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Emergent Neural Computational Architectures Based on Neuroscience

Towards Neuroscience-Inspired Computing

  • Stefan Wermter
  • Jim Austin
  • David Willshaw

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2036)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2036)

Table of contents

  1. Front Matter
    Pages I-X
  2. Towards Novel Neuroscience-Inspired Computing

    1. Stefan Wermter, Mark Elshaw, Jim Austin, David Willshaw
      Pages 1-19
  3. Modular Organisation and Robustness

    1. Silke Dodel, J. Michael Herrmann, Theo Geisel
      Pages 39-52
    2. James A. Reggia, Yuri Shkuro, Natalia Shevtsova
      Pages 68-82
    3. Thorsten Hansen, Wolfgang Sepp, Heiko Neumann
      Pages 127-138
    4. Thorsten Hansen, Heiko Neumann
      Pages 139-153
    5. Gary G. R. Green, Will Woods, S. Manchanda
      Pages 154-160
    6. Hauke Bartsch, Martin Stetter, Klaus Obermayer
      Pages 174-187
    7. Alistair G. Rust, Rod Adams, Stella George, Hamid Bolouri
      Pages 188-202
  4. Timing and Synchronisation

    1. Simon R. Schultz, Huw D. R. Golledge, Stefano Panzeri
      Pages 212-226
    2. Roman Borisyuk, Galina Borisyuk, Yakov Kazanovich
      Pages 237-254
    3. David C. Sterratt#x22C6;
      Pages 270-284
    4. Péter Andr#x00E1s
      Pages 296-310
    5. Ramin Assadollahi, Friedemann Pulvermüller
      Pages 311-319
    6. Stefano Panzeri, Edmund T. Rolls, Francesco P. Battaglia, Ruth Lavis
      Pages 320-332
  5. Learning and Memory Storage

    1. Rafal Bogacz, Christophe Giraud-Carrier, Malcolm W. Brown
      Pages 428-441
    2. Tim Pearce, Paul Verschure, Joel White, John Kauer
      Pages 461-479
    3. Doina Caragea, Adrian Silvescu, Vasant Honavar
      Pages 547-559
    4. Stephen José Hanson, Michiro Negishi, Catherine Hanson
      Pages 560-576
  6. Back Matter
    Pages 577-577

About this book

Introduction

It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.

Keywords

Nervous System algorithms autonomous robot biologically inspired classification cognition complexity information processing knowledge learning memory neuroscience performance robot robotics

Editors and affiliations

  • Stefan Wermter
    • 1
  • Jim Austin
    • 2
  • David Willshaw
    • 3
  1. 1.Centre of Informatics, SCETUniversity of SunderlandUK
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK
  3. 3.Institute for Adaptive and Neural ComputationUniversity of EdinburghEdinburghUK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-44597-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42363-8
  • Online ISBN 978-3-540-44597-5
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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