Competitive Repetition-suppression (CoRe) Learning

  • Davide Bacciu
  • Antonina Starita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


The paper introduces Competitive Repetition-suppression (CoRe) learning, a novel paradigm inspired by a cortical mechanism of perceptual learning called repetition suppression. CoRe learning is an unsupervised, soft-competitive [1] model with conscience [2] that can be used for self-generating compact neural representations of the input stimuli. The key idea underlying the development of CoRe learning is to exploit the temporal distribution of neurons activations as a source of training information and to drive memory formation. As a case study, the paper reports the CoRe learning rules that have been derived for the unsupervised training of a Radial Basis Function network.


Input Pattern Perceptual Learning Radial Basis Function Network Input Stimulus Competitive Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Davide Bacciu
    • 1
    • 2
  • Antonina Starita
    • 2
  1. 1.IMT Lucca School for Advanced StudiesLuccaItaly
  2. 2.Dipartimento d’InformaticaUniversità degli Studi di PisaPisaItaly

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