×

Wilson-Cowan neural-network model in image processing. (English) Zbl 0797.68172

Summary: The neural-network model based on the theory proposed by Wilson and Cowan has been simulated by using digitized real images. Mathematically, the model is based on coupled nonlinear differential equations that describe the functional dynamics of cortical nervous tissue, and the model can operate in different dynamical modes, depending on coupling strengths. The model is shown to store images in reduced form and to recognize edges of an object. Examples of how the network processes input images are shown.

MSC:

68U10 Computing methodologies for image processing
92B20 Neural networks for/in biological studies, artificial life and related topics
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] H.R. Wilson and J.D. Cowan, ?Excitatory and inhibitory interactions in localized populations of model neurons?, Biophys. J., vol. 12, pp. 1-24, 1972.
[2] H.R. Wilson and J.D. Cowan, ?A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue,? Kybernetik, vol. 13, pp. 55-80, 1973. · Zbl 0281.92003
[3] H. Sakaguchi, ?Oscillatory and excitable behaviours in a population of model neurons,? Prog. Theor. Phys., vol. 79, pp. 1061-1068, 1988.
[4] M.M. Gupta and G.K. Knopf, ?A dynamic neural network for visual memory,? in Visual Communications and Image Processing ’90: Fifth in a Series, M. Kunt, ed., Proc. Soc., Photo-Opt. Instrum. Eng., vol. 1360, pp. 1044-1055, 1990.
[5] J.L. Horner (ed.), Optical Signal Processing, Academic Press; San Diego, CA, 1987.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.