Learning with Single Quadratic Integrate-and-Fire Neuron

1. McKenna, T., Davis, J., Zornetzer, S.F. (eds.): Single Neuron Computation. Academic Press, San Diego (1992)

   MATH  Google Scholar 

2. Anderson, J.A., Rosenfeld, E. (eds.): Neurocomputing:  Foundations of Research. MIT Press, Cambridge (1960)

   Google Scholar 

3. Scholles, M., Hosticka, B.J., Kesper, M., Richert, P., Schwarz, M.: Biologically Inspired Artificial Neurons: Modeling and Applications. In: Proceedings of International Joint Conference on Neural Networks, vol. 3, pp. 2300–2303 (1993)

   Google Scholar 

4. Iannella, N.,, B.A.: A Spiking Neural Network Architecture for Nonlinear Function Approximation. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop, pp. 139–146 (1999)

   Google Scholar 

5. Yadav, A., Mishra, D., Ray, S., Yadav, R.N., Kalra, P.K.: Learning with Single Integrate-and-Fire Neuron. In: Proceedings of International Joint Conference on Neural Network (2005)

   Google Scholar 

6. Freeman, W.J.: Why Neural Networks Don t Yet Fly: Inquiry into the Neurodynamics of Biological Intelligence. In: Proceedings of IEEE International Conference on Neural Networks, vol. 2, pp. 1–7 (1988)

   Google Scholar 

7. Hebb, D.: Organization of Behavior. John Weiley and Sons, New York (1949)

   Google Scholar 

8. Widrow, B., Steams, S.: Adaptive Signal Processing. Prentice-Hall, Upper Saddle River (1985)

   MATH  Google Scholar 

9. Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1999)

   Google Scholar 

10. Hoppensteadt, F.C., Izhikevich, E.M.: Weakly Connected Neural Networks. Springer, New York (1997)

    Google Scholar 

11. Gerstner, W., Kistler, W.M.: Spiking Neuron Models: An Introduction. Cambridge University Press, New York (2002)

    MATH  Google Scholar 

12. McCulloch, W.S., Pitts, W.S.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Mathematical Biophysics 5, 18–27 (1943)

    Google Scholar 

13. Sinha, M., Chaturvedi, D.K., Kalra, P.K.: Development of Flexible Neural Network. Journal of The Institution of Engineers (India) 83 (2002)

    Google Scholar 

14. Feng, J., Buxton, H., Deng, Y.C.: Training the Integrate-and-Fire Model with the Informax Principle I. Journal of Physics–A 35, 2379–2394 (2002)

    MATH  MathSciNet  Google Scholar 

15. Feng, J., Sun, Y., Buxton, H., Wei, G.: Training Integrate-and-Fire Neurons with the Informax Principle II. IEEE Trans. on Neural Networks 14(2), 326–336 (2003)

    Article  Google Scholar 

16. Feng, J., Li, G.: Neuronal Models with Current Inputs. Journal of Physics–A 34, 1649–1664 (2001)

    MATH  MathSciNet  Google Scholar 

17. Liu, S.C., Douglas, R.: Temporal Coding in a Silicon Network of Integrate-and-Fire Neurons. IEEE Trans. on Neural Networks 15(5), 1305–1314 (2004)

    Article  Google Scholar 

18. Ermentrout, G.B.: Type I Membranes, Phase Resetting Curves, and Synchrony. Neural Computation 8(5), 979–1001 (1996)

    Article  Google Scholar 

19. Ermentrout, G.B., Kopell, N.: Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation. SIAM Journal of Applied Mathematics 46(2), 233–253 (1986)

    Article  MATH  MathSciNet  Google Scholar 

20. Latham, P.E., Richmond, B.J., Nelson, P.G., Nirenberg, S.: Intrinsic Dynamics in Neuronal Networks I. The Journal of Neurophysiology 83(2), 808–827 (2000)

    Google Scholar 

21. http://www.cs.colostate.edu/eeg/

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