A Connectionist Model of Reading with Error Correction Properties

  • Max Raphael Sobroza MarquesEmail author
  • Xiaoran Jiang
  • Olivier Dufor
  • Claude Berrou
  • Deok-Hee Kim-Dufor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10930)


Recent models of associative long term memory (LTM) have emerged in the field of neuro-inspired computing. These models have interesting properties of error correction, robustness, storage capacity and retrieval performance. In this context, we propose a connectionist model of written word recognition with correction properties, using associative memories based on neural cliques. Similarly to what occurs in human language, the model takes advantage of the combination of phonological and orthographic information to increase the retrieval performance in error cases. Therefore, the proposed architecture and principles of this work could be applied to other neuro-inspired problems that involve multimodal processing, in particular for language applications.


Connectionist model Reading model Word error correction Associative memory Multimodal neural network 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Max Raphael Sobroza Marques
    • 1
    Email author
  • Xiaoran Jiang
    • 2
  • Olivier Dufor
    • 1
  • Claude Berrou
    • 1
  • Deok-Hee Kim-Dufor
    • 1
  1. 1.Institut Mines Télécom Atlantique, Département d’ElectroniqueTechnopôle Brest-IroiseBrestFrance
  2. 2.Inria Rennes Bretagne AtlantiqueRennesFrance

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