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Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience

  • Roberto Santana
  • Concha Bielza
  • Pedro Larrañaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6098)

Abstract

Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used.

Keywords

Betweenness Centrality Probabilistic Graphical Model Abductive Inference High Betweenness Centrality Network Descriptor 
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 2010

Authors and Affiliations

  • Roberto Santana
    • 1
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del Monte, MadridSpain

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