References

The following papers were cited above.

[1] Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences, 79, pp. 2554-2558, 1982.

[2] Hopfield, J.J. Neurons with graded response have collective computational properties like those of two-state neurons, Proceedings of the National Academy of Sciences, 81, pp. 3088-3092, 1984.

[3] Pawlicki, T.F., Lee, D.-S., Hull, J.J., and Sihari, S.N. Neural network models and their application to handwritten digit recognition, Proceedings of the IEEE International Conference on Neural Networks, vol. II, 1988, pp. 63-70.

[4] Ackley, D.H., Hinton, G.E., and Sejnowski, T.J. A learning algorithm for Boltzmann machines, Cognitive Science, 9, pp. 147-169, 1985.

[5] Foo, Y.-P. S., and Takefuji, Y. Stochastic Neural Networks for Solving Job-Shop Scheduling: Part 1. Problem Representation, Proceedings of the IEEE International Conference on Neural Networks, vol. II, 1988, pp. 275-282.

[6] Foo, Y.-P. S., and Takefuji, Y. Stochastic Neural Networks for Solving Job-Shop Scheduling: Part 2. Architecture and Simulations, Proceedings of the IEEE International Conference on Neural Networks, vol. II, 1988, pp. 283-290.

[7] Kosko, B. Bidirectional associative memories, IEEE Transactions on Systems, Man, and Cybernetics, SMC-18, pp. 42-60, 1988.

[8] Carpenter, G. and Grossberg, S. A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics and Image Understanding, 37, pp. 54-115, 1987.

[9] Carpenter, G. and Grossberg, S. ART2: Self-organization of stable category recognition codes for analog input patterns, Proceedings of the IEEE First International Conference on Neural Networks, vol. II, 1987, pp. 727-736.

[10] Carpenter, G. and Grossberg, S. ART2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics, 26, pp. 4919-4930, 1987.

[11] Grossberg, S. and Mingolla, E. Neural dynamics of perceptual grouping: Textures, boundaries and emergent segmentations, Perception and Psychophysics, 38, pp. 141-171, 1985.

[12] Carpenter, G. and Grossberg, S. Invariant pattern recognition and recall by an attentive self-organizing ART architecture in a nonstationary world, Proceedings of the IEEE First International Conference on Neural Networks, vol. II, 1987, pp. 737-746.

[13] Lozo, P., Johnson, R.P., Nandagopal, D., Nussey, G., and Zyweck, T. An Adaptive resonance Theory (ART) based Neural Network Approach to Classifying Targets in Infrared Images, Proceedings of the Second Australian Conference on Neural Networks, 1991, pp. 22-25.

[14] Fukushima, K., Miyake, S., and Ito, T. Neocognitron: a neural network model for a mechanism of visual pattern recognition, IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, pp. 826-834, 1983.

[15] Hubel, D.H., and Wiesel, T.N. Receptive fields, binocular interaction and functional architecture in cat's visual cortex, Journal of Physiology, 160, pp. 106-154, 1962.

[16] Hubel, D.H., and Wiesel, T.N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat, Journal of Neurophysiology, 28, pp. 229-289, 1965.

The following books were also used in preparing these sections.

Anderson, J. A., and Rosenfield, E. Neurocomputing: Foundations of Research, The MIT Press (Cambridge, Mass.), 1989.

Beale,R., and Jackson, T. Neural Computing: An Introduction, Adam Hilger (Bristol), 1990.

Hecht-Nielsen, R. Neurocomputing, Addison-Wesley (Reading, Mass.), 1991.

Simpson, P.K. Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations, Pergamon Press (New York), 1990.

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