Plausible Neural Networks for Biological Modelling

Plausible Neural Networks for Biological Modelling

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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).
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Product details

  • Hardback | 262 pages
  • 160.02 x 241.3 x 22.86mm | 544.31g
  • Dordrecht, Netherlands
  • English
  • 2001 ed.
  • IX, 262 p.
  • 0792371925
  • 9780792371922

Table of contents

Preface. Part I: Fundamentals. 1. Biological Evidence for Synapse Modification Relevant for Neural Network Modelling; J.E. Vos. 2. What is Different with Spiking Neurons; W. Gerstner. 3. Recurrent Neural Networks: Properties and Models; J.-P. Draye. 4. A Derivation of the Learning Rules for Dynamic Recurrent Neural Networks; H.A.K. Mastebroek. Part II: Applications to Biology. 5. Simulation of the Human Oculomotor Integrator Using a Dynamic Recurrent Neural Network; J.-P. Draye, G. Cheron. 6. Pattern Segmentation in an Associative Network of Spiking Neurons; R. Ritz. 7. Cortical Models for Movement Control; D. Bullock. 8. 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. 9. Cortical Maps as Topology-Representing Neural Networks Applied to Motor Control: Articulatory Speech Synthesis; P. Morasso, et al. 10. Line and Edge Detection by Curvature-Adaptive Neural Networks; J.H. van Deemter, J.M.H. du Buf. 11. Path Planning and Obstacle Avoidance Using a Recurrent Neural Network; E. Mulder, H.A.K. Mastebroek. Index.
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