Skip to main content
Log in

Complete synchronization of coupled Rulkov neuron networks

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

Abstract

Based on the master stability function (MSF) analysis, the complete synchronization of coupled chaotic Rulkov neuron networks is investigated in detail. The two-dimensional parameter-space plots that display directly the values of the MSF in different colors are numerically obtained. For the electrical coupled Rulkov neuron network, the values of MSF are all positive when the single Rulkov neuron is in chaotic bursting state or spiking state, which means that complete synchronization of the electrical coupled Rulkov neuron network can not attain. Importantly, a specific inner linking function is found to make the values of the MSF negative, which means that the necessary condition of complete synchronization is satisfied. Through numerical simulations, the existence of complete synchronization is verified for Rulkov neuron network with the very specific inner linking function that has been employed. In addition, the number of nodes of networks and the explicit thresholds for the coupling strength that guarantees the necessary condition of complete synchronization are derived in three typical regular networks. More interestingly, the same route of spatiotemporal patterns transition is found for Rulkov neurons in three typical regular networks.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Belykh, I., de Lange, E., Hasler, M.: Synchronization of bursting neurons: what matters in the network topology. Phys. Rev. Lett. 94, 188101 (2005)

    Article  Google Scholar 

  2. Belykh, I., Hasler, M.: Mesoscale and clusters of synchrony in networks of bursting neurons. Chaos 21, 016106 (2011)

    Article  MathSciNet  Google Scholar 

  3. Boccaletti, S., Kurths, J., Osipov, G., Valladares, D.L., Zhou, C.S.: The synchronization of chaotic systems. Phys. Rep. 366, 1–101 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cao, H.J., Sanjuán, M.A.F.: A mechanism for elliptic bursting and synchronization of bursts in a map-based neuron network. Cognit. Process. 10, 23–31 (2009)

    Article  Google Scholar 

  5. Cao, H.J., Wu, Y.G.: Bursting types and stable domains of Rulkov neuron network with the mean field coupling. Int. J. Bifurc. Chaos 23, 1330041 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. de Vries, G.: Bursting as an emergent phenomenon in coupled chaotic maps. Phys. Rev. E 64, 051914 (2001)

    Article  Google Scholar 

  7. Eckhorn, R.: Neural mechanisms of scene segmentation: recording from the visual cortex suggest basic circuits or linking field models. IEEE Trans. Neural Netw. 10, 464–479 (1999)

    Article  Google Scholar 

  8. Heagy, J.F., Pecora, L.M., Carroll, T.L.: Short wavelength bifurcations and size instabilities in coupled oscillator systems. Phys. Rev. Lett. 74, 4185–4188 (1995)

    Article  Google Scholar 

  9. Hu, D.P., Cao, H.J.: Stability and synchronization of coupled Rulkov map-based neurons with chemical synapses. Commun. Nonlinear Sci. Numer. Simul. (2015). doi:10.1016/j.cnsns.10.025

  10. Ibarz, B., Cao, H.J., Sanjuán, M.A.F.: Bursting regimes in map-based neuron models coupled through fast threshold modulation. Phys. Rev. E 77, 051918 (2008)

    Article  MathSciNet  Google Scholar 

  11. Ibarz, B., Casado, J.M., Sanjuán, M.A.F.: Map-based models in neuronal dynamics. Phys. Rep. 501, 1–74 (2011)

    Article  Google Scholar 

  12. Izhikevich, E.M., Hoppensteadt, F.: Classification of bursting mappings. Int. J. Bifurc. Chaos 14, 3847–3854 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  13. Izhikevich, E.M.: Dynamical Systems in Neroscience: The Geometry of Excitability and Bursting. The MIT press, Cambrigde (2007)

    Google Scholar 

  14. Juang, J., Liang, Y.H.: Cluster synchronization in networks of neurons with chemical synapses. Chaos 24, 013110 (2014)

    Article  MathSciNet  Google Scholar 

  15. Kocarev, L., Parlitz, U.: Generalized synchronization, predictability, and equivalence of unidirectionally coupled dynamical systems. Phys. Rev. Lett. 76, 1816–1819 (1996)

    Article  Google Scholar 

  16. Nordenfelt, A., Used, J., Sanjuán, M.A.F.: Bursting frequency versus phase synchronization in time delayed neuron networks. Phys Rev. E 87, 052903 (2013)

    Article  Google Scholar 

  17. Qin, H.X., Ma, J., Jin, W.Y., Wang, C.N.: Dynamics of electric activities in neuron and neurons of network induced by autapses. Sci. China Technol. Sci. 57, 936–946 (2014)

    Article  Google Scholar 

  18. Pecora, L.M., Carroll, T.L.: Synchronization in chaotic systems. Phys. Rev. Lett. 64, 821–825 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pecora, L.M., Carroll, T.L.: Master stability functions for synchronized coupled systems. Phys. Rev. Lett. 80, 2109–2112 (1998)

    Article  Google Scholar 

  20. Pikovsky, A., Rosenblum, M., Kurths, J.: Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press, New York (2001)

    Book  MATH  Google Scholar 

  21. Rabinovich, M.I., Varona, P., Selverston, I., Abarbanel, H.D.: Dynamical principles in neuroscience. Rev. Mod. Phys. 78, 1231–1265 (2006)

    Article  Google Scholar 

  22. Rosenblum, M., Pikovsky, A., Kurths, J.: Phase synchronization of chaotic oscillators. Phys. Rev. Lett. 76, 1804–1807 (1996)

    Article  MATH  Google Scholar 

  23. Rulkov, N.F.: Regularization of synchronized chaotic bursts. Phys. Rev. Lett. 86, 183–186 (2001)

    Article  Google Scholar 

  24. Rulkov, N.F., Timofeev, I., Bazhenov, M.: Oscillations in large-scale cortical networks: map-based model. J. Comput. Neurosci. 17, 203–223 (2004)

    Article  Google Scholar 

  25. Singer, W.: Time as Coding Space in Neocortical Processing: A Hypothesis. Springer, Berlin (1994)

    Google Scholar 

  26. Tanaka, G., Ibarz, B., Sanjuán, M.A.F., Aihara, K.: Synchronization and propagation of bursts in networks of coupled map neurons. Chaos 16, 013113 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  27. Uhlhaas, P.J., Singer, W.: Neural synchrony in brain disorders: relevance for cognitive Dysfunctions and pathophysiology. Neuron 52, 155–168 (2006)

    Article  Google Scholar 

  28. Wang, Q.Y., Duan, Z.S., Feng, Z.S., Chen, G.R., Lu, Q.S.: Synchronization transition in gap-junction-coupled leech neurons. Phys. A 387, 4404–4410 (2008)

    Article  Google Scholar 

  29. Wang, C.X., Cao, H.J.: Parameter space of the Rulkov chaotic neuron model. Commun. Nonlinear Sci. Numer. Simul. 19, 2060–2070 (2014)

    Article  MathSciNet  Google Scholar 

  30. Wang, C.X., Cao, H.J.: Stability and chaos of Rulkov map-based neuron network with electrical synapse. Commun. Nonlinear Sci. Numer. Simul. 20, 536–545 (2015)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by the Natural Science Foundation of China (NSFC) under Project No. 11171017 and the Fundamental Research Funds for the Central Universities under Project No. 2015YJS175.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongjun Cao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, H., Cao, H. Complete synchronization of coupled Rulkov neuron networks. Nonlinear Dyn 84, 2423–2434 (2016). https://doi.org/10.1007/s11071-016-2654-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-016-2654-z

Keywords

Navigation