Log in

Bifurcation analysis of a tri-neuron neural network model in the frequency domain

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, a class of neural network models with three neurons is considered. By applying the frequency domain approach and analyzing the associated characteristic equation, the existence of the bifurcation parameter point is determined. If the coefficient μ is chosen as a bifurcation parameter, it is found that Hopf bifurcation occurs when the parameter μ passes through a critical value. The direction and the stability of Hopf bifurcation periodic solutions are determined by the Nyquist criterion and the graphical Hopf bifurcation theorem. Some numerical simulations for justifying the theoretical analysis are also provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Germany)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Allwright, D.J.: Harmonic balance and the Hopf bifurcation theorem. Math. Proc. Camb. Philos. Soc. 82, 453–467 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  2. An der Heiden, U.: Delays in physiological systems. J. Math. Biol. 8, 345–364 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  3. Babcock, K.L., Westervelt, R.M.: Dynamics of simple electronic neural networks with added intertia. Physica D 23, 464–469 (1986)

    Article  Google Scholar 

  4. Babcock, K.L., Westervelt, R.M.: Dynamics of simple electronic neural networks. Physica D 28, 305–316 (1987)

    Article  MathSciNet  Google Scholar 

  5. Baldi, P., Atiya, A.: How delays affect neural dynamics and learning. IEEE Trans. Neural Netw. 5, 610–621 (1994)

    Article  Google Scholar 

  6. Cao, J.: On stability analysis in delayed celler neural networks. Phys. Rev. E 59, 5940–5944 (1999)

    Article  MathSciNet  Google Scholar 

  7. Cao, J., Wang, J.: Absolute exponential stability of recurrent neural networks with Lipschitz-continuous activation and time delays. Neural Netw. 17, 379–390 (2004)

    Article  MATH  Google Scholar 

  8. Compbell, S.A., Ruan, S., Wei, J.: Qualitative analysis of a neural network model with multiple time delays. Int. J. Bifurc. Chaos 9, 1585–1595 (1999)

    Article  Google Scholar 

  9. Fan, D., Wei, J.: Hopf bifurcation analysis in a tri-neuron network with time delay. Nonlinear Anal., Real World Appl. 9, 9–25 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  10. Gopalsamy, K., He, X.: Delay-independent stability in bi-directional associative memory networks. IEEE Trans. Neural Netw. 5, 998–1002 (1994)

    Article  Google Scholar 

  11. Gopalsamy, K., Leung, I.: Delay induced periodicity in a neural netlet of excitation and inhibition. Physica D 89, 395–426 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  12. Guckenheimer, J., Holmes, P.: Nonlinear Oscillations, Dynamics Systems and Bifurcations of Vector Fields. Applied Mathematical Sciences, Springer, New York (1997)

    Google Scholar 

  13. Guo, S., Huang, L.: Hopf bifurcating periodic orbits in a ring of neurons with delays. Physica D 182, 19–44 (2003)

    Article  MathSciNet  Google Scholar 

  14. Hassard, B., Kazarinoff, D., Wan, Y.: Theory and Applications of Hopf Bifurcation. Cambridge University Press, Cambridge (1981)

    MATH  Google Scholar 

  15. Hopfield, J.J.: Neural network and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  16. Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA 81, 3088–3092 (1984)

    Article  Google Scholar 

  17. Li, X., Wei, J.: On the zeros of a fourth degree exponential polynomial with applications to a neural network model with delays. Chaos Solitons Fractals 26, 519–526 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  18. Li, S., Liao, X., Li, C.: Hopf bifurcation of two-neuron network with differential discrete time delays. Int. J. Bifurc. Chaos 5, 1589–1600 (2005)

    Article  MathSciNet  Google Scholar 

  19. Liao, X., Li, S.: Hopf bifurcation on a two-neuron system with distributed delays: a frequency domain approach. Nonlinear Dyn. 31, 299–326 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  20. Liao, X., Li, S., Chen, G.: Bifurcation analysis on a two-neuron system with distributed delays in the frequency domain. Neural Netw. 17, 545–561 (2004)

    Article  MATH  Google Scholar 

  21. Marcus, C.M., Westervelt, R.M.: Stability of an along neural network with delay. Phys. Rev. A 39, 347–359 (1989)

    Article  MathSciNet  Google Scholar 

  22. Mees, A.I., Chua, L.O.: The Hopf bifurcation theorem and its applications to nonlinear oscillations in circuits and systems. IEEE Trans. Circuits Syst. 26, 235–254 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  23. Mohamad, S., Gopalsamy, K.: Exponential stability of continuous-time and discrete-time cellular networks with delays. Appl. Math. Comput. 135, 17–38 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  24. Moiola, J.L., Chen, G.: Frequency domain approach to computational analysis of bifurcations and limit cycles: a tutorial. Int. J. Bifurc. Chaos 3, 843–867 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  25. Moiola, J.L., Chen, G.: Hopf Bifurcation Analysis: A Frequency Domain Approach. World Scientific, Singapore (1996)

    Google Scholar 

  26. Orosz, G., Stépán, G.: Hopf bifurcation calculations in delayed systems with translational symmetry. J. Nonlinear Sci. 14, 505–528 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  27. Orosz, G., Moehlis, J., Murray, R.M.: Controlling biological networks by time-delayed signals. Philos. Trans. R. Soc. Lond. Ser. A, Math. Phys. Sci. 368, 439–454 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  28. Park, J.H.: On global stability criterion for neural networks with discrete and distributed delays. Chaos Solitons Fractals 30, 897–902 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  29. Song, Y., Han, M., Wei, J.: Stability and Hopf bifurcation on a simplified BAM network model with delays. Physica D 200, 185–204 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  30. Stepan, G.: Introduction to delay effects in brain dynamics. Philos. Trans. R. Soc., Math. Phys. Eng. Sci. 367, 1059–1062 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  31. Wang, L., Zou, X.: Hopf bifurcation in bidirectional associative memory neural networks with delays: analysis and computation. J. Comput. Appl. Math. 167, 73–90 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  32. Wei, J., Li, M.Y.: Global existence of periodic solutions in a tri-neuron network model with delays. Physica D 198, 109–119 (2004)

    Article  MathSciNet  Google Scholar 

  33. Wei, J., Ruan, S.: Stability and bifurcation in a neural network model with two delays. Physica D 130, 255–272 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  34. Wei, J., Velarde, M.G.: Bifurcation analysis and existence of periodic solutions in a simple neural network with delays. Chaos 143, 940–953 (2004)

    Article  MathSciNet  Google Scholar 

  35. Yan, X.: Hopf bifurcation and stability for a delayed tri-neuron network model. J. Comput. Appl. Math. 196, 579–595 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  36. Yan, X.: Bifurcation analysis in a simplified tri-neuron BAM networks model with multiple delays. Nonlinear Anal., Real World Appl. 9, 963–976 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  37. Ye, H., Michel, A., Wang, K.: Qualitative analysis of Cohen–Grossberg neural networks with multiple delays. Phys. Rev. E 51, 2611–2618 (1995)

    Article  MathSciNet  Google Scholar 

  38. Zhang, Q., Wei, X., Xu, J.: Stability of delayed cellular neural networks. Chaos Solitons Fractals 31, 514–520 (2007)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 11261010 and No. 11201138), Soft Science and Technology Program of Guizhou Province (No. 2011LKC2030), Scientific Research Fund of Hunan Provincial Education Department (No. 12B034), Natural Science and Technology Foundation of Guizhou Province (J[2012]2100), Governor Foundation of Guizhou Province ([2012]53), and Doctoral Foundation of Guizhou University of Finance and Economics (2010).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chang** Xu.

Appendices

Appendix A

(I): Calculating the eigenvalue \(\tilde{\lambda}\).

Since \(\tilde{\lambda}\) is the eigenvalue of \([G(\mathrm{i}\tilde{\omega};\tilde{\mu})J(\mu)]\), one has

$$\begin{aligned} \tilde{\lambda}^3+\frac{a}{\mathrm{i}\tilde{\omega}+\tilde{\mu }}\tilde{\lambda}^2+ \frac{b}{(\mathrm{i}\tilde{\omega}+\tilde {\mu})^2} \tilde{\lambda}+\frac{c}{(\mathrm{i}\tilde{\omega}+\tilde{\mu })^3}=0. \end{aligned}$$
(A.1)

Noticing that \(\tilde{\lambda}=(\hat{\lambda}\mathrm{i}\tilde{\omega};\tilde {\mu})\) is a real number and separating Eq. (A.1) into real and imaginary parts, we have

$$\begin{aligned} &\tilde{\lambda}\bigl(\tilde{\mu}^3-3\tilde{\omega}\tilde{\mu } \bigr)+a\tilde{\lambda}^2\bigl(\tilde{\mu}^2 -\tilde{ \omega}^2\bigr)+b\tilde{\lambda}\tilde{\mu}+c=0, \\ &\tilde{\lambda}\bigl(3\tilde{\mu}^2\tilde{\omega}-\tilde{\omega }^3\bigr)+2a\tilde{\lambda}^2(\tilde{\mu}\tilde{\omega} +b\tilde{\lambda}\tilde{\mu})=0. \end{aligned}$$

Then we have

$$\begin{aligned} &8\tilde{\lambda}\tilde{\mu}^3+8\tilde{\lambda}^2\tilde{ \mu }^2+\bigl(2\tilde{\lambda}b +2a^2\tilde{ \lambda}^3\bigr)\tilde{\mu}+ab\tilde{\lambda}^2-c=0, \end{aligned}$$
(A.2)
$$\begin{aligned} &\tilde{\omega}^2=3\tilde{\mu}^2+2a\tilde{\mu}+b. \end{aligned}$$
(A.3)

By (A.3), we can compute the eigenvalue \(\tilde{\lambda}\).

(II): Calculating the eigenvalue \((\frac{d\tilde{\lambda}}{d\mu} ) |_{\mu=\mu _{0}}\).

By using μ instead of \(\tilde{\mu}\) in Eq. (A.2) and by taking the derivative with respect to μ on both sides of Eq. (A.2), and letting \(\mu=\mu_{0}, \tilde{\lambda}=-1\), we obtain

$$\begin{aligned} \biggl(\frac{d\tilde{\lambda}}{d\mu} \biggr) \bigg|_{\mu=\mu_0} = \frac{12\mu_0^2-8\mu_0+a^2+b}{8\mu_0^3-11a\mu_0^2+2b\mu_0+6a^2\mu _0-2ab}. \end{aligned}$$
(A.4)

(III): Calculating the real and imaginary parts of \((\frac{d\tilde{\lambda}}{d\omega} ) |_{\omega =\omega_{0}}\).

Consider Eq. (A.1)

$$\lambda^3+\frac{a}{\mathrm{i}\tilde{\omega}+\tilde{\mu}}\lambda ^2+ \frac{b}{(\mathrm{i}\tilde{\omega}+\tilde{\mu})^2} \lambda+\frac{c}{(\mathrm{i}\tilde{\omega}+\tilde{\mu})^3}=0. $$

Fixing μ at \(\tilde{\mu}\) and taking the derivative with respect to ω on both sides of Eq. (A.1), we get

$$\begin{aligned} \biggl(\frac{d\tilde{\lambda}}{d\omega} \biggr) \bigg|_{\omega =\tilde{\omega}}= \frac{D_{11}(\tilde{\lambda},\tilde{\mu},\tilde{\omega })+iD_{12}(\tilde{\lambda},\tilde{\mu},\tilde{\omega})}{ D_{21}(\tilde{\lambda},\tilde{\mu},\tilde{\omega })+iD_{22}(\tilde{\lambda},\tilde{\mu},\tilde{\omega})}, \end{aligned}$$
(A.5)

where

$$\begin{aligned} D_{11}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) =&-2a\tilde { \omega}\tilde{\mu}\tilde{\lambda}^2 \bigl(\tilde{\mu}^2- \tilde{\omega}^2\bigr) +2b\tilde{\omega}\bigl(\tilde{\mu }^2-\tilde{\omega}^2\bigr)-4b\tilde{\omega} \tilde{ \lambda}\bigl(\tilde{\mu}^2-\tilde{\omega}^2 \bigr)^3 +6c\tilde{\omega}\bigl(\tilde{\mu}^2-\tilde{ \omega}^2\bigr)^3\\ &{}-6c\tilde {\omega}\bigl(\tilde{ \mu}^2-\tilde{\omega}^2\bigr)^2 \bigl(\tilde{ \mu}^2-3\tilde{\omega}^2\bigr), \\ D_{12}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) =& \bigl[a\bigl( \tilde{\mu}^2-\tilde{\omega}^2\bigr)+4a\tilde{\mu}\tilde{ \omega }^2\bigr]\bigl(\tilde{\mu}^2-\tilde{ \omega}^2\bigr)^4 \tilde{\lambda}^2 +3c\bigl( \tilde{\mu}^2-\tilde{\omega}^2\bigr)^4 +\bigl[2b\tilde{\mu}\bigl(\tilde{\mu}^2-\tilde{ \omega}^2\bigr)^2\\ &{}+8b\tilde {\mu}\tilde{ \omega}^2 \bigl(\tilde{\mu}^2-\tilde{\omega}^2 \bigr)\bigr]\bigl(\tilde{\mu}^2-\tilde{\omega }^2\bigr) \tilde{\lambda}, \\ D_{21}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) =&\bigl(\tilde{\mu }^2-\tilde{\omega}^2\bigr)^4 \bigl[3 \lambda^2\bigl(\tilde{\mu}^2-\tilde{\omega}^2 \bigr) +2\lambda{a}\tilde{\mu}\bigl(\tilde{\mu}^2-\tilde{ \omega}^2\bigr)+b\bigl(\tilde {\mu}^2-\tilde{ \omega}^2\bigr)\bigr], \\ D_{22}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) =&\bigl(\tilde{\mu }^2-\tilde{\omega}^2\bigr)^4 \bigl[2\tilde{ \lambda} {a}\tilde{\omega}\bigl(\tilde{\mu}^2-\tilde{\omega }^2\bigr)+2b\tilde{\mu}\tilde{\omega}\bigr]. \end{aligned}$$

Then we obtain

$$\begin{aligned} &\operatorname{Re} \biggl(\frac{d\tilde{\lambda}}{d\omega} \biggr) \bigg|_{\omega=\tilde{\omega}}= \frac{D_{11}(\tilde{\lambda},\tilde{\mu},\tilde{\omega })D_{21}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) +D_{12}(\tilde{\lambda},\tilde{\mu},\tilde{\omega})D_{22}(\tilde {\lambda},\tilde{\mu},\tilde{\omega})}{ D_{21}^2(\tilde{\lambda},\tilde{\mu},\tilde{\omega })-D_{22}^2(\tilde{\lambda},\tilde{\mu},\tilde{\omega})}, \end{aligned}$$
(A.6)
$$\begin{aligned} &\operatorname{Im} \biggl(\frac{d\tilde{\lambda}}{d\omega} \biggr) \bigg|_{\omega=\tilde{\omega}}= \frac{D_{12}(\tilde{\lambda},\tilde{\mu},\tilde{\omega })D_{21}(\tilde{\lambda},\tilde{\mu},\tilde{\omega}) -D_{11}(\tilde{\lambda},\tilde{\mu},\tilde{\omega})D_{22}(\tilde {\lambda},\tilde{\mu},\tilde{\omega})}{ D_{22}^2(\tilde{\lambda},\tilde{\mu},\tilde{\omega })-D_{22}^2(\tilde{\lambda},\tilde{\mu},\tilde{\omega})}. \end{aligned}$$
(A.7)

Using μ 0 instead of \(\tilde{\mu}\), ω 0 instead of \(\tilde{\omega}\), and −1 instead of \(\tilde{\lambda}\), we have

$$\begin{aligned} &\operatorname{Re} \biggl(\frac{d\tilde{\lambda}}{d\omega} \biggr) \bigg|_{\omega=\tilde{\omega}}= \frac{D_{11}(-1,\mu_0,\omega_0)D_{21}(-1,\mu_0,\omega_0) +D_{12}(-1,\mu_0,\omega_0)D_{22}(-1,\mu_0,\omega_0)}{ D_{21}^2(-1,\mu_0,\omega_0)-D_{22}^2(-1,\mu_0,\omega_0)}, \end{aligned}$$
(A.8)
$$\begin{aligned} &\operatorname{Im} \biggl(\frac{d\tilde{\lambda}}{d\omega} \biggr) \bigg|_{\omega=\tilde{\omega}}= \frac{D_{12}(-1,\mu_0,\omega_0)D_{21}(-1,\mu_0,\omega_0) -D_{11}(-1,\mu_0,\omega_0)D_{22}(-1,\mu_0,\omega_0)}{ D_{22}^2(-1,\mu_0,\omega_0)-D_{22}^2(-1,\mu_0,\omega_0)}, \end{aligned}$$
(A.9)

where

$$\begin{aligned} D_{11}(-1,\mu_0,\omega_0) =&-2a \omega_0\mu_0\bigl(\mu_0^2- \omega _0^2\bigr) +2b\omega_0\bigl( \mu_0^2-\omega_0^2\bigr) +4b \omega_0\bigl(\mu_0^2-\omega_0^2 \bigr)^3 +6c\omega_0\bigl(\mu_0^2- \omega_0^2\bigr)^3\\ &{}-6c\omega_0 \bigl(\mu_0^2-\omega _0^2 \bigr)^2\bigl(\mu_0^2-3\omega_0^2 \bigr), \\ D_{12}(-1,\mu_0,\omega_0) =& \bigl[a\bigl( \mu_0^2-\omega_0^2\bigr)+4a \mu_0\omega_0^2\bigr]\bigl( \mu_0^2-\omega _0^2 \bigr)^4+3c\bigl(\mu_0^2- \omega_0^2\bigr)^4 -\bigl[2b\mu_0\bigl(\mu_0^2- \omega_0^2\bigr)^2\\ &{}+8b\mu_0 \omega_0^2\bigl(\mu _0^2- \omega_0^2\bigr)\bigr]\bigl(\mu_0^2- \omega_0^2\bigr), \\ D_{21}(-1,\mu_0,\omega_0) =&\bigl( \mu_0^2-\omega_0^2 \bigr)^4\bigl[3\bigl(\mu _0^2- \omega_0^2\bigr)-2{a}\mu_0\bigl( \mu_0^2-\omega_0^2\bigr)+b\bigl( \mu_0^2-\omega _0^2\bigr)\bigr], \\ D_{22}(-1,\mu_0,\omega_0) =&\bigl( \mu_0^2-\omega_0^2 \bigr)^4\bigl[-2{a}\omega _0\bigl(\mu_0^2- \omega_0^2\bigr)+2b\mu_0 \omega_0\bigr]. \end{aligned}$$

Appendix B

$$\begin{aligned} k_{11} =&\frac{1}{1+a_{11}} \biggl\{ 1-\frac {a_{21}a_{13}}{a_{13}a_{21}-a_{23}(1+a_{11})}- \frac {a_{21}}{1+a_{11}}y_1 \\ &{}-\frac {a_{31}[(1+a_{11})(1+a_{12})-a_{12}a_{21}]-a_{21}[a_{12}a_{31}-a_{32}(1+a_{11})]}{ (1+a_{11})[(1+a_{11})(1+a_{12})-a_{12}a_{21}]}x_1y_1 \biggr\} , \\ k_{12} =&\frac{1}{1+a_{11}} \biggl[\frac {a_{13}(1+a_{11})}{a_{13}a_{21}-a_{23}(1+a_{11})}+ \frac {a_{12}a_{31}-a_{32}(1+a_{11})}{(1+a_{11})(1+a_{12})-a_{12}a_{21}}x_1y_1 \biggr], \\ k_{13} =&\frac{1}{1+a_{11}}x_1y_1, \\ k_{21} =&-\frac{1+a_{11}}{(1+a_{11})(1+a_{12})-a_{12}a_{21}} \biggl\{ \frac{a_{21}}{1+a_{11}} \\ &{}+\frac {a_{31}[(1+a_{11})(1+a_{12})-a_{12}a_{21}]-a_{21}[a_{12}a_{31}-a_{32}(1+a_{11})]}{ (1+a_{11})[(1+a_{11})(1+a_{12})-a_{12}a_{21}]}x_1 \biggr\} , \\ k_{22} =&\frac{1+a_{11}}{(1+a_{11})(1+a_{12})-a_{12}a_{21}}\frac {a_{12}a_{31}-a_{32}(1+a_{11})}{ (1+a_{11})(1+a_{12})-a_{12}a_{21}}x_1, \\ k_{23} =&\frac{1+a_{11}}{(1+a_{11})(1+a_{12})-a_{12}a_{21}}x_1, \\ k_{31} =&-\frac{1}{z_1} \biggl\{ \frac {a_{31}[(1+a_{11})(1+a_{12})-a_{12}a_{21}]-a_{21}[a_{12}a_{31}-a_{32}(1+a_{11})]}{ (1+a_{11})[(1+a_{11})(1+a_{12})-a_{12}a_{21}]}x_1 \biggr\} , \\ k_{32} =&\frac{1}{z_1}\frac{a_{12}a_{31}-a_{32}(1+a_{11})}{ (1+a_{11})(1+a_{12})-a_{12}a_{21}}, \\ k_{33} =&\frac{1}{z_1}, \\ x_1 =&-\frac {[(1+a_{11})(1+a_{12})-a_{12}a_{21}][a_{13}a_{21}-a_{23}(1+a_{11})]}{m_1+n_1}, \\ m_1 =&\bigl[(1+a_{11}) (1+a_{33})-a_{13}a_{31} \bigr] \bigl[(1+a_{11}) (1+a_{12})-a_{12}a_{21} \bigr], \\ n_1 =&\bigl[a_{23}(1+a_{11})-a_{13}a_{21} \bigr] \bigl[a_{12}a_{31}-a_{32}(1+a_{11}) \bigr], \\ y_1 =&-\frac{\{ a_{12}[a_{13}a_{21}-a_{23}(1+a_{11})]+a_{33}[(1+a_{11})(1+a_{12})-a_{12}a_{21}]\} (1+a_{11})}{ [a_{13}a_{21}-a_{23}(1+a_{11})][(1+a_{11})(1+a_{12})-a_{12}a_{21}]}, \\ z_1 =&\frac{m_1+n_1}{(1+a_{11})[(1+a_{11})(1+a_{12})-a_{12}a_{21}]}, \end{aligned}$$

where

$$\begin{aligned} &a_{11}=-\frac{f_{11}'(0)}{\mu}, \quad a_{12}=-\frac{f_{12}'(0)}{\mu },\quad a_{13}=- \frac{f_{13}'(0)}{\mu}, \\ & a_{21}=-\frac{f_{21}'(0)}{\mu},\quad a_{22}=-\frac{f_{22}'(0)}{\mu },\quad a_{23}=- \frac{f_{23}'(0)}{\mu}, \\ &a_{31}=-\frac{f_{31}'(0)}{\mu},\quad a_{32}=-\frac{f_{32}'(0)}{\mu },\quad a_{33}=- \frac{f_{33}'(0)}{\mu}. \end{aligned}$$

Appendix C

$$\begin{aligned} l_{11} =&\frac{1}{1+d_{11}} \biggl\{ 1-\frac {d_{21}d_{13}}{d_{13}d_{21}-d_{23}(1+d_{11})}- \frac {d_{21}}{1+d_{11}}y_2 \\ &{}-\frac {d_{31}[(1+d_{11})(1+d_{12})-d_{12}d_{21}]-d_{21}[d_{12}d_{31}-d_{32}(1+d_{11})]}{ (1+d_{11})[(1+d_{11})(1+d_{12})-d_{12}d_{21}]}x_2y_2 \biggr\} , \\ l_{12} =&\frac{1}{1+d_{11}} \biggl[\frac {d_{13}(1+d_{11})}{d_{13}d_{21}-d_{23}(1+d_{11})}+ \frac {d_{12}d_{31}-d_{32}(1+d_{11})}{(1+d_{11})(1+d_{12})-d_{12}d_{21}}x_2y_2 \biggr], \\ l_{13} =&\frac{1}{1+d_{11}}x_2y_2, \\ l_{21} =&-\frac{1+d_{11}}{(1+d_{11})(1+d_{12})-d_{12}d_{21}} \biggl\{ \frac{d_{21}}{1+d_{11}} \\ &{}+\frac {d_{31}[(1+d_{11})(1+d_{12})-d_{12}d_{21}]-d_{21}[d_{12}d_{31}-d_{32}(1+d_{11})]}{ (1+d_{11})[(1+d_{11})(1+d_{12})-d_{12}d_{21}]}x_2 \biggr\} , \\ l_{22} =&\frac{1+d_{11}}{(1+d_{11})(1+d_{12})-d_{12}d_{21}}\frac {d_{12}d_{31}-d_{32}(1+d_{11})}{ (1+d_{11})(1+d_{12})-d_{12}d_{21}}x_2, \\ l_{23} =&\frac{1+d_{11}}{(1+d_{11})(1+d_{12})-d_{12}d_{21}}x_2, \\ l_{31} =&-\frac{1}{z_2} \biggl\{ \frac {d_{31}[(1+d_{11})(1+d_{12})-d_{12}d_{21}]-d_{21}[d_{12}d_{31}-d_{32}(1+d_{11})]}{ (1+d_{11})[(1+d_{11})(1+d_{12})-d_{12}d_{21}]}x_2 \biggr\} , \\ l_{32} =&\frac{1}{z_2}\frac{d_{12}d_{31}-d_{32}(1+d_{11})}{ (1+d_{11})(1+d_{12})-d_{12}d_{21}}, \\ l_{33} =&\frac{1}{z_2}, \\ x_2 =&-\frac {[(1+d_{11})(1+d_{12})-d_{12}d_{21}][d_{13}d_{21}-d_{23}(1+d_{11})]}{m_2+n_2}, \\ m_2 =&\bigl[(1+d_{11}) (1+d_{33})-d_{13}d_{31} \bigr] \bigl[(1+d_{11}) (1+d_{12})-d_{12}d_{21} \bigr], \\ n_2 =&\bigl[d_{23}(1+d_{11})-d_{13}d_{21} \bigr] \bigl[d_{12}d_{31}-d_{32}(1+d_{11}) \bigr], \\ y_2 =&-\frac{\{ d_{12}[d_{13}d_{21}-d_{23}(1+d_{11})]+d_{33}[(1+d_{11})(1+d_{12})-d_{12}d_{21}]\} (1+d_{11})}{ [d_{13}d_{21}-d_{23}(1+d_{11})][(1+d_{11})(1+d_{12})-d_{12}d_{21}]}, \\ z_2 =&\frac{m_2+n_2}{(1+d_{11})[(1+d_{11})(1+d_{12})-d_{12}d_{21}]}, \end{aligned}$$

where

$$\begin{aligned} &d_{11}=-\frac{f_{11}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}, \quad d_{12}=-\frac{f_{12}'(0)}{2\mathrm{i}\tilde{\omega}+\mu },\quad d_{13}=- \frac{f_{13}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}, \\ & d_{21}=-\frac{f_{21}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}, \quad d_{22}=-\frac{f_{22}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}, \quad d_{23}=-\frac{f_{23}'(0)}{\mu}, \\ &d_{31}=-\frac{f_{31}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}, \quad d_{32}=-\frac{f_{32}'(0)}{2\mathrm{i}\tilde{\omega}+\mu },\qquad d_{33}=- \frac{f_{33}'(0)}{2\mathrm{i}\tilde{\omega}+\mu}. \end{aligned}$$

Appendix D

$$\begin{aligned} &t_1=\frac{k_{11}f_{11}^{\prime\prime}(0)+k_{12}f_{21}^{\prime \prime}(0)+k_{13}f_{31}^{\prime\prime}(0)}{\mu},\qquad t_2=\frac{k_{11}f_{12}^{\prime\prime}(0)+k_{12}f_{22}^{\prime \prime}(0)+k_{13}f_{32}^{\prime\prime}(0)}{\mu}, \\ &t_3=\frac{k_{11}f_{13}^{\prime\prime}(0)+k_{12}f_{23}^{\prime \prime}(0)+k_{13}f_{33}^{\prime\prime}(0)}{\mu}, \qquad t_4=\frac{k_{21}f_{11}^{\prime\prime}(0)+k_{22}f_{21}^{\prime \prime}(0)+k_{23}f_{31}^{\prime\prime}(0)}{\mu}, \\ &t_5=\frac{k_{21}f_{12}^{\prime\prime}(0)+k_{22}f_{22}^{\prime \prime}(0)+k_{23}f_{32}^{\prime\prime}(0)}{\mu},\qquad t_6=\frac{k_{21}f_{13}^{\prime\prime}(0)+k_{22}f_{23}^{\prime \prime}(0)+k_{23}f_{33}^{\prime\prime}(0)}{\mu}, \\ &t_7=\frac{k_{31}f_{11}^{\prime\prime}(0)+k_{32}f_{21}^{\prime \prime}(0)+k_{33}f_{31}^{\prime\prime}(0)}{\mu}, \qquad t_8=\frac{k_{31}f_{12}^{\prime\prime}(0)+k_{32}f_{22}^{\prime \prime}(0)+k_{33}f_{32}^{\prime\prime}(0)}{\mu}, \\ &t_9=\frac{k_{31}f_{13}^{\prime\prime}(0)+k_{32}f_{23}^{\prime \prime}(0)+k_{33}f_{33}^{\prime\prime}(0)}{\mu},\\ &r_1=\frac{[f_{13}'(0)(f_{22}'(0)+\tilde{\lambda}\tilde{\mu })-f_{12}'(0)f_{23}'(0)]^2 +(\tilde{\lambda}\tilde{\mu})^2}{[(f_{11}'(0)+\tilde{\lambda }\tilde{\mu}) (f_{22}'(0)+\tilde{\lambda}\tilde{\mu})-f_{12}'(0)f_{21}'(0)]^2+ [\tilde{\lambda}\tilde{\mu}(f_{11}'(0)+2\tilde{\lambda}\tilde {\mu}+f_{22}'(0))]^2}, \\ &r_2=\frac{(f_{11}'(0)f_{23}'(0)+\tilde{\lambda}\tilde{\mu }f_{23}'(0)-f_{13}'(0)f_{21}'(0))^2 +(\tilde{\lambda}\tilde{\mu}f_{23}'(0))^2}{ [(f_{11}'(0)+\tilde{\lambda}\tilde{\mu}) (f_{22}'(0)+\tilde{\lambda}\tilde{\mu})-f_{12}'(0)f_{21}'(0)]^2+ [\tilde{\lambda}\tilde{\mu}(f_{11}'(0)+2\tilde{\lambda}\tilde {\mu}+f_{22}'(0))]^2}. \end{aligned}$$

Appendix E

$$\begin{aligned} &n_1=\frac{l_{11}f_{11}^{\prime\prime}(0)+l_{12}f_{21}^{\prime \prime}(0)+l_{13}f_{31}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}},\qquad n_2=\frac{l_{11}f_{12}^{\prime\prime}(0)+l_{12}f_{22}^{\prime \prime}(0)+l_{13}f_{32}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \\ &n_3=\frac{l_{11}f_{13}^{\prime\prime}(0)+l_{12}f_{23}^{\prime \prime}(0)+l_{13}f_{33}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \qquad n_4=\frac{l_{21}f_{11}^{\prime\prime}(0)+l_{22}f_{21}^{\prime \prime}(0)+l_{23}f_{31}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \\ &n_5=\frac{l_{21}f_{12}^{\prime\prime}(0)+l_{22}f_{22}^{\prime \prime}(0)+l_{23}f_{32}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \qquad n_6=\frac{l_{21}f_{13}^{\prime\prime}(0)+l_{22}f_{23}^{\prime \prime}(0)+l_{23}f_{33}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \\ &n_7=\frac{l_{31}f_{11}^{\prime\prime}(0)+l_{32}f_{21}^{\prime \prime}(0)+l_{33}f_{31}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \qquad n_8=\frac{l_{31}f_{12}^{\prime\prime}(0)+l_{32}f_{22}^{\prime \prime}(0)+l_{33}f_{32}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}, \\ &n_9=\frac{l_{31}f_{13}^{\prime\prime}(0)+l_{32}f_{23}^{\prime \prime}(0)+l_{33}f_{33}^{\prime\prime}(0)}{2\mathrm{i}\tilde {\omega}+\tilde{\mu}}. \end{aligned}$$

Appendix F

$$\begin{aligned} p_1^{(1)} =&-\frac{1}{4}f_{11}^{\prime\prime }(0) \biggl[(t_1r_1+t_2r_2+t_3)v_1 + \frac {1}{2}\bigl(n_1v_1^2+n_2v_2^2+n_3 \bigr)\overline{v_1}\biggr] \\ &{}-\frac{1}{4}f_{12}^{\prime\prime }(0)\biggl[(t_4r_1+t_5r_2+t_6)v_2 + \frac {1}{2}\bigl(n_4v_1^2+n_5v_2^2+n_6 \bigr)\overline{v_2}\biggr] \\ &{}-\frac{1}{4}f_{13}^{\prime\prime }(0)\biggl[(t_7r_1+t_8r_2+t_9)v_2 + \frac{1}{2}\bigl(n_7v_1^2+n_8v_2^2+n_9 \bigr)\biggr] \\ &{}-\frac{1}{8}\bigl[f_{111}^{\prime\prime\prime}(0)v_1^2 \overline {v_1}+f_{122}^{\prime\prime\prime}(0)v_2^2 \overline {v_2}-f_{333}^{\prime\prime\prime}(0)\bigr], \\ p_1^{(2)} =&-\frac{1}{4}f_{21}^{\prime\prime }(0) \biggl[(t_1r_1+t_2r_2+t_3)v_1 + \frac {1}{2}\bigl(n_1v_1^2+n_2v_2^2+n_3 \bigr)\overline{v_1}\biggr] \\ &{}-\frac{1}{4}f_{22}^{\prime\prime }(0)\biggl[(t_4r_1+t_5r_2+t_6)v_2 + \frac {1}{2}\bigl(n_4v_1^2+n_5v_2^2+n_6 \bigr)\overline{v_2}\biggr] \\ &{}-\frac{1}{4}f_{23}^{\prime\prime }(0)\biggl[(t_7r_1+t_8r_2+t_9)v_2+ \frac{1}{2}\bigl(n_7v_1^2+n_8v_2^2+n_9 \bigr)\biggr] \\ &{}-\frac{1}{8}\bigl[f_{211}^{\prime\prime\prime}(0)v_1^2 \overline {v_1}+f_{222}^{\prime\prime\prime}(0)v_2^2 \overline {v_2}-f_{223}^{\prime\prime\prime}(0)\bigr], \\ p_1^{(3)} =&-\frac{1}{4}f_{31}^{\prime\prime }(0) \biggl[(t_1r_1+t_2r_2+t_3)v_1+ \frac {1}{2}\bigl(n_1v_1^2+n_2v_2^2+n_3 \bigr)\overline{v_1}\biggr] \\ &{}+\frac{1}{4}f_{32}^{\prime\prime }(0)\biggl[(t_4r_1+t_5r_2+t_6)v_2+ \frac {1}{2}\bigl(n_4v_1^2+n_5v_2^2+n_6 \bigr)\overline{v_2}\biggr] \\ &{}+\frac{1}{4}f_{33}^{\prime\prime }(0)\biggl[(t_7r_1+t_8r_2+t_9)v_2+ \frac{1}{2}\bigl(n_7v_1^2+n_8v_2^2+n_9 \bigr)\biggr] \\ &{}-\frac{1}{8}\bigl[f_{311}^{\prime\prime\prime}(0)v_1^2 \overline {v_1}+f_{322}^{\prime\prime\prime}(0)v_2^2 \overline {v_2}-f_{333}^{\prime\prime\prime}(0)\bigr]. \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, C., Zhang, Q. Bifurcation analysis of a tri-neuron neural network model in the frequency domain. Nonlinear Dyn 76, 33–46 (2014). https://doi.org/10.1007/s11071-013-1107-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-013-1107-1

Keywords

Navigation