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|a Concepts for Neural Networks
|h [electronic resource] :
|b A Survey /
|c edited by Lawrence J. Landau.
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|a 1st ed. 1998.
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|a London :
|b Springer London :
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|c 1998.
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|a Perspectives in Neural Computing,
|x 1431-6854
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|a 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 -- 2.3 Neural Networks as Associative Memories -- 2.4 Creating Maps of the Outside World -- 2.5 Learning a Rule from an Expert -- 2.6 Puzzling Mathematics -- 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 -- 4.4 A Compound 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 -- 5.4 Connectionist Implementation -- 5.5 An Experiment with a Robot -- 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 -- 9.7 Awareness of Words -- 9.8 Activity ‘Bubbles’ in Cortex -- 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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|a 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.
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|a Loaded electronically.
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|a Electronic access restricted to members of the Holy Cross Community.
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|a Artificial intelligence.
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|a Computational complexity.
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