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Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

  • Thomas Petsche
  • Bradley W. Dickinson
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 661)

Keywords

Receptive Field Input Sequence Connection Weight Convolutional Code Constraint Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. Bruck and M. Blaum, Neural networks, error-correcting codes, and polynomials over the binary n-cube, IEEE Transactions on Information Theory, vol. 35, pp. 976–987, Sept. 1989.Google Scholar
  2. 2.
    E. Baum, J. Moody, and F. Wilczek, Internal representations for associative memory, Biological Cybernetics, vol. 59, pp. 217–228, 1988.Google Scholar
  3. 3.
    G. Hinton, J. McClelland, and D. Rumelhart, Distributed representations, in Parallel Distributed Processing, vol. I (D. Rumelhart and J. McClelland, eds.), ch. 3, pp. 77–109, MIT Press, 1986.Google Scholar
  4. 4.
    S. Hanson and L. Pratt, Comparing biases for minimal network construction with back-propagation, in Advances in Neural Information Processing Systems 1 (D. Touretzky, ed.), pp. 177–185, Morgan Kaufmann, 1989.Google Scholar
  5. 5.
    T. Petsche and B. W. Dickinson, A trellis-structured neural network, in Proceedings of the 1987 Conference on Neural Information Processing Systems (D. Z. Anderson, ed.), pp. 592–601, American Institute of Physics, Nov. 1987.Google Scholar
  6. 6.
    D. Tank and J. Hopfield, Simple “neural” optimization networks: An A/D converter, signal decision circuit and a linear programming circuit, IEEE Trans. on Circuits and Systems, vol. 33, pp. 533–541, May 1986.Google Scholar
  7. 7.
    A. Viterbi and J. Omura, Principles of Digital Communications and Coding. McGraw-Hill, 1979.Google Scholar
  8. 8.
    S. Grossberg, How does a brain build a cognitive code, in Studies of Mind and Brain, pp. 1–52, D. Reidel Publishing Company, 1982.Google Scholar
  9. 9.
    R. McEliece, E. Posner, E. Rodemich, and S. Venkatesh, The capacity of the Hopfield associative memory, IEEE Trans. on Information Theory, vol. 33, no. 4, pp. 461–482, 1987.Google Scholar
  10. 10.
    P. A. Chou, The capacity of the Kanerva associative memory, IEEE Transactions on Information Theory, vol. 35, pp. 281–298, Mar. 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Thomas Petsche
    • 1
  • Bradley W. Dickinson
    • 2
  1. 1.Siemens Corporate Research, Inc.Princeton
  2. 2.Department of Electrical EngineeringPrinceton UniversityUSA

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