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Recruitment Learning

  • Joachim Diederich
  • Cengiz Günay
  • James M. Hogan

Part of the Studies in Computational Intelligence book series (SCI, volume 303)

Table of contents

  1. Front Matter
  2. Recruitment in Discrete-time Neural Networks

    1. Front Matter
      Pages 1-1
    2. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 3-36
    3. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 37-56
    4. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 57-81
    5. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 83-135
    6. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 137-179
  3. Recruitment in Continuous-time Neural Networks

    1. Front Matter
      Pages 181-181
    2. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 183-198
    3. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 199-242
    4. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 243-274
    5. Joachim Diederich, Cengiz Günay, James M. Hogan
      Pages 275-281
  4. Back Matter

About this book

Introduction

This book presents a fascinating and self-contained account of "recruitment learning", a model and theory of fast learning in the neocortex. In contrast to the more common attractor network paradigm for long- and short-term memory, recruitment learning focuses on one-shot learning or "chunking" of arbitrary feature conjunctions that co-occur in single presentations. The book starts with a comprehensive review of the historic background of recruitment learning, putting special emphasis on the ground-breaking work of D.O. Hebb, W.A.Wickelgren, J.A.Feldman, L.G.Valiant, and L. Shastri. Afterwards a thorough mathematical analysis of the model is presented which shows that recruitment is indeed a plausible mechanism of memory formation in the neocortex. A third part extends the main concepts towards state-of-the-art spiking neuron models and dynamic synchronization as a tentative solution of the binding problem. The book further discusses the possible role of adult neurogenesis for recruitment. These recent developments put the theory of recruitment learning at the forefront of research on biologically inspired memory models and make the book an important and timely contribution to the field.

Keywords

Computational Intelligence Recruitment Learning

Authors and affiliations

  • Joachim Diederich
    • 1
  • Cengiz Günay
    • 2
  • James M. Hogan
    • 3
  1. 1.School of Information Technology and Electrical Engineering, School of Medicine - Central Clinical DivisionThe University of QueenslandBrisbaneAustralia
  2. 2.Department of BiologyEmory UniversityAtlantaU.S.A.
  3. 3.School of Software Engineering and Data CommunicationsQueensland University of TechnologyBrisbaneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-14028-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-14027-3
  • Online ISBN 978-3-642-14028-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site