<電子ブック>
Models of Neural Networks III : Association, Generalization, and Representation

責任表示
著者
本文言語
出版者
出版年
出版地
関連情報
概要 One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many prac...tical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.続きを見る
目次 1. Global Analysis of Recurrent Neural Networks
1.1 Global Analysis-Why?
1.2 A Framework for Neural Dynamics
1.3 Fixed Points
1.4 Periodic Limit Cycles and Beyond
1.5 Synchronization of Action Potentials
1.6 Conclusions
References
2. Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns
2.1 Introduction
2.2 Correlation-Based Models
2.3 The Problem of Map Structure
2.4 The Computational Significance of Correlatin-Based Rules
2.5 Open Questions
References
3. Associative Data Storage and Retrieval in Neural Networks
3.1 Introduction and Overview
3.1.1 Memory and Representation
3.2 Neural Associatve Memory Models
3.3 Analysis of the Retrieval Process
3.4 Information Theory of the Memory Process
3.5 Model Performance
3.6 Discussion
Appendix 3.1
Appendix 3.2
References
4. Inferences Modeled with Neural Networks
4.1 Introduction
4.2 Model for Cognitive Systems and for Experiences
4.3 Inductive Inference
4.4 External Memory
4.5 Limited Use of External Memory
4.6 Deductive Inference
4.7 Conclusion
References
5. Statistical Mechanics of Generalization
5.1 Introduction
5.2 General Results
5.3 The Perceptron
5.4 Geometry in Phase Space and Asymptotic Scaling
5.5 Applications to Perceptrons
5.6 Summary and Outlook
Appendix 5.1: Proof of Sauer's Lemma
Appendix 5.2: Order Parameters for ADALINE
References
6. Bayesian Methods for Backpropagation Networks
6.1 Probability Theory and Occam's Razor
6.2 Neural Networks as Probabilistic Models
6.3 Setting Regularization Constants ? and ?
6.4 Model Comparison
6.5 Error Bars and Predictions
6.6 Pruning
6.7 Automatic Relevance Determination
6.8 Implicit Priors
6.9 Cheap and Cheerful Implementations
6.10 Discussion
References
7. Penacée: A Neural Net System for Recognizing On-Line Handwriting
7.1 Introduction
7.2 Description of the Building Blocks
7.3 Applications
7.4 Conclusion
References
8. Topology Representing Network in Robotics
8.1 Introduction
8.2 Problem Description
8.3 Topology Representing Network Algorithm
8.4 Experimental Results and Discussion
References.
続きを見る
本文を見る Full text available from SpringerLink ebooks - Physics and Astronomy (Archive)

詳細

レコードID
主題
SSID
eISBN
登録日 2020.06.27
更新日 2020.06.28