1. Anderson,J.A, Silverstein, J.W., Ritz, S.A. & Jomnes, R.S. (1977). Distinctive features, categorical perception and probability learning: some aplications of a neural model. Psychological Review, 84.
2. German, S. y German, D. (1984) Stochastic relaxation, Gibbs distributions and Baysian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741
3. Grossberg, S. (1974). Classical and instrumental learning by neural networks. Progress in theoretical biology, vol. 3, pp. 51-141. New York: Academic Press.
4. Grossberg, S. (1987). Competitive learning: from interactive activation to adaptative resonance. Cognitive Science, 11, 23-63.
5. Hebb, D.O. (1949). Organization of behavior. New York: Science Editions.
6. Hertz, J., Krogh, A. & Palmer, R.G. (1991). Introduction to the Theory of Neural Computation. Addison - Wesley.
7. Hinton, G.E y Sejnowski, T.J. (1986) Learning and relearning in Boltzman machines. En Rumerlhart & McClelland (1986)
8. Hinton, G.E. (1992) Redes neuronales que aprenden de la experiencia. Investigación y ciencia, noviembre, 1992
9. Hoplfield, J.L. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 79, 2554-2558.
10. Kohonen, T. (1984). Self-organization and associative memory, Series in Information Sciences, vol. 8. Berlin: Springer-Verlag.
11. Kolen, J.F. y Goel, A.K. (1991) Learning in parallel distributed processing networks: Computational complexity and information content. IEEE Transactions on systems, man and cybernetics, 21, 359-367.
12. Kosko, B. (1987) Bi-directional associative memories. IEEE Transactions on Systems, Man and Cybernetics 18(1): 49-60
13. Minai, A.A. y Williams, R.D. (1990) Acceleration of back-propagation trough learning rate and momentum adaptation. International Joint Conference on Neural Networks, 1, 676-679
14. Minsky, M.L. & Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.
15. Nelson, M.N.; Illingworth, W.T. (1990). A practical Guide to Neural Nets, Addison Wesley. (Incluye software para PC)
16. NeuralWare, Inc (1991) Neural Computing. Neural Ware, Inc. Pittsburg, PA
17. Perazzo, R. (1991) Redes artificiales y modelos del funcionamiento cerebral. Ciencia Hoy, 3-13, mayo/junio 1991, pp 43-54
18. Pérez, JC. Modelos conexionistas. Universidad de Valencia (inédito)
19. Pitarque, A., Ruiz, J.C. y Algarabel, S. Una introducción a los principales tipos de arquitectura conexionista. Universidad de Valencia (inédito)
20. Quinlan,(1991). Connectionism and Psychology. Harvester Wheateaf. N.Y.
21. Ratcliff, R. (1990) Connectionism models of recognition memory.: Constraints imposed by learning and forgettiong functions. Psychological Review, 97, 285-308.
22. Rosemblatt, R. (1962) Principles of neurodynamicas. New York: Spartan Books.
23. Rumelhart, D.E., McClelland, J.L. & Group, PR (1986) Parallel Distributed Processing. Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press (incluye Software para PC)
24. Rumelhart, D.E., McClelland, JL. (1988). Explorations in Parallel Distributed Processing. A Handbook of Models. Programs and Exercices. Cambridge, MA: MIT Press.
25. Szu, H. y Hartley, R.(1987). Fast simulated annealing. Physics Letters 1222(3,4), 157-162.
26. Waltz, D. & Feldman, J.A. (1988). Connectionist Models and Their Implications. In Waltz, D. & Feldman ,J.A. (De.), Connectionist Models and Their Implications, Norwood, NJ: Ablex Publishing.
27. Wassermann, P.D. (1989). Neural computing: Theory and Practice. VNR. New York
28. Widrow, B. (1959) Adaptative sampled-data systems, a statistical theory of adaptation. 1959 IRE WESCON Convention Record, part 4. New York: Institure of Radio Engineers.