Time-series prediction with single integrate-and-fire neuron

Applied Soft Computing Journal

Volumn 7, Issue 3, 2007, Pages 739-745

  Yadav, A ^a   Mishra, D ^a   Yadav, R N ^a   Ray, S ^a   Kalra, P K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

Backpropagation; Integrate and fire neuron; Time series prediction

Indexed keywords

BACKPROPAGATION; LEARNING ALGORITHMS; MULTILAYER NEURAL NETWORKS; PROBLEM SOLVING; TIME SERIES ANALYSIS;

INTEGRATE AND FIRE NEURONS (IFN); MULTILAYER PERCEPTRON NEURAL NETWORKS; TIME SERIES PREDICTION;

NEURONS;

EID: 34047260770     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2006.02.004     Document Type: Article

References (23)

1. Freeman W.J. Why neural networks don't yet fly: inquiry into the neuro-dynamics of biological intelligence. IEEE Int. Conf. Neural Netw. 1 2 (1988) 1-7
2. McCulloch W., and Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5 (1943) 115-133
3. Hebb D. Organization of Behavior (1949), Wiley, New York
4. Widrow B., and Hoff M.E. Adaptive Switching Circuits (1960), IREWESCON Connection Recors, IRS, New York
5. Widrow B., and Steams S. Adaptive Signal Processing (1985), Prentice-Hall, Englewood Cliffs, NJ
6. Sinha M., Chaturvedi D.K., and Kalra P.K. Development of flexible neural network. J. IE(I) 83 (2002)
7. R.N. Yadav, V. Singh, P.K. Kalra, Classification using single neuron, in: Proceedings of the IEEE International Conference on Industrial Informatics, Canada, August, 2003, pp. 124-129.
8. D. Mishra, A. Yadav, P.K. Kalra, Chaotic behavior in neural networks and FitzHugh-Nagumo neuronal model, in: Proceedings of the ICONIP-2004, LNCS 3316, India 2004, pp. 868-873.
9. Koch C., and Poggio T. Multiplying with synapses and neurons. Single Neuron Computation (1992), Academic Press, Boston, Massachusetts pp. 315-315
10. Koch C. Biophysics of Computation: Information Processing in Single Neurons (1999), Oxford University Press
11. Feng J., Buxton H., and Deng Y.C. Training the integrate-and-fire model with the Informax principle I. J. Phys. A 35 (2002) 2379-2394
12. Feng J., Sun Y., Buxton H., and Wei G. Training integrate-and-fire neurons with the Informax principle II. IEEE Trans. Neural Netw. 14 (2003) 326-336
13. Feng J., and Li G. Neuronal models with current inputs. J. Phys. A 24 (2001) 1649-1664
14. Scholles M., Hosticka B.J., Kesper M., Richert P., and Schwarz M. Biologically inspired artificial neurons: modeling and applications. Proceedings of the 1993 International Joint Conference on Neural Networks, IJCNN '93-Nagoya, vol. 3 (1993) 2300-2303
15. Iannella N., and Back A. A spiking neural network architecture for nonlinear function approximation, Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop (1999) 139-146
16. Liu S.C., and Douglas R. Temporal coding in a silicon network of integrate-and-fire neurons. IEEE Trans. Neural Netw. 15 (2004) 1305-1314
17. Gerstner W. Time structure of the activity in neural network models. Phys. Rev. E 51 (1995) 738-758
18. Hodgkin A.L., and Huxley A.F. A quantitative description of membrane current and application to conduction and excitation in nerve. J. Physiol. 117 (1954) 500-544
19. Hodgkin A.L. The local changes associated with repetitive action in a non-modulated axon. J. Physiol. 107 (1948) 165-181
20. Xu K., Xie M., Tang L.C., and Ho S.L. Application of neural networks in forecasting engine systems reliability. Appl. Soft Comput. 2 4 (2003) 255-268
21. Dudul S.V. Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network. Appl. Soft Comput. 5 4 (2005) 333-355
22. Carpinteiro O.A.S., Reis A.J.R., and da Silva A.P.A. A hierarchical neural model in short-term load forecasting. Appl. Soft Comput. 4 4 (2004) 405-412
23. http://www.cs.colostate.edu/eeg/