Concepts for Neural Networks: A Survey

Concepts for Neural Networks: A Survey

by Lawrence J. Landau (Editor)

Paperback(1st Edition.)

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Concepts for Neural Networks - A Survey provides a wide-ranging survey of concepts relating to the study of neural networks. It includes chapters explaining the basics of both artificial neural networks and the mathematics of neural networks, as well as chapters covering the more philosophical background to the topic and consciousness. There is also significant emphasis on the practical use of the techniques described in the area of robotics. Containing contributions from some of the world's leading specialists in their fields (including Dr. Ton Coolen and Professor Igor Aleksander), this volume will provide the reader with a good, general introduction to the basic concepts needed to understan d and use neural network technology.

Product Details

ISBN-13: 9783540761631
Publisher: Springer London
Publication date: 12/04/1997
Series: Perspectives in Neural Computing
Edition description: 1st Edition.
Pages: 312
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Neural Networks: An Overview.- 1.1 Introduction.- 1.2 What is a Neural Network?.- 1.3 Neural Network Dynamics.- 1.4 Training a Neural Network.- 1.5 Problems for Artificial Neural Networks.- 1.6 Problems for Neurobiologically Realistic Neural Networks.- 1.7 Conclusions.- 1.8 Further Reading.- 1.9 Bibliography.- 2 A Beginner’s Guide to the Mathematics of Neural Networks.- 2.1 Introduction: Neural Information Processing.- 2.2 From Biology to Mathematical Models.- 2.2.1 From Biological Neurons to Model Neurons.- 2.2.2 Universality of Model Neurons.- 2.2.3 Directions and Strategies.- 2.3 Neural Networks as Associative Memories.- 2.3.1 Recipes for Storing Patterns and Pattern Sequences.- 2.3.2 Symmetric Networks: The Energy Picture.- 2.3.3 Solving Models of Noisy Attractor Networks.- 2.4 Creating Maps of the Outside World.- 2.4.1 Map Formation through Competitive Learning.- 2.4.2 Solving Models of Map Formation.- 2.5 Learning a Rule from an Expert.- 2.5.1 Perceptrons.- 2.5.2 Multi-Layer Networks.- 2.5.3 Calculating what is Achievable.- 2.5.4 Solving the Dynamics of Learning for Perceptrons.- 2.6 Puzzling Mathematics.- 2.6.1 Complexity due to Frustration, Disorder and Plasticity.- 2.6.2 The World of Replica Theory.- 2.7 Further Reading.- 2.8 Bibliography.- 3 Neurobiological Modelling.- 3.1 Introduction.- 3.2 The Single Nerve Cell.- 3.3 Retinal Processing.- 3.4 The Cortical Streams.- 3.5 The Ventral Stream.- 3.6 The Dorsal Stream.- 3.7 Object Construction.- 3.8 Modelling the Processing Streams.- 3.9 Conclusions.- 3.10 Bibliography.- 4 Neural Network Control of a Simple Mobile Robot.- 4.1 Introduction.- 4.2 The Robot ‘Insects’.- 4.3 Neural Network for Obstacle Avoidance.- 4.3.1 Learning Strategy.- 4.3.2 Results.- 4.4 A Compound Eye.- 4.4.1 The First Compound Eye.- 4.4.2 Estimating Robot Position.- 4.4.3 Weightless Networks.- 4.4.4 Multi Discriminator Network.- 4.4.5 Processing Grey Level Data.- 4.4.6 Weightless Networks and the First Eye.- 4.4.7 Results.- 4.4.8 The Second Eye.- 4.5 Navigation.- 4.6 Discussion.- 4.7 Bibliography.- 5 A Connectionist Approach to Spatial Memory and Planning.- 5.1 Overview.- 5.2 Introduction.- 5.3 Biological Spatial Memory.- 5.3.1 Stimulus-Response Theory.- 5.3.2 Cognitive Maps.- 5.3.3 Topological Network-Maps.- 5.3.4 Planning.- 5.3.5 Summary.- 5.4 Connectionist Implementation.- 5.4.1 Principles of a Network-Map Based Artificial Spatial Memory.- 5.4.2 Sparse Versus Distributed Representation of Views.- 5.4.3 Implementation of a Sparse Model.- 5.4.4 A Constructive Learning Procedure for States and Actions.- 5.4.5 Planning and Search Procedure.- 5.5 An Experiment with a Robot.- 5.5.1 Task and Experimental Set-up.- 5.5.2 Learning and Planning Test.- 5.6 Discussion.- 5.7 Conclusion.- 5.8 Appendix: Vision system for letter detection and recognition..- 5.9 Appendix: Vision system for state recognition.- 5.10 Appendix: Notes on a biologically plausible implementation.- 5.11 Bibliography.- 6 Turing’s Philosophical Error?.- 6.1 Turing’s Machine.- 6.2 Digital Problems.- 6.3 Turing’s Thesis.- 6.4 How Turing Built his Universal Machine.- 6.5 Gödel’s Attempt to Break Turing’s Thesis.- 6.6 Yes-or-No Questions.- 6.7 Material and Formal Proofs.- 6.8 The Dialectica Lecture.- 6.9 A Calculus of Concepts?.- 6.10 Turing’s Error.- 6.11 Further Reading.- 6.12 Bibliography.- 7 Penrose’s Philosophical Error.- 7.1 Can Computers Think?.- 7.2 Solving Problems in Arithmetic.- 7.3 The Barber Paradox.- 7.4 Gödel’s Theorem.- 7.5 Penrose.- 7.6 A Computational Model for Thought?.- 7.7 Can Computers Think?.- 7.8 Appendix: Filling in Some Details.- 7.9 Bibliography.- 8 Attentional Modulation in Visual Pathways.- 8.1 Introduction.- 8.2 Structural Equation Modelling.- 8.3 Design and Image Acquisition.- 8.4 Image Analysis and Categorical Comparisons.- 8.5 Modelling of the Posterior Visual Pathway.- 8.6 Modelling Modulation by Interaction Terms.- 8.7 Interaction Effects Demonstrated with Regression Analysis.- 8.8 Regional Specificity.- 8.9 Conclusion.- 8.10 Bibliography.- 9 Neural Networks and the Mind.- 9.1 Introduction.- 9.2 The Relational Mind.- 9.3 Exploring the Relational Mind Model.- 9.4 Memory in the Relational Mind Model.- 9.5 A Neural Candidate for Global Control.- 9.6 Consciousness and Sites of Working Memory.- 9.6.1 Psychological Bases for Working Memory.- 9.6.2 PET Studies of Working Memory.- 9.7 Awareness of Words.- 9.8 Activity ‘Bubbles’ in Cortex.- 9.8.1 General Discussion.- 9.8.2 Technical Results.- 9.9 The Emergence of Qualia.- 9.10 Discussion.- 9.11 Bibliography.- 10 Confusions about Consciousness.- 10.1 Consciousness Studies.- 10.2 Bibliography.- 11 Round Table Discussion.- 11.1 Presentations.- 11.2 Audience Participation.

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