Boolean networks which learn to compute

  • Stefano Patarnello
  • Paolo Carnevali
Conference paper
Part of the Lecture Notes in Physics book series (LNP, volume 314)


Through a training procedure based on simulated annealing, Boolean networks can ‘learn’ to perform specific tasks. As an example, a network implementing a binary adder has been obtained after a training procedure based on a small number of examples of binary addition, thus showing a generalization capability. Depending on problem complexity, network size, and number of examples used in the training, different learning regimes occur. For small networks an exact analysis of the statistical mechanics of the system shows that learning takes place as a phase transition. The ‘simplicity’ of a problem can be related to its entropy. Simple problems are those that are thermodynamically favored.


Simulated Annealing Boolean Function Training Procedure Boolean Network Generalization Capability 
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 1988

Authors and Affiliations

  • Stefano Patarnello
    • 1
  • Paolo Carnevali
    • 1
  1. 1.IBM ECSECRomeItaly

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