int. J. Mod. Phys. B 27, 1850298 (2018)

    Google Scholar 

  46. H.X. Qin, J. Ma, C. Wang et al., Sci. China Phys. Mech. Astron. 57, 1918 (2014)

    ADS  Google Scholar 

  47. J. Lechleiter, S. Girard, E. Peralta et al., Science 252, 123 (1991)

    ADS  Google Scholar 

  48. J.Z. Yang, M. Zhang, Phys. Lett. A 352, 69 (2006)

    ADS  Google Scholar 

  49. Y. Wu, B. Wang, X.X. Zhang, et al., Int. J. Mod. Phys. B 33, (201) 1950354

  50. J. Ma, Y. Xu, J. Tang et al., Commun. Nonlinear Sci. Numer. Simul. 34(34), 55 (2016)

    ADS  MathSciNet  Google Scholar 

  51. J. Ma, J. Tang, Nonlinear Dyn. 89, 1569 (2017)

    Google Scholar 

  52. M. Gosak, R. Markovič, J. Dolenšek et al., Phys. Life Rev. 24, 118 (2018)

    ADS  Google Scholar 

  53. Q. Zheng, J. Shen, Commun. Nonlinear Sci. Numer. Simul. 27, 93 (2015)

    ADS  MathSciNet  Google Scholar 

  54. M.C. Cross, P.C. Hohenberg, Rev. Mod. Phys. 65, 851 (1993)

    ADS  Google Scholar 

  55. S. Liu, Y. Wu, J. Li et al., Nonlinear Dyn. 73, 1055 (2013)

    Google Scholar 

  56. R.A. Gray, A.M. Pertsov, J. Jalife, Nature 392, 75 (1998)

    ADS  Google Scholar 

  57. K. Takagaki, C. Zhang, J.-Y. Wu et al., J. Neurosci. Methods 200, 207 (2011)

    Google Scholar 

  58. J. Ma, Q. Liu, H. Ying et al., Commun. Nonlinear Sci. Numer. Simul. 18, 1665 (2013)

    ADS  MathSciNet  Google Scholar 

  59. Z. Wang, Z. Rostami, S. Jafari et al., Chaos Soliton Fract. 128, 229 (2019)

    ADS  Google Scholar 

  60. Y. Li, M. Oku, G. He et al., Neural Netw. 88, 9 (2017)

    Google Scholar 

  61. Z. Rostami, S. Jafari, M. Perc et al., Nonlinear Dyn. 94, 679 (2018)

    Google Scholar 

  62. A.S. Etémé, C.B. Tabi, A. Mohamadou et al., Physica A 533, 122037 (2019)

    MathSciNet  Google Scholar 

  63. Q.D. Li, H.Z. Zeng, J. Li, Nonlinear Dyn. 79, 2295 (2015)

    Google Scholar 

  64. D. Gonze, S. Bernard, C. Waltermann et al., Biophys J. 89, 120 (2005)

    Google Scholar 

  65. J. Ma, C.N. Wang, W.Y. Jin et al., Appl. Math. Comput. 217, 3844 (2010)

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant no.12175080.

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Correspondence to Ya Jia.

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Wu, Y., Ding, Q., Yu, D. et al. Pattern formation induced by gradient field coupling in bi-layer neuronal networks. Eur. Phys. J. Spec. Top. 231, 4077–4088 (2022). https://doi.org/10.1140/epjs/s11734-022-00628-0

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  • DOI: https://doi.org/10.1140/epjs/s11734-022-00628-0

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