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Plausible Neural Networks for Biological Modelling

  • Book
  • © 2001

Overview

Editors:
  1. Henk A. K. Mastebroek

    1. Department of Neurobiophysics and Biomedical Engineering, Physics Lab., University of Groningen, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

  2. Johan E. Vos

    1. Department of Developmental Neurology, Medical Physiology, University of Groningen, The Netherlands

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Mathematical Modelling: Theory and Applications (MMTA, volume 13)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-6
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  2. Biological Evidence for Synapse Modification, Relevant for Neural Network Modelling

    • J. E. Vos
    Pages 7-21

  3. What is Different with Spiking Neurons?

    • Wulfram Gerstner
    Pages 23-48

  4. Recurrent Neural Networks: Properties and Models

    • Jean-Philippe Draye
    Pages 49-74

  5. A Derivation of Learning Rules for Dynamic Recurrent Neural Networks

    • Henk A. K. Mastebroek
    Pages 75-89

  6. Simulation of the Human Oculomotor Integrator Using a Dynamic Recurrent Neural Network

    • Jean-Philippe Draye, Guy Cheron
    Pages 91-115

  7. Pattern Segmentation in an Associative Network of Spiking Neurons

    • Raphael Ritz
    Pages 117-133

  8. Cortical Models for Movement Control

    • Daniel Bullock
    Pages 135-162

  9. Implications of Activity Dependent Processes in Spinal Cord Circuits for the Development of Motor Control; a Neural Network Model

    • J. J. van Heijst, J.E. Vos
    Pages 163-187

  10. Cortical Maps as Topology-Representing Neural Networks Applied to Motor Control:

    • Pietro Morasso, Vittorio Sanguineti, Francesco Frisone
    Pages 189-218

  11. Line and Edge Detection by Curvature-Adaptive Neural Networks

    • Jacobus H. van Deemter, Johannes M. H. du Buf
    Pages 219-239

  12. Path Planning and Obstacle Avoidance using a Recurrent Neural Network

    • Erwin Mulder, Henk A.K. Mastebroek
    Pages 241-253

  13. Back Matter

    Pages 255-261
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Keywords

About this book

The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub­ systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies).

Editors and Affiliations

  • Department of Neurobiophysics and Biomedical Engineering, Physics Lab., University of Groningen, The Netherlands

    Henk A. K. Mastebroek

  • Department of Developmental Neurology, Medical Physiology, University of Groningen, The Netherlands

    Johan E. Vos

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