Learning with single quadratic integrate-and-fire neuron

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3971 LNCS, Issue , 2006, Pages 424-429

  Mishra, Deepak ^a   Yadav, Abhishek ^b   Kalra, Prem K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

^b G B PANT UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; BENCHMARKING; FUNCTIONS; PROBLEM SOLVING;

MULTILAYER PERCEPTION; QUADRATIC INTEGRATE-AND-FIRE NEURON (QIFN);

NEURAL NETWORKS;

EID: 33745888685     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11759966_63     Document Type: Conference Paper

References (21)

1. McKenna, T., Davis, J., Zornetzer, S.F. (ed.): Single Neuron Computation. Academic Press, San Diego, CA, USA (1992)
2. Anderson, J.A., Rosenfeld, E. (ed.): Neurocomputing: Foundations of Research. MIT Press, Cambridge (1960)
3. Scholles, M., Hosticka, B.J., Kesper, M., Richert, P., Schwarz, M.: Biologically Inspired Artificial Neurons: Modeling and Applications. Proceedings of International Joint Conference on Neural Networks, Vol. 3 (1993) 2300-2303
4. Iannella, N., Back, A.: A Spiking Neural Network Architecture for Nonlinear Function Approximation. Proceedings of the 1999 IEEE Signal Processing Society Workshop, (1999) 139-146
5. Yadav, A., Mishra, D., Ray, S., Yadav, R.N., Kalra, P.K.: Learning with Single Integrate-and-Fire Neuron. Proceedings of International Joint Conference on Neural Network, (2005)
6. Freeman, W.J.: Why Neural Networks Dont Yet Fly: Inquiry into the Neurodynamics of Biological Intelligence. Proceedings of IEEE International Conference on Neural Networks, Vol. 2 (1988) 1-7
7. Hebb, D.: Organization of Behavior. John Weiley and Sons, New York (1949)
8. Widrow, B., Steams, S.: Adaptive Signal Processing. Prentice-Hall, Upper Saddle River, NJ USA (1985)
9. Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1999)
10. Hoppensteadt, F.C., Izhikevich, E.M.: Weakly Connected Neural Networks. Springer-Verlag, New York (1997)
11. Gerstner, W., Kistler, W.M.: Spiking Neuron Models: An Introduction. Cambridge University Press, New York (2002)
12. McCulloch, W.S., Pitts, W.S.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Mathematical Biophysics 5 (1943) 18-27
13. Sinha, M., Chaturvedi, D.K., Kalra, P.K.: Development of Flexible Neural Network. Journal of The Institution of Engineers (India) 83 (2002)
14. Feng, J., Buxton, H., Deng, Y.C.: Training the Integrate-and-Fire Model with the Informax Principle I. Journal of Physics-A 35 (2002) 2379-2394
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) (2003) 326-336
16. Feng, J., Li, G.: Neuronal Models with Current Inputs. Journal of Physics-A 34 (2001) 1649-1664
17. Liu, S.C., Douglas, R.: Temporal Coding in a Silicon Network of Integrate-and-Fire Neurons. IEEE Trans. on Neural Networks 15(5) (2004) 1305-1314
18. Ermentrout, G.B.: Type I Membranes, Phase Resetting Curves, and Synchrony. Neural Computation 8(5) (1996) 979-1001
19. Ermentrout, G.B., Kopell, N.: Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation. SIAM Journal of Applied Mathematics 46(2) (1986) 233-253
20. Latham, P.E., Richmond, B.J., Nelson, P.G., Nirenberg, S.: Intrinsic Dynamics in Neuronal Networks I. The Journal of Neurophysiology 83(2) (2000) 808-827
21. www.cs.colostate.edu/eeg/