A Neural Model on Cognitive Process

  • Rubin Wang
  • Jing Yu
  • Zhi-kang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper we studied a new dynamic evolution model on phase encoding in population of neuronal oscillators under condition of different phase, and investigated neural information processing in cerebral cortex and dynamic evolution under action of different stimulation signal. It is obtained that evolution of the averaging number density along with time in space of three dimensions is described in different cluster of neuronal oscillators firing action potential at different phase space by means of method of numerical analysis. The results of numerical analysis show that the dynamic model proposed in this paper can be used to describe mechanism of neurodynamics on attention and memory.


Neural Model Neural Population Neural Oscillator Neuronal Oscillator Average Number Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, R.B., Zhang, Z.K.: Nonlinear stochastic models of neurons activities. Neurocom-puting 51, 401–411 (2003)CrossRefGoogle Scholar
  2. 2.
    Wang, R.B., Zhang, Z.K.: A Dynamic Evolution Model for the Set of Populations of Neurons. Int. J. Nonlinear Sci. and Num. Simul. 4(3), 203–208 (2003)Google Scholar
  3. 3.
    Wang, R.B., Yu, W., Jiao, X.F.: A Review of Application on Stochastic Dynamics in Brain Information Processing. Fudan Lecture in Neurobiology 307, 193–206 (2004)Google Scholar
  4. 4.
    Wang, R.B., Jiao, X.F.: Stochastic Model and Neural Coding of Large-scale Neural Popu-lation with Variable Coupling Strength. Neurocomputing (2005) (in press)Google Scholar
  5. 5.
    Jiao, X.F., Wang, R.B.: Nonlinear Evolution Model and Neuronal Coding of Neuronal Population with the Variable Coupling Strength in the Presence of External Stimuli. Appl. Phys. Lett. 87, 83901 (2005)CrossRefGoogle Scholar
  6. 6.
    Wang, R.B.: A New Nonlinear Phase Setting Models in Neurons Activities. In: Wang, L.P., Rajapakse, J.C. (eds.) Proceedings of 9th International Conference on Neural Infor-mation Processing, vol. 5, pp. 2497–2501. Fudan University Press, Shanghai (2002)Google Scholar
  7. 7.
    Wang, R.B., Yu, W.: A Stochastic Nonlinear Evolution Model and Dynamic Neural Coding on Spontaneous Behavior of Large-scale Neuronal Population. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 490–497. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Wang, R.B., Yu, W.: Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation. J. Biol. Med. Eng (2006) (in press)Google Scholar
  9. 9.
    Tass, P.A.: Phase Resetting in Medicine and Biology. Springer, Heidelberg (1999)MATHGoogle Scholar
  10. 10.
    Wang, R.B.: Some Advances in Nonlinear Stochastic Evolution Models of Neuron Popu-lation. In: Zhu, W.Q. (ed.) Advance in Stochastic Structural Dynamics., pp. 453–461. CRC Press, USA (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rubin Wang
    • 1
    • 2
  • Jing Yu
    • 2
  • Zhi-kang Zhang
    • 1
  1. 1.Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and EngineeringEast China University of Science and TechnologyShanghaiP.R. China
  2. 2.School of ScienceDonghua UniversityShanghaiP.R. China

Personalised recommendations