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Neurocomputing: An Introduction

  • Bipin Kumar Tripathi
Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 571)

Abstract

Human brain is the gateway to the deepest mystery of modern computing science, which confines all the trump cards in the final frontier of technical and scientific inventions. It is an enigmatic and quaint field of investigation having long past and was always being a remarkable interesting area for researchers. The crucial characteristic of human intelligence is that it is evolving (developing, revealing, and unfolding) through genetically ‘wired’ rules and experience. Neurocomputing is the branch of science and engineering, which is based on human like intelligent behaviors of machines. It is a vast discipline of research that mainly includes neuroscience, machine learning, searching and knowledge representation. The traditional rule-based learning is now appears to be inadequate for various engineering applications because it is incompetent to serve increasing demand of machine learning when dealing with large amount of data. This opened up new avenues for the nonconventional computation models for such applications. Hence, it gives rise to new area of research, which is named as computational neuroscience. This chapter argues that we need to understand evolution of information processing in the brain and then use these principles when building intelligent machines through high dimensional neurocomputing.

Keywords

Neural Network Artificial Neural Network Activation Function Computational Neuroscience Artificial Neuron 
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.
    McCulloch, W.S., Pitts, W.: A logical calculation of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)Google Scholar
  2. 2.
    Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)Google Scholar
  3. 3.
    Rosenblatt, F.: The percepton : a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 231–237 (1958)MathSciNetGoogle Scholar
  4. 4.
    Tripathi, B.: Novel neuron models in complex domain and applications, PhD Thesis (Thesis defended on 21 April 2010), Indian Intitute of Technology, Kanpur (2010)Google Scholar
  5. 5.
    Kim, T., Adali, T.: Approximation by fully complex multilayer perceptrons. Neural Comput. 15, 1641–1666 (2003)CrossRefMATHGoogle Scholar
  6. 6.
    Isokawa, T., Nishimura, H., Matsui, N.: Quaternionic multilayer perceptron with local analyticity. Information, 3, 756–770 (2012)Google Scholar
  7. 7.
    Matsui, N., Isokawa, T., Kusamichi, H., Peper, F., Nishimura, H.: Quaternion neural network with geometrical operators. J. Intell. Fuzzy Syst. 15, 149–164 (2004)MATHGoogle Scholar
  8. 8.
    Nitta, T.: An extension of the back-propagation algorithm to complex numbers. Neural Netw. 10(8), 1391–1415 (1997)CrossRefGoogle Scholar
  9. 9.
    Tripathi, B.K., Kalra, P.K.: Complex generalized-mean neuron model and its applications. Appl. Soft Comput. 11(01), 768–777 (2011)Google Scholar
  10. 10.
    Hirose, A.: Complex-Valued Neural Networks. Springer, New York (2006)CrossRefMATHGoogle Scholar
  11. 11.
    Tripathi, B.K., Kalra, P.K.: The novel aggregation function based neuron models in complex domain. Soft Comput. 14(10), 1069–1081 (2010)Google Scholar
  12. 12.
    Georgiou, G.M., Koutsougeras, C.: Complex domain backpropagation. In: IEEE transaction on circuits and systems-II: Analog and Digital Signal Processing, vol. 39, no. 5, pp. 330–334, May 1992Google Scholar
  13. 13.
    Aizenberg, N.N., Yu, I.L., Pospelov, D.A.: About one generalization of the threshold function. Doklady Akademii Nauk SSSR (The Reports of the Academy of Sciences of the USSR) 196(6), 1287–1290 (1971) (in Russian)Google Scholar
  14. 14.
    Aizenberg, I., Moraga, C.: Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a back-propagation learning algorithm. Soft Comput. 11(2), 169–183 (2007)Google Scholar
  15. 15.
    Aizenberg, I., Aizenberg, N., Vandewalle, J.: Multi-valued and Universal Binary Neurons: Theory, Learning, Applications, ISBN 0-7923-7824-5. Kluwer Academic Publishers, London (2000)CrossRefGoogle Scholar
  16. 16.
    Tripathi, B.K., Kalra, P.K.: On the learning machine for three dimensional mapping. Neural Comput. Appl. 20(01), 105–111 (2011)Google Scholar
  17. 17.
    Nitta, T.: A quaternary version of the backpropagation algorithm. Proc. IEEE Int. Conf. Neural Netw. 5, 2753–2756 (1995)Google Scholar
  18. 18.
    Nitta, T.: N-dimensional Vector Neuron. IJCAI workshop, Hyderabad (2007)Google Scholar
  19. 19.
    Nitta, T.: Orthogonality of decision boundaries in complex-valued neural networks. Neural Comput. 16(1), 73–97 (2004)CrossRefMATHGoogle Scholar
  20. 20.
    Nitta, T.: Solving the XOR problem and the detection of symmetry using a single complex-valued neuron. Neural Netw. 16, 1101–1105 (2003)CrossRefGoogle Scholar
  21. 21.
    Amin, M.F., Murase, K.: Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing 72(4–6), 945–955 (2009)CrossRefGoogle Scholar
  22. 22.
    Chen, X., Tang, Z., Li, S.: A modified error function for the complex-value back propagation neural networks. Neural Inf. Process. 8(1), (2005)Google Scholar
  23. 23.
    Zurada, J.M.: Introduction to Artificial Neural Systems. Jaicob publishing house, India (2002)Google Scholar
  24. 24.
    Gupta, M.M., Homma, N.: Static and dynamic neural networks. Fundamentals to Advanced Theory, Wiley (2003)Google Scholar
  25. 25.
    Mandic, D., Goh, V.S.L.: Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  26. 26.
    Tripathi, B.K., Kalra, P.K.: On efficient learning machine with root power mean neuron in complex domain. IEEE Trans. Neural Netw. 22(05), 727–738, ISSN: 1045–9227 (2011)Google Scholar
  27. 27.
    Hamilton, W.R.: Lectures on Quaternions. Hodges and Smith, Ireland (1853)Google Scholar
  28. 28.
    Schwartz, C.: Calculus with a quaternionic variable. J. Math. Phys 50, 013523:1–013523:11 (2009)Google Scholar
  29. 29.
    Piazza, F., Benvenuto, N.: On the complex backpropagation algorithm. IEEE Trans. on Signal Process. 40(4), 967–969 (1992)Google Scholar
  30. 30.
    Srivastava, V.: Computational Intelligence Techniques: Development, Evaluation and Applications. PhD Thesis (Thesis defended on 26 April 2014), Uttar Pradesh Technical University, Lucknow (2014)Google Scholar
  31. 31.
    Taylor, J.G., Commbes, S.: Learning higher order correlations. Neural Netw. 6, 423–428 (1993)CrossRefGoogle Scholar
  32. 32.
    Chen, M.S., Manry, M.T.: Conventional modeling of the multilayer perceptron using polynomial basis functions. IEEE Trans. Neural Netw. 4, 164–166 (1993)Google Scholar
  33. 33.
    Kosmatopoulos, E., Polycarpou, M., Christodoulou, M., Ioannou, P.: High-order neural network structures for identification of dynamical systems. IEEE Trans. on Neural Netw. 6(2), 422–431 (1995)Google Scholar
  34. 34.
    Amin, M.F., Islam, M.M., Murase, K.: Ensemble of single-layered complex-valued neural networks for classification tasks. Neurocomputing 72(10–12), 2227–2234 (2009)CrossRefGoogle Scholar
  35. 35.
    Savitha, R., Suresh, S., Sundararajan, N., Kim, H.J.: Fast learning fully complex-valued classifiers for real-valued classification problems. In: Liu, D., et al. (ed.). ISNN 2011, Part I, Lecture Notes in Computer Science (LNCS), vol. 6675, pp. 602–609 (2011)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

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