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Emergent Neural Computational Architectures Based on Neuroscience

Towards Neuroscience-Inspired Computing

  • Book
  • © 2001

Overview

Editors:
  1. Stefan Wermter

    1. Centre of Informatics, SCET, University of Sunderland, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. Jim Austin

    1. Department of Computer Science, University of York, York, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. David Willshaw

    1. Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK

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    You can also search for this editor in PubMed   Google Scholar

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (40 chapters)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. Towards Novel Neuroscience-Inspired Computing

    1. Towards Novel Neuroscience-Inspired Computing

      • Stefan Wermter, Mark Elshaw, Jim Austin, David Willshaw
      Pages 1-19

  3. Modular Organisation and Robustness

    1. Images of the Mind: Brain Images and Neural Networks

      • John G. Taylor
      Pages 20-38

    2. Stimulus-Independent Data Analysis for fMRI

      • Silke Dodel, J. Michael Herrmann, Theo Geisel
      Pages 39-52

    3. Emergence of Modularity within One Sheet of Neurons: A Model Comparison

      • Cornelius Weber, Klaus Obermayer
      Pages 53-67

    4. Computational Investigation of Hemispheric Specialization and Interactions

      • James A. Reggia, Yuri Shkuro, Natalia Shevtsova
      Pages 68-82

    5. Explorations of the Interaction between Split Processing and Stimulus Types

      • John Hicks, Padraic Monaghan
      Pages 83-97

    6. Modularity and Specialized Learning: Mapping between Agent Architectures and Brain Organization

      • Joanna Bryson, Lynn Andrea Stein
      Pages 98-113

    7. Biased Competition Mechanisms for Visual Attention in a Multimodular Neurodynamical System

      • Gustavo Deco
      Pages 114-126

    8. Recurrent Long-Range Interactions in Early Vision

      • Thorsten Hansen, Wolfgang Sepp, Heiko Neumann
      Pages 127-138

    9. Neural Mechanisms for Representing Surface and Contour Features

      • Thorsten Hansen, Heiko Neumann
      Pages 139-153

    10. Representations of Neuronal Models Using Minimal and Bilinear Realisations

      • Gary G. R. Green, Will Woods, S. Manchanda
      Pages 154-160

    11. Collaborative Cell Assemblies: Building Blocks of Cortical Computation

      • Ronan G. Reilly
      Pages 161-173

    12. On the Influence of Threshold Variability in a Mean-Field Model of the Visual Cortex

      • Hauke Bartsch, Martin Stetter, Klaus Obermayer
      Pages 174-187

    13. Towards Computational Neural Systems through Developmental Evolution

      • Alistair G. Rust, Rod Adams, Stella George, Hamid Bolouri
      Pages 188-202

    14. The Complexity of the Brain: Structural, Functional, and Dynamic Modules

      • Péter Érdi, Tamás Kiss
      Pages 203-211
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Keywords

About this book

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.

Editors and Affiliations

  • Centre of Informatics, SCET, University of Sunderland, UK

    Stefan Wermter

  • Department of Computer Science, University of York, York, UK

    Jim Austin

  • Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK

    David Willshaw

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