Priming an artificial associative memory

  • Cédric Bertolini
  • Hélène Paugam-Moisy
  • Didier Puzenat
Neural Modeling (Biophysical and Structural Models)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)


This article presents a method enabling the simulation of a well known psychological phenomenon: the “repetition priming”. The artificial neural network model used is a Hopfield network. This primed associative memory is one of the basic models that, used with other primed neural models, will permit to simulate more complex cognitive processes, notably memorization processes, recognition and identification. The priming method is validated by a set of experiments. The phenomenon, which can be facilitator—with or without interposed items—or inhibitor, can be detected and measured.


Priming Cascaded processing Associative memory Artificial neural networks Connectionism 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Cédric Bertolini
    • 1
  • Hélène Paugam-Moisy
    • 1
  • Didier Puzenat
    • 1
  1. 1.ERIC laboratoryUniversity Lumière Lyon 2France

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