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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 381271, 13 pages
Research Article

Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

1College of Mathematics and Computational Science, Hunan University of Arts and Science, Changde 415000, China
2Department of Mathematics, Hunan College of Finance and Economics, Changsha 410205, China

Received 6 July 2014; Accepted 7 September 2014

Academic Editor: Shifei Ding

Copyright © 2015 Xuyan Xiang and Jianguo Wu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Feng, Y. Deng, and E. Rossoni, “Dynamics of moment neuronal networks,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 73, no. 4, Article ID 041906, 17 pages, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  2. X. Y. Xiang and Y. C. Deng, “Training moment nenronal networks with intra- and interlayer interactions,” Information: An International Interdisciplinary Journal, vol. 15, pp. 363–374, 2012. View at Google Scholar
  3. T. P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press, Oxford, UK, 2002.
  4. X. Y. Xiang, Y. C. Deng, and X. Q. Yang, “Second order spiking perceptrons,” Soft Computing, vol. 13, pp. 1219–1230, 2009. View at Publisher · View at Google Scholar
  5. X. Y. Xiang and Y. C. Deng, “The learning of moment neuronal networks,” Nerucomputing, vol. 73, pp. 2597–2613, 2010. View at Google Scholar
  6. X. Y. Xiang, Y. Chen, and L. F. Liu, “An updated learning rule for moment neuronal networks,” Journal of Information and Computational Science, vol. 8, no. 13, pp. 2509–2516, 2011. View at Google Scholar
  7. J. F. Feng, Computational Neuroscience-A Comprehensive Approach, Chapman & Hall/CRC Press, London, UK, 2003. View at MathSciNet
  8. W. Gerstner and W. Kistler, Spiking Neuron Models, Cambridge University Press, London, UK, 2003.
  9. V. Livak, H. Sompolinsky, I. Segev, and M. Abeles, “On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance,” The Journal of Neuroscience, vol. 23, pp. 3006–3015, 2003. View at Google Scholar
  10. M. N. Shadlen and W. T. Newsome, “Noise, neural codes and cortical organization,” Current Opinion in Neurobiology, vol. 4, pp. 569–579, 1994. View at Google Scholar
  11. J. F. Feng and D. Brown, “Impact of correlated inputs on the output of the integrate-andfire models,” Neural Computation, vol. 12, pp. 671–692, 2000. View at Publisher · View at Google Scholar
  12. E. Zohary, M. N. Shadlen, and W. T. Newsome, “Correlated neuronal discharge rate and its implications for psychophysical performance,” Nature, vol. 370, pp. 140–143, 1994. View at Google Scholar
  13. H. C. Tuckwell, Introduction to Theoretical Neurobiology, Cambridge University Press, London, UK, 1988.
  14. I. Sadeghkhani, A. Ketabi, and R. Feuillet, “Radial basis function neural network application to measurement and control of shunt reactor overvoltages based on analytical rules,” Mathematical Problems in Engineering, vol. 2012, Article ID 647305, 14 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet