World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

Bifurcation Analysis of the Nagumo–Sato Model and Its Coupled Systems

    https://doi.org/10.1142/S0218127416300068Cited by:3 (Source: Crossref)

    The Nagumo–Sato model is a simple mathematical expression of a single neuron, and it is categorized as a discrete-time hybrid dynamical system. To compute bifurcation sets in such a discrete-time hybrid dynamical system accurately, conditions for periodic solutions and bifurcations are formulated herewith as a boundary value problem, and Newton’s method is implemented to solve that problem. As the results of the analysis, the following properties are obtained: border-collision bifurcations play a dominant role in dynamical behavior of the model; chaotic regions are distinguished by tangent bifurcations; and multistable attractors are observed in its coupled system. We demonstrate several bifurcation diagrams and corresponding topological properties of periodic solutions.

    References

    • Aihara, K., Takabe, T. & Toyoda, M. [1990] “ Chaotic neural networks,” Phys. Lett. A 144, 333–340. Crossref, Web of ScienceGoogle Scholar
    • Banerjee, S., Karthik, M. S., Yuan, G. & Yorke, J. A. [2000] “ Bifurcations in one-dimensional piecewise smooth maps — Theory and applications in switching circuits,” IEEE Trans. Circuits Syst.-I: Fund. Th. Appl. 47, 389–394. Crossref, Web of ScienceGoogle Scholar
    • Bemporad, A. & Morari, M. [1999] “ Control of systems integrating logic, dynamics and constraints,” Automatica 35, 407–427. Crossref, Web of ScienceGoogle Scholar
    • Bernardo, M., Budd, C. J., Champneys, A. R. & Kowalczyk, P. [2008] Piecewise-Smooth Dynamical Systems: Theory and Applications (Springer-Verlag, London). Google Scholar
    • Blondel, V. & Tsitsiklis, J. [1999] “ Complexity of stability and controllability of elementary hybrid systems,” Automatica 35, 479–489. Crossref, Web of ScienceGoogle Scholar
    • Boyland, P. L. [1986] “ Bifurcations of circle maps: Arnol’d tongues, bistability and rotation intervals,” Commun. Math. Phys. 106, 353–381. Crossref, Web of ScienceGoogle Scholar
    • Crutchfield, J. & Kaneko, K. [1988] “ Are attractors relevant to turbulence?Phys. Rev. Lett. 60, 2715–2718. Crossref, Web of ScienceGoogle Scholar
    • Fujii, H. & Tsuda, I. [2004] “ Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony,” Neurocomputing 58–60, 151–157. Crossref, Web of ScienceGoogle Scholar
    • Hata, M. [1982] “ Dynamics of Caianiello’s equation,” J. Math. Kyoto Univ. 22, 155–173. Crossref, Web of ScienceGoogle Scholar
    • Heemels, W., Schutter, B. D. & Bemporad, A. [2001] “ Equivalence of hybrid dynamical models,” Automatica 37, 1085–1091. Crossref, Web of ScienceGoogle Scholar
    • Ito, D., Ueta, T., Imura, J. & Aihara, K. [2011] “ Analysis and controlling of interrupt chaotic systems by a switching threshold,” Proc. NOLTA 2011, pp. 577–580. Google Scholar
    • Kinoshita, K. & Ueta, T. [2010] “ Bifurcation analysis of coupled Nagumo–Sato models,” Proc. NOLTA 2010, pp. 488–491. Google Scholar
    • Kitajima, H., Yoshinaga, T., Aihara, K. & Kawakami, H. [2001] “ Chaotic bursts and bifurcation in chaotic neural networks with ring structure,” Int. J. Bifurcation and Chaos 11, 1631–1643. Link, Web of ScienceGoogle Scholar
    • Kousaka, T., Ueta, T. & Kawakami, H. [1999] “ Bifurcation of switched nonlinear dynamical systems,” IEEE Trans. CAS-II 46, 878–885. Crossref, Web of ScienceGoogle Scholar
    • Kuznetsov, Y. A. [2004] Elements of Applied Bifurcation Theory, 3rd edition (Springer-Verlag, NY). CrossrefGoogle Scholar
    • Nagumo, J. & Sato, S. [1972] “ On a response characteristic of a mathematical neuron model,” Biol. Cybern. 10, 155–164. Google Scholar
    • Nakagawa, M. & Okabe, M. [1992] “ On the chaos region of the modified Nagumo–Sato model,” J. Phys. Soc. Japan 61, 1121–1124. CrossrefGoogle Scholar
    • Oku, M. & Aihara, K. [2012] “ Numerical analysis of transient and periodic dynamics in single and coupled Nagumo–Sato models,” Int. J. Bifurcation and Chaos 22, 1230021-1–15. Link, Web of ScienceGoogle Scholar
    • Politi, A., Livi, R., Oppo, G. & Kapral, R. [1993] “ Unpredictable behaviour in stable systems,” Europhys. Lett. 22, 571–576. CrossrefGoogle Scholar
    • Ueta, T., Miyazaki, H., Kousaka, T. & Kawakami, H. [2004] “ Bifurcation and chaos in coupled BVP oscillators,” Int. J. Bifurcation and Chaos 14, 1305–1324. Link, Web of ScienceGoogle Scholar
    Remember to check out the Most Cited Articles!

    Check out our Bifurcation & Chaos