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Selfreparing Neural Networks: A Model for Recovery from Brain Damage

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

We introduce selfrepairing neural networks as a model for recovery from brain damage. Small lesions are repaired through reinstatement of the redundancy in the network’s connections. With mild lesions, this process can model autonomous recovery. Moderate lesions require patterned input. In this paper, we discuss implementations in three types of network of increasing biological plausibility. We also mention some results from random graph theory. Finally, we discuss the implications for rehabilitation theory.

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Author information

Authors and Affiliations

  1. University of Amsterdam, Roetersstraat 15, 1018 WB, Amsterdam, The Netherlands

    Jaap M. J. Murre & Robert Griffioen

  2. University of Maastricht, The Netherlands

    Jaap M. J. Murre

  3. Trinity College, Ireland

    I. H. Robertson

Authors
  1. Jaap M. J. Murre
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  2. Robert Griffioen
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  3. I. H. Robertson
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Murre, J.M.J., Griffioen, R., Robertson, I.H. (2003). Selfreparing Neural Networks: A Model for Recovery from Brain Damage. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_158

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_158

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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