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A General Learning Rule for Network Modeling of Neuroimmune Interactome

  • D. Remondini
  • P. Tieri
  • S. Valensin
  • E. Verondini
  • C. Franceschi
  • F. Bersani
  • G. C. Castellani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)

Abstract

We propose a network model in which the communication between its elements (cells, neurons and lymphocytes) can be established in various ways. The system evolution is driven by a set of equations that encodes various degrees of competition between elements. Each element has an “internal plasticity threshold” that, by setting the number of inputs and outputs, determines different network global topologies.

Keywords

Network Theory Learning Rule Immune Network Idiotypic Network Base Learning Rule 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • D. Remondini
    • 1
    • 3
    • 4
  • P. Tieri
    • 1
    • 2
  • S. Valensin
    • 1
    • 2
  • E. Verondini
    • 1
    • 4
  • C. Franceschi
    • 1
    • 2
  • F. Bersani
    • 1
    • 4
  • G. C. Castellani
    • 1
    • 3
    • 4
  1. 1.“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and BiocomplexityItaly
  2. 2.Department of experimental PathologyItaly
  3. 3.DIMORFIPAItaly
  4. 4.Physics Department and INFNUniversity of BolognaItaly

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