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2002: Comparing Immune and Neural Networks

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SBRN 2002 (Simpósio Brasileiro de Redes Neurais, Recife, PE.

SBRN 2002 (Simpósio Brasileiro de Redes Neurais, Recife, PE.

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  • 1. Comparing Immune and Neural Networks Leandro Nunes de Castro [email_address] http://www.dca.fee.unicamp.br/~lnunes CNPq Profix: 540396/01-1 Computer and Electrical Engineering School State University of Campinas
  • 2. Contents
    • Background and Motivation
    • The Immune System and Network Theory
    • A Brief Outline of Neural Networks
    • Similarities and Differences
    • Discussion
  • 3. Background and Motivation
    • The nervous and immune system can be viewed as composed of networks
    • Immune networks as a new computational intelligence approach
    • New avenues for research in both fields
    • Development of hybrid structures
  • 4. The Immune System and Network Theory
    • Basic components
  • 5.
    • The Immune Network Theory
    The Immune System and Network Theory
  • 6.
    • Immune network models:
      • Continuous
      • Discrete
    • s = NA  NS + INC  DUC
      • s : stimulation
      • NA: network activation
      • NS: network suppression
      • INC: influx of new cells
      • DUC: death of unstimulated cells
    The Immune System and Network Theory
  • 7. Neural Networks
    • Characterized by:
      • Neuron model
      • Network architecture
        • Feedforward
        • Recurrent
      • Learning approaches
        • Supervised
        • Unsupervised
        • Reinforcement
  • 8. Immune and Neural Networks
    • Immune
      • Cells or molecules composed of attribute strings in a given shape-space
      • Connection links
      • Stimulation level
    • Neural
      • Cells composed of an integrator and an activation function
      • Connection links
      • Linear combination of the inputs and the weight vector
      • “ Feedforward” signal propagation
    Basic Components
  • 9. Immune and Neural Networks
    • Immune
      • Response: reproduction and/or alteration in its shape
      • Cells and molecules distributed according to the universe of antigens
    • Neural
      • Response: output stimulus (real value or spike)
      • Neurons have pre-specified positions in the network
    Basic Components
  • 10. Immune and Neural Networks
    • Immune
      • Indicate with which elements a cell interacts
      • Its strength corresponds to the degree of interaction
      • Stimulatory or suppressive interactions
      • Not directly updated
    • Neural
      • Indicate with which neurons a unit interacts
      • Weights the degree of input stimulation
      • Excitatory or inhibitory interactions
      • Tuned so as to perform better in the environment
    Connections
  • 11. Immune and Neural Networks
    • Immune
      • “ Free” structures that usually follow the spatial distribution of the input data set
      • Adaptation based on the stimulation level. It allows for the reproduction and adaptation of attributes
    • Neural
      • Usually, pre-defined structures, even if constructive or pruning techniques are used
      • Adaptation based on the activation. It allows for the variation in connection strengths (attributes)
    Structure and Adaptability
  • 12. Discussion
    • Quite different approaches
      • Components
      • Structure
      • Adaptation
    • Similar applications:
      • Pattern recognition and classification, prediction, robotics, data analysis, function optimization
    • Hybrid algorithms

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