Extended Random Neural Networks

  • G. Martinelli
  • F. M. Frattale Mascioli
  • M. Panella
  • A. Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)


Random neural networks mimic at a very deep level the biological nervous system. However, it is difficult to meet during learning the biological constraints imposed on their parameters. In the paper two possible extensions are proposed in order to remove this difficulty. Moreover, the proposed learning algorithm is tailored to the specific architecture in order to reduce the computational cost. Two architectures are considered and illustrated by simulation tests.


Bimodal neuron Recurrent architecture 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • G. Martinelli
    • 1
  • F. M. Frattale Mascioli
    • 1
  • M. Panella
    • 1
  • A. Rizzi
    • 1
  1. 1.INFO-COM Dpt.University of Rome ”La Sapienza”RomeItaly

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