Neural Networks

  • Stephen LynchEmail author


To provide a brief historical background to neural networks.


  1. [1]
    Z. G. Bandar, D. A. McLean, J. D. O’Shea, and J. A. Rothwell, Analysis of the behaviour of a subject, International Publication Number WO 02/087443 A1, (2002).Google Scholar
  2. [2]
    P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, Cambridge, MA, 2001.zbMATHGoogle Scholar
  3. [3]
    C. Dawson (Editor), Applied Artificial Neural Network, MDPI AG, Basel, 2016.Google Scholar
  4. [4]
    M. Di Marco, M. Forti, and A. Tesi, Existence and characterization of limit cycles in nearly symmetric neural networks. IEEE Trans. circuits & systems–1: Fundamental theory and applications, 49 (2002), 979–992.Google Scholar
  5. [5]
    W. J. Freeman, Neurodynamics: An Exploration in Mesoscopic Brain Dynamics (Perspectives in Neural Computing), Springer-Verlag, New York, 2000.CrossRefzbMATHGoogle Scholar
  6. [6]
    M. T. Hagan, H. B. Demuth, and M.H. Beale, Neural Network Design, Brooks-Cole, Pacific Grove, CA, 1995.Google Scholar
  7. [7]
    H. Haken, Brain Dynamics: An Introduction to Models and Simulations, Springer-Verlag, New York, 2008.zbMATHGoogle Scholar
  8. [8]
    S. O. Haykin, Neural Networks and Learning Machines, 3rd ed. Prentice-Hall, Upper Saddle River, NJ, 2008.Google Scholar
  9. [9]
    J. Heaton, Introduction to the Math of Neural Networks (Kindle Edition), Heaton Research Inc., 2012.Google Scholar
  10. [10]
    D. O. Hebb, The Organization of Behaviour, John Wiley, New York, 1949.Google Scholar
  11. [11]
    J. J. Hopfield and D. W. Tank, Neural computation of decisions in optimization problems, Biological Cybernetics, 52 (1985), 141–154.MathSciNetzbMATHGoogle Scholar
  12. [12]
    J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proc. National Academy of Sciences, 81 (1984), 3088–3092.CrossRefzbMATHGoogle Scholar
  13. [13]
    J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. National Academy of Sciences, 79 (1982), 2554–2558.MathSciNetCrossRefzbMATHGoogle Scholar
  14. [14]
    E.M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience), MIT Press, 2006.Google Scholar
  15. [15]
    T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43 (1982), 59–69.MathSciNetCrossRefzbMATHGoogle Scholar
  16. [16]
    B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, Upper Saddle River, NJ, 1999.zbMATHGoogle Scholar
  17. [17]
    W. McCulloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5 (1943), 115–133.MathSciNetCrossRefzbMATHGoogle Scholar
  18. [18]
    M. Minsky and S. Papert, Perceptrons, MIT Press, Cambridge, MA, 1969.zbMATHGoogle Scholar
  19. [19]
    F. Pasemann, Driving neuromodules into synchronous chaos, Lecture Notes in Computer Science, 1606 (1999), 377–384.CrossRefzbMATHGoogle Scholar
  20. [20]
    F. Pasemann, A simple chaotic neuron, Physica D, 104 (1997), 205–211.CrossRefzbMATHGoogle Scholar
  21. [21]
    T. Rashid, Make Your Own Neural Network, CreateSpace Independent Publishing Platform, 2016.Google Scholar
  22. [22]
    F. Rosenblatt, The perceptron: A probabalistic model for information storage and organization in the brain, Psychological Review, 65 (1958), 386–408.CrossRefGoogle Scholar
  23. [23]
    J. A. Rothwell, The word liar, New Scientist, March (2003), 51.Google Scholar
  24. [24]
    D. E. Rumelhart and J. L. McClelland, eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1, 1986.Google Scholar
  25. [25]
    I. W. Sandberg (ed.), J. T. Lo, C. L. Fancourt, J. Principe, S. Haykin, and S. Katargi, Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives (Adaptive Learning Systems to Signal Processing, Communications and Control), Wiley-Interscience, New York, 2001.Google Scholar
  26. [26]
    S. Samarasinghe, Neural Networks for Applied Sciences and Engineering, Auerbach, 2006.Google Scholar
  27. [27]
    S. J. Schiff, K. Jerger, D. H. Doung, T. Chang, M. L. Spano, and W. L. Ditto, Controlling chaos in the brain, Nature, 370 (1994), 615.CrossRefGoogle Scholar
  28. [28]
    A.M.F. Souza and F.M. Soares, Neural Network Programming with Java, Packt Publishing, 2016.Google Scholar
  29. [29]
    B. Widrow and M. E. Hoff, Adaptive switching circuits, 1960 IRE WESCON Convention Record, New York, IRE Part 4 (1960), 96–104.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterUK

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