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Reseach Article

Hopfield Model of a Neuron Action under Dynamical Thresholds

by A.K. Verma, Ruby Khan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 6
Year of Publication: 2011
Authors: A.K. Verma, Ruby Khan
10.5120/2364-3105

A.K. Verma, Ruby Khan . Hopfield Model of a Neuron Action under Dynamical Thresholds. International Journal of Computer Applications. 19, 6 ( April 2011), 30-35. DOI=10.5120/2364-3105

@article{ 10.5120/2364-3105,
author = { A.K. Verma, Ruby Khan },
title = { Hopfield Model of a Neuron Action under Dynamical Thresholds },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 6 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number6/2364-3105/ },
doi = { 10.5120/2364-3105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:18.515353+05:30
%A A.K. Verma
%A Ruby Khan
%T Hopfield Model of a Neuron Action under Dynamical Thresholds
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 6
%P 30-35
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present Hopfield model of a neuron dynamics given by the neuronic equation. In the first model second order neuronic equation describe the behavior of a neuron in the presence of some local positive feedback. The second model portray two neurons in which first order neuronic equation represents dynamics of the second neuron in the presence of a discharged pulse coded signal function from the first neuron. We have shown that the solution is bounded and the paths surrounding the equilibrium point are not closed curves in the phase plane. Some conditions ensuring the existence and uniqueness of the equilibrium point are derived.

References
  1. Hopfield, J.J. 1982. Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. USA, Vol.79, no. 8,pp. 2554-2558.
  2. Hopfield, J.J.1984. Neurons with graded response have collective computational properties like those of two-state neurons, Proc. Nat. Acad. Sci. USA, Vol.81,no.10, pp. 3088-3092.
  3. Hopfield, J.J. and Tank, D.W.1985. Neural computation of decisions in optimization problems, Biological Cybernetics, Vol .52, no.3, pp. 141-152.
  4. Fabris, F. and Della, G. Riccia.1993. An application of the Hopfield model to Huffman codes, IEEE Trans. Information Theory, Vol.39, no.3,pp. 1071-1076.
  5. Cao, J.2001. Global stability conditions for delayed CNNs, IEEE Trans. Circuits Syst.I,Vol. 48, pp. 1330-1333.
  6. Driessche, P. V. D and Zou, X.1998. Global attractivity in delayed Hopfield neural network models, SIAM J. App. Math, Vol .58, no. 6,pp. 1878-1890.
  7. Farrell, J. A and Michel, A. N.1990. A synthesis procedure for Hopfield's continuous-time associative memory, IEEE Trans. Circuits Syst, Vol.37, pp. 877-884.
  8. Guan, Z. H. and Chen, G. R.1999. On delayed impulsive Hopfield neural networks, Neural Networks, Vol.12, no. 2, pp. 273-280.
  9. Mohamad, S.2003. Convergence dynamics of delayed Hopfield type neural networks under almost periodic stimuli, Acta Appl. Math,Vol. 76. no. 2, pp. 117-135.
  10. Gopalsamy, K. and Leung, I. K. C.1997. Convergence under dynamical thresholds with delays, IEEE Trans. Neural Network,Vol. 8, no.2, pp. 341-348.
  11. Zhang, F. Y. Li, W. T. and Huo, H. F.2003. Global stability of a class of delayed cellular neural networks with dynamical thresholds, International Journal of Applied Mathematics,Vol. 13, no. 4,pp. 359-368.
  12. Zhang, F. Y. and Li, W. T.2005. Global stability of delayed Hopfield neural networks under dynamical threshold, Discrete Dynamics in Nature and Society, Vol.2005, no.1, pp. 1-17.
  13. Zhang, F. Y. and Huo, H. F.2006. Global stability of Hopfiled neural networks under dynamical thresholds with distributed delays, Discrete Dynamics in Nature and Society, Vol.2006, Article ID 27941, pp. 1-11.
  14. Caianiello, E. R. and De Luca, A.1966. Decision equation for binary systems: Application to neuronal behavior, Kybernetik,Vol. 3, pp.33-40.
  15. Cronin, J.1964. Fixed points and topological degree theory in nonlinear analysis, Providence, RI, Amer.Math.Soc.
  16. Cao, J and Wang, J.2003. Global asymptotic stability of a general class of recurrent neural networks with time-varying delays, IEEE.Trans. Circuits Syst.I,Vol.no1,pp.34-44.
  17. Perko, L.1991. Differential Equations and Dynamical Systems, Springer- Verlag, New York.
  18. Kosko, B.2005. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall of India Private Limited.
Index Terms

Computer Science
Information Sciences

Keywords

Dynamical thresholds Equilibrium point Convergence Lyapunov functional Homotopic mapping Homotopy invariance principle Bendixson's criteria Limit cycles