Skip to main content
SpringerLink
Log in

Artificial Neural Network Modelling: An Introduction

  • Chapter
  • First Online:
Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

While scientists from different disciplines, such as neuroscience, medicine and high performance computing, eagerly attempt to understand how the human brain functioning happens, Knowledge Engineers in computing have been successful in making use of the brain models thus far discovered to introduce heuristics into computational algorithmic modelling. Gaining further understanding on human brain/nerve cell anatomy, structure, and how the human brain functions, is described to be significant especially, to devise treatments for presently described as incurable brain and nervous system related diseases, such as Alzheimer’s and epilepsy. Despite some major breakthroughs seen over the last few decades neuroanatomists and neurobiologists of the medical world are yet to understand how we humans think, learn and remember, and how our cognition and behaviour are linked. In this context, the chapter outlines the most recent human brain research initiatives following which early Artificial Neural Network (ANN) architectures, components, related terms and hybrids are elaborated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Furber, Billion pound brain project under way. BBC by Fergus Walsh, 2 Oct 2013, http://www.bbc.co.uk/news/health-24428162. Accessed 18 Oct 2015

  2. H. Markram, Billion pound brain project under way. BBC by Fergus Walsh, 7 Oct 2013, http://www.bbc.co.uk/news/health-24428162. Accessed 18 Oct 2015

  3. B. Obama, Obama proposes brain mapping project. BBC, 2 April 2013, http://www.bbc.co.uk/news/science-environment-22007007. Accessed 18 Oct 2015

  4. S.-i. Amari, Foreword, in Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering, ed. by N.K. Kasabov (USA, Bradford, 1995), p. xi

    Google Scholar 

  5. J. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan, 1975

    Google Scholar 

  6. G. Matsumato, The Brain can Acquire its Algorithm in a Self-Organized Fashion, in Proceedings of the ICONIP/ANZIIS/ANNES’99 International Workshop, Perth, Australia, 22–23 Nov 1999, p. 116

    Google Scholar 

  7. T.M. Mitchell, Machine learning and Data Mining. Communications of the ACM, Nov 1999, vol 42, No. 11

    Google Scholar 

  8. L. Fu, Knowledge discovery basedon neural networks. Communications of the ACM, Nov 1999, vol. 42. No. 11, pp. 47–50

    Google Scholar 

  9. I. Aleksander, I compute therefore I am. London BBC Science and Technology, 1999, http://news.bbc.co.uk/2/hi/science/nature/166370.stm. Accessed 27 Aug 2015

  10. N.K. Kasabov, Foundations of Neural Networks, A Bradford Book, (The MIT Press, Fuzzy systems and Knowledge Engineering, Cambridge, Massachusetts, London, England, 1995), 581 pp

    Google Scholar 

  11. T. Kohonene, G. Deboeck, Visual Explorations in Finance with Self-organizing Maps, (Springer, London, 1998), 258 pp

    Google Scholar 

  12. W. Pitts, W.S. McCulloch, How we know univesrsals, The perception of auditory and visual forms. Bull. Math. Biophys. 9(3), 127–147 (1947)

    Article  Google Scholar 

  13. W.S. McCulloch, W.H. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Google Scholar 

  14. B. Widrow, Generalization and information storage in networks of Adaline ‘Neurons’, in Self-Organizing Systems, symposium proceedings, ed. by M.C. Yovitz, G.T. Jacobi, G. Goldstein (Spartan Books, Washington, DC, 1962), pp. 435–461

    Google Scholar 

  15. B. Widrow, Review of “Adaptive Systems of Logic Network and Binary Memories”, Trans. Electron. Comput.: IEEE J. Aleksander EC16(5), 710–711, (1967)

    Google Scholar 

  16. B. Widrow, J.B. Angell, Reliable, trainable networks for computing and control. Aerosp. Eng. 21(9), 78–123 (1962)

    Google Scholar 

  17. B. Widrow, M.E. Hoff, Associative Storage and Retrieval of Digital Information in Networks of Adaptive `Neurons’. Biol. Prototypes Synth. Syst. 1, 160 (1962)

    Article  Google Scholar 

  18. M.L. Minsky, S. Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, Cambridge, MA, 1969)

    MATH  Google Scholar 

  19. S. Amari, Neural theory of association and concept formation. Biol. Cybern. 26, 175–185 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  20. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  21. D.E. Rumelhart, G. Hinton, R. Williams, Learning internal representation by error propagation, in D.E. Rumelhart, J.L. McClelland, PDP Research Group (eds), Parallel Distributed Processing Exploration in the Microstructure of Cognition: Foundations, vol. 1 (MIT Press, Cambridge, MA, 1986) p. 1

    Google Scholar 

  22. P. Werbos, Backpropagation through time: What it does and how to do it., Proc. IEEE 87, 10 (1990)

    Google Scholar 

  23. G.A. Carpenter, S. Grossberg, ART 2: Stable self-organization of pattern recognition codes for analog input patterns. Appl. Opt. 26, 4919–4930 (1987)

    Article  Google Scholar 

  24. G.A. Carpenter, S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vision, Graph., Image Proc. 37, 54–115 (1987)

    Article  MATH  Google Scholar 

  25. T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  26. B. Kosko, Bidirectional associative memories. IEEE Trans. Syst., Man Cybern. 18, 49–60 (1988)

    Article  MathSciNet  Google Scholar 

  27. T. Moody, C. Darken, Fast learning in networks of locally tuned processing units. Neural Comput. 1, 281–294 (1989)

    Article  Google Scholar 

  28. J. Taylor, C. Mannion, New Developments in Neural Computing (Adam Hilger, Bristol, England 1989)

    Google Scholar 

  29. I. Aleksander, Neural Computing Architectures. The Design of Brain-like Machines, in I. Aleksander, H. Morton (eds.), (MIT Press, Cambridge, MA, 1990) (An Introduction to Neural Computing, London, Chapman & Hall., 1989)

    Google Scholar 

  30. T. Yamakawa, Pattern recognition hardware system employing a fuzzy neuron, in Proceedings of the International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, July 1990, pp. 943–948

    Google Scholar 

  31. T. Furuhashi, T. Hasegawa, S. Horikawa et al., An adaptive fuzzy controller using fuzzy neural, in networks. In: Proceedings of Fifth International Fuzzy Systems Association World Congress, IEEE (1993), pp. 769–772

    Google Scholar 

  32. W. Freeman, C. Skarda, Spatial EEG patterns, non-linear dynamics and perception: The neo-Sherringtonian view. Brain Res. Rev. 10(10), 147–175 (1985)

    Article  Google Scholar 

  33. K. Kaneko, Clustering, coding, switching, hierarchical ordering, and control in network of chaotic elements. Physica 41D, 137–172 (1990)

    MathSciNet  MATH  Google Scholar 

  34. R. Borisyuk, A. Kirillov, Bifurcation analysis of neural network model. Biol. Cybern. 66, 319–325 (1992)

    Article  MATH  Google Scholar 

  35. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence (Englewood Cliffs, NJ, Prentice-Hall, 1992)

    MATH  Google Scholar 

  36. F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the Brain. Psychol. Rev. 65, 386–408 (1958)

    Article  MathSciNet  Google Scholar 

  37. B. Widrow, M.E. Hoff, Adaptive switching circuits. IRE WESCON Convention Rec. N.Y. 4, 96–104 (1960)

    Google Scholar 

  38. A. Newell, H.A. Simon, Human Problem Solving, Englewood Cliffs (Prentice Hall, NJ, 1972)

    Google Scholar 

  39. P. Smolenski, Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artif. Intell. 46, 159–216 (1990)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Subana Shanmuganathan

Authors
  1. Subana Shanmuganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subana Shanmuganathan .

Editor information

Editors and Affiliations

  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Shanmuganathan, S. (2016). Artificial Neural Network Modelling: An Introduction. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28495-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28493-4

  • Online ISBN: 978-3-319-28495-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation