Neural Networks

  • Sigeru Omatu
  • Marzuki Khalid
  • Rubiyah Yusof
Part of the Advances in Industrial Control book series (AIC)


Neural networks are networks of nerve cells (neurons) in the brain. The human brain has billions of individual neurons and trillions of interconnections. Neurons are continuously processing and transmitting information to one another. In 1909, Cajal [1], [2] found that the brain consists of a large number of highly connected neurons which apparently can send very simple excitatory and inhibitory messages to each other and update their excitations on the basis of these simple messages. Figure 2.1.1 shows Purkinje Cell with its dendrite stained [2]. A neuron has three major regions; the cell body (soma), the axon, and the dendrites as shown in Fig. 2.1.2 [2].


Neural Network Hide Layer Synaptic Vesicle Steep Descent Connection Weight 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Cajal, S.R., “Histologie du Systeme nerveux de 1-homme et des vertebres”, Maloine, Paris, 1909.Google Scholar
  2. [2]
    Dayhoff, J.E., “Neural Network Architectures: An Introduction”, Van Nostrand Reinhold, New York, 1990.Google Scholar
  3. [3]
    McCulloch, W.S. and W. Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics, Vol. 9, pp. 127–147, 1943.Google Scholar
  4. [4]
    Hebb, D.O., “The Organization of Behavior”, Wiley, New York, 1949.Google Scholar
  5. [5]
    Bernstein, N., “Profiles:AI, Marvin Minsky”, The New Yorker, pp. 50–126, 1981.Google Scholar
  6. [6]
    Rosenblatt, F., “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms”, Spartan Book, Washinghton D.C., 1961.Google Scholar
  7. [7]
    Minsky, M.L. and S.A. Papert, “Perceptrons”, MIT Press, Cambridge, MA, 1969.zbMATHGoogle Scholar
  8. [8]
    Hopfield, J.J., “Neural networks and physical systems with emergent computational abilities”, Proc. of the National Academy of Sciences“, Vol. 79, pp. 2554–2558, 1982.MathSciNetCrossRefGoogle Scholar
  9. [9]
    Hopfield, J.J. and D.W. Tank, “Neural computation of decisions in optimization problems”, Biological Cybernetics, Vol. 52, pp. 141–152, 1985.MathSciNetzbMATHGoogle Scholar
  10. [10]
    Werbos, P.J., “Beyond regression: New tools for prediction and analysis in the behavioral sciences”, Ph.D. Thesis, Applied Mathematics, Harvard University, Nov., 1974.Google Scholar
  11. [11]
    Rumelhart, D.E. et al., “ Parallel Distributed Processing: Explorations in the Micro Sructure of Cognition Vol.I: Foundations”, MIT Press, Cambridge, MA, 1986.Google Scholar
  12. [12]
    Rumelhart, D.E. and D. Zipser, “Feature discovery and competitive learning”, Cognitive Science, Vol. 9, pp. 75–112, 1985.CrossRefGoogle Scholar
  13. [13]
    Grossberg, S., “Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors”, Biological Cybernetics, Vol. 23, pp. 121–134, 1976.MathSciNetzbMATHCrossRefGoogle Scholar
  14. [14]
    Kohonen, T., “Self-organized formation of topologically correct feature maps”, Biological Cybernetics, Vol. 43, pp. 59–69, 1982.MathSciNetzbMATHCrossRefGoogle Scholar
  15. [15]
    Widrow, B., “ADALINE and MADALINE - 1963”, Plenary Speech, Vol. I, Proc. of IEEE First Int. Conf. on Neural Networks, San Diego, CA, pp. 143–158, 1987.Google Scholar

Copyright information

© Springer-Verlag London Limited 1996

Authors and Affiliations

  • Sigeru Omatu
    • 1
  • Marzuki Khalid
    • 2
  • Rubiyah Yusof
    • 2
  1. 1.Department of Computer and Systems Sciences, College of EngineeringOsaka Prefecture UniversitySakai, Osaka 593Japan
  2. 2.Business and Advanced Technology CentreUniversiti Teknologi MalaysiaKuala LumpurMalaysia

Personalised recommendations