Plausible Neural Networks for Biological Modelling

1. Front Matter

   Pages i-6

   PDF

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

    PDF

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).

• Nervous System
• algorithms
• biology
• neurons
• simulation
• spiking neurons
• topology

• 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

Policies and ethics