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Models of Neural Networks I

This collection of articles responds to the urgent need for timely and comprehensive reviews in a multidisciplinary, rapidly developing field of research. The book starts out with an extensive introduction to the ideas used in the subsequent chapters, which are all centered around the theme of colle...

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Bibliographic Details
Other Authors: Domany, Eytan (Editor), Hemmen, J.Leo van (Editor), Schulten, Klaus (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 1995, 1995
Edition:2nd ed. 1995
Series:Physics of Neural Networks
Subjects:
Neuroscience
Complex Systems
Neurosciences
System Theory
Mathematical Physics
Biophysics
Automated Pattern Recognition
Theoretical, Mathematical And Computational Physics
Pattern Recognition Systems
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 7.5 Applications: Sequence Recognition, Counting, and the Generation of Complex Sequences
  • 7.6 Hebbian Learning with Delays
  • 7.7 Epilogue
  • References
  • 8. Self-organizing Maps and Adaptive Filters
  • 8.1 Introduction
  • 8.2 Self-organizing Maps and Optimal Representation of Data
  • 8.3 Learning Dynamics in the Vicinity of a Stationary State
  • 8.4 Relation to Brain Modeling
  • 8.5 Formation of a “Somatotopic Map”
  • 8.6 Adaptive Orientation and Spatial Frequency Filters
  • 8.7 Conclusion
  • References
  • 9. Layered Neural Networks
  • 9.1 Introduction
  • 9.2 Dynamics of Feed-Forward Networks
  • 9.3 Unsupervised Learning in Layered Networks
  • 9.4 Supervised Learning in Layered Networks
  • 9.5 Summary and Discussion
  • References
  • Elizabeth Gardner-An Appreciation
  • 4.2 Definition of Supervised Learning
  • 4.3 Adaline Learning
  • 4.4 Perceptron Learning
  • 4.5 Binary Synapses
  • 4.6 Basins of Attraction
  • 4.7 Forgetting
  • 4.8 Outlook
  • References
  • 5. Hierarchical Organization of Memory
  • 5.1 Introduction
  • 5.2 Models: The Problem
  • 5.3 A Toy Problem: Patterns with Low Activity
  • 5.4 Models with Hierarchically Structured Information
  • 5.5 Extensions
  • 5.6 The Enhancement of Storage Capacity: Multineuron Interactions
  • 5.7 Conclusion
  • References
  • 6. Asymmetrically Diluted Neural Networks
  • 6.1 Introduction
  • 6.2 Solvability and Retrieval Properties
  • 6.3 Exact Solution with Dynamic Functionals
  • 6.4 Extensions and Related Work
  • Appendix A
  • Appendix B
  • Appendix C
  • References
  • 7. Temporal Association
  • 7.1 Introduction
  • 7.2 Fast Synaptic Plasticity
  • 7.3 Noise-Driven Sequences of Biased Patterns
  • 7.4 Stabilizing Sequences by Delays
  • 1. Collective Phenomena in Neural Networks
  • 1.1 Introduction and Overview
  • 1.2 Prerequisites
  • 1.3 The Hopfield Model
  • 1.4 Nonlinear Neural Networks
  • 1.5 Learning, Unlearning, and Forgetting
  • 1.6 Hierarchically Structured Information
  • 1.7 Outlook
  • References
  • 2. Information from Structure: A Sketch of Neuroanatomy
  • 2.1 Development of the Brain
  • 2.2 Neuroanatomy Related to Information Handling in the Brain
  • 2.3 The Idea of Electronic Circuitry
  • 2.4 The Projection from the Compound Eye onto the First Ganglion (Lamina) of the Fly
  • 2.5 Statistical Wiring
  • 2.6 Symmetry of Neural Nets
  • 2.7 The Cerebellum
  • 2.8 Variations in Size of the Elements
  • 2.9 The Cerebral Cortex
  • 2.10 Inborn Knowledge
  • References
  • 3. Storage Capacity and Learning in Ising-Spin Neural Networks
  • 3.1 Introduction
  • 3.2 Content-addressability: A Dynamics Problem
  • 3.3 Learning
  • 3.4 Discussion
  • References
  • 4. Dynamics of Learning
  • 4.1 Introduction

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