2002: Comparing Immune and Neural Networks


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

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2002: Comparing Immune and Neural Networks

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