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Plausible Neural Networks for Biological Modelling (Mathematical Modelling
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This book has the unique intention of returning the mathematical tools of neural networks to the biological realm of the nervous system, where they originated a few decades ago. It aims to introduce, in a didactic manner, two relatively recent developments in neural network methodology, namely recurrence in the architecture and the use of spiking or integrate-and-fire neurons. In addition, the neuro-anatomical processes of synapse modification during development, training, and memory formation are discussed as realistic bases for weight-adjustment in neural networks. While neural networks have many applications outside biology, where it is irrelevant precisely which architecture and which algorithms are used, it is essential that there is a close relationship between the network's properties and whatever is the case in a neuro-biological phenomenon that is being modelled or simulated in terms of a neural network.A recurrent architecture, the use of spiking neurons and appropriate weight update rules contribute to the plausibility of a neural network in such a case. Therefore, in the first half of this book the foundations are laid for the application of neural networks as models for the various biological phenomena that are treated in the second half of this book. These include various neural network models of sensory and motor control tasks that implement one or several of the requirements for biological plausibility.
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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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