• DocumentCode
    3208805
  • Title

    Comparing immune and neural networks

  • Author

    De Castro, Leandro Nunes

  • Author_Institution
    Dept. of Comput. Eng. & Ind. Autom., State Univ. of Campinas, Brazil
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    250
  • Lastpage
    255
  • Abstract
    The complexity of the immune system is sometimes compared to that of the brain. Both systems can be viewed as composed of networks of elements, which endow them with interesting features for the development of computational tools with potentialities for problem solving. This paper has two main goals: 1) to introduce the general features of immune networks to the artificial neural network (ANN) community; and 2) to present a theoretical comparison between an ANN and a standard immune network. The comparison is highly simplified and general, taking into account how each network is structured, their basic components and mechanisms of adaptation, and information processing capabilities.
  • Keywords
    learning (artificial intelligence); neural nets; pattern recognition; antigenic recognition; differential equations; immune networks; immune system; information processing; learning algorithms; neural network; pattern recognition; Artificial neural networks; Biological neural networks; Biological system modeling; Computer networks; Immune system; Nervous system; Neural networks; Neurons; Pattern recognition; Viruses (medical);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on
  • Print_ISBN
    0-7695-1709-9
  • Type

    conf

  • DOI
    10.1109/SBRN.2002.1181486
  • Filename
    1181486