Emergent Neural Computational Architectures Based on Neuroscience

1. Front Matter

   Pages I-X

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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

4. Timing and Synchronisation

   1. Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission

      • Simon R. Schultz, Huw D. R. Golledge, Stefano Panzeri

      Pages 212-226

   2. Segmenting State into Entities and Its Implication for Learning

      • James Henderson

      Pages 227-236

   3. Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain

      • Roman Borisyuk, Galina Borisyuk, Yakov Kazanovich

      Pages 237-254

   4. Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle

      • Guido Bugmann

      Pages 255-269

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.

• Nervous System
• algorithms
• autonomous robot
• biologically inspired
• classification
• cognition
• complexity
• information processing
• knowledge
• learning
• memory
• neuroscience
• performance
• robot
• robotics
• algorithm analysis and problem complexity

• 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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