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Stability and Hopf Bifurcation of a Three-Neuron Network with Multiple Discrete and Distributed Delays

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Abstract

In this paper, a class of three-neuron network with discrete and distributed delays is introduced. We first give a detailed Hopf bifurcation analysis for the proposed network. Choosing the discrete time delay as a bifurcation parameter, the existence of Hopf bifurcation is studied. Moreover, by using the normal form theory and center manifold theorem, the formulae determining the direction of the bifurcations and the stability of the bifurcating periodic solutions are derived. Finally, numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

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References

  1. Seow MJ, Asari VK, Livingston A (2010) Learning as a nonlinear line of attraction in a recurrent neural network. Neural Comput Appl 19:337–342

    Article  Google Scholar 

  2. Gangal AS, Kalra PK, Chauhan DS (2007) Performance evaluation of complex valued neural networks using various error functions. Int J Electr Electron Sci Eng 1(5):728–733

    Google Scholar 

  3. Zhu Q, Li X (2012) Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen–Grossberg neural networks. Fuzzy Sets Syst 203:74–94

    Article  MathSciNet  Google Scholar 

  4. Xiao M, Zheng W et al (2015) Undamped oscillations generated by Hopf bifurcations in fractional order recurrent neural networks with Caputo derivative. IEEE Trans Neural Netw Learn Syst 26(12):3201–3214

    Article  MathSciNet  Google Scholar 

  5. Xie W, Zhu Q (2015) Mean square exponential stability of stochastic fuzzy delayed Cohen–Grossberg neural networks with expectations in the coefficients. Neurocomputing 166:133–139

    Article  Google Scholar 

  6. Zhu Q, Cao J, Hayat T (2015) Robust stability of Markovian jump stochastic neural networks with time delays in the leakage terms. Neural Process Lett 41(1):1–27

    Article  Google Scholar 

  7. Wang Z, Huang X, Shi G (2011) Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay. Comput Math Appl 62:1531–1539

    Article  MathSciNet  Google Scholar 

  8. Zhu Q, Cao J (2012) Stability of Markovian jump neural networks with impulse control and time varying delays. Nonlinear Anal Real Word Appl 13(5):2259–2270

    Article  MathSciNet  Google Scholar 

  9. Arik S (2016) Dynamical analysis of uncertain neural networks with multiple time delays. Int J Syst Sci 47(3):730–739

    Article  MathSciNet  Google Scholar 

  10. Xu W, Cao J et al (2015) A new framework for analysis on stability and bifurcation in a class of neural networks with discrete and distributed delays. IEEE Trans Cybern 45(10):2224–2236

    Article  Google Scholar 

  11. Xu W, Cao J et al (2015) Bifurcation analysis of a class of (n+1)-dimension internet congestion control systems. Int J Bifurc Chaos 25(2):1550019

    Article  MathSciNet  Google Scholar 

  12. Liu Y, Zhang D et al (2017) Stability analysis of quaternion-valued neural networks: decomposition and direct approaches. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2017.2755697

    Article  Google Scholar 

  13. Zhu Q, Cao J, Rakkiyappan R (2015) Exponential input-to-state stability of stochastic Cohen–Grossberg neural networks with mixed delays. Nonlinear Dyn 79(2):1085–1098

    Article  MathSciNet  Google Scholar 

  14. Liu Y, Li B et al (2017) Function perturbations on singular Boolean networks. Automatica 84:36–42

    Article  MathSciNet  Google Scholar 

  15. Liu Y, Zhang D et al (2016) Global u-stability criteria for quaternion-valued neural networks with unbounded time-varying delays. Inf Sci 360:273–288

    Article  Google Scholar 

  16. Liu Y, Zhang D, Lu J (2017) Global exponential stability for quaternion-valued recurrent neural networks with time-varying delays. Nonlinear Dyn 1(87):553–565

    Article  Google Scholar 

  17. Liu Y, Xu P, Lu J, Liang J (2016) Global stability of Clifford-valued recurrent neural networks with time delays. Nonlinear Dyn 2(84):767–777

    Article  MathSciNet  Google Scholar 

  18. Shen H, Zhu Y, Zhang L, Park JH (2016) Extended dissipative state estimation for Markov jump neural networks with unreliable links. IEEE Trans Neural Netw Learn Syst 28:1–13

    MathSciNet  Google Scholar 

  19. Xiao M, Zheng W et al (2016) Stability and bifurcation analysis of arbitrarily high-dimensional genetic regulatory networks with hub structure and bidirectional coupling. IEEE Trans Circuits Syst I Regul Pap 63(8):1243–1254

    Article  MathSciNet  Google Scholar 

  20. Xiao M, Zheng W et al (2013) Hopf bifurcation of an (n+1)-neuron bidirectional associative memory neural network model with delays. IEEE Trans Neural Netw Learn Syst 24(1):118–132

    Article  Google Scholar 

  21. Zhu Q, Rakkiyappan R, Chandrasekar A (2014) Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control. Neurocomputing 136:136–151

    Article  Google Scholar 

  22. Huang X, Fan YJ, Jia J, Wang Z, Li YX (2017) Quasi-synchronization of fractional-order memristor-based neural networks with parameter mismatches. IET Control Theory Appl 14:2317–2327

    Article  Google Scholar 

  23. Cheng Z, Li D, Cao J (2016) Stability and Hopf bifurcation of a three-layer neural network model with delays. Neurocomputing 175:355–370

    Article  Google Scholar 

  24. Gan QT, Liang YH (2012) Synchronization of non-identical unknown chaotic delayed neural networks based on adaptive sliding mode control. Neural Process Lett 35:245–255

    Article  Google Scholar 

  25. Huang X, Zhao Z, Wang Z, Li YX (2012) Chaos and hyperchaos in fractional-order cellular neural networks. Neurocomputing 94:13–21

    Article  Google Scholar 

  26. Du Y, Xu R, Liu Q (2013) Stability and bifurcation analysis for a discrete time bidirectional ring neural network model with delay. Electron J Differ Equ 2013:1–12

    Article  MathSciNet  Google Scholar 

  27. Yu WW, Cao JD, Chen GR (2008) Stability and Hopf bifurcation of a general delayed recurrent neural network. IEEE Trans Neural Netw 19(5):845–854

    Article  Google Scholar 

  28. Karaoglu E, Yilmaz E, Museyin H (2016) Stability and bifurcation analysis of two-neuron network with discrete and distributed delays. Neurocomputing 182:102–110

    Article  Google Scholar 

  29. Huang CX, Huang LH, Feng JF, Nai MY, He YG (2007) Hopf bifurcation analysis for a two-neuron network with four delays. Chaos Solitons Fractals 34:795–812

    Article  MathSciNet  Google Scholar 

  30. Shayer LP, Campbell SA (2000) Stability, bifurcation and multistability in a system of two coupled neurons with multiple time delays. SIAM J Appl Math 61:673–700

    Article  MathSciNet  Google Scholar 

  31. Hassard B, Kazarinoff N, Wan Y (1981) Theory and applications of Hopf bifurcation. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  32. Kundu A, Das P, Roy AB (2016) Stability, bifurcation and synchronization in a delayed neural network model of n-identical neurons. Math Comput Simul 121:12–33

    Article  MathSciNet  Google Scholar 

  33. Tank DW, Hopfield JJ (1987) Neural computation by concentrating information in time. Proc Natl Acad Sci USA 84:1986–1991

    Article  MathSciNet  Google Scholar 

  34. De Vries B, Principle JC (1992) The gamma model—a new neural model for temporal processing. Neural Netw 5:565–576

    Article  Google Scholar 

  35. Principle JC, Kuo JM, Celebi S (1994) An analysis of the gamma memory in dynamic neural networks. IEEE Trans Neural Netw 5:337–361

    Google Scholar 

  36. Ruan S, Filfil RS (2004) Dynamics of a two-neuron system with discrete and distributed delays. Physica D 191:323–342

    Article  MathSciNet  Google Scholar 

  37. Zhou XB, Wu Y, Li Y, Yao X (2009) Stability and bifurcation analysis on a two-neuron network with discrete and distributed delays. Chaos Solitons Fractals 40:1493–1505

    Article  MathSciNet  Google Scholar 

  38. Olien L, Belair J (1997) Bifurcation, stability and monotonicity properties of a delayed neural network model. Physica D 102:349–363

    Article  MathSciNet  Google Scholar 

  39. Atay FM (2010) Complex time-delay systems: theory and applications. Springer, Berlin

    Book  Google Scholar 

  40. Syed Ali M (2014) Stability analysis of Markovian jumping stochastic Cohen Grossberg neural networks with discrete and distributed time varying delays. Chin Phys B 23(6):60702–60708

    Article  Google Scholar 

  41. Ncube I (2013) Stability switching and Hopf bifurcation in a multiple-delayed neural network with distributed delay. J Math Anal Appl 407:141–146

    Article  MathSciNet  Google Scholar 

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Correspondence to Zhen Wang.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 61573008, 61473178, 61473177, 61503002), Post-Doctoral Applied Research Projects of Qingdao (No. 2016115) and SDUST Research Fund (No. 2014TDJH102).

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Wang, Z., Li, L., Li, Y. et al. Stability and Hopf Bifurcation of a Three-Neuron Network with Multiple Discrete and Distributed Delays. Neural Process Lett 48, 1481–1502 (2018). https://doi.org/10.1007/s11063-017-9754-8

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