Self-organization and associative memory:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
1988
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Springer series in information sciences
8 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 312 S. graph. Darst. |
| ISBN: | 3540183140 0387183140 |
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Datensatz im Suchindex
| _version_ | 1804135982572765184 |
|---|---|
| adam_text | Teuvo Kohonen
Self-Organization and
Associative Memory
Second Edition
With 99 Figures
Springer-Verlag Berlin Heidelberg New York
London Paris Tokyo
Contents
1 Various Aspects of Memory 1
1 1 On the Purpose and Nature of Biological Memory 1
111 Some Fundamental Concepts 1
112 The Classical Laws of Association 3
113 On Different Levels of Modelling 4
1 2 Questions Concerning the Fundamental Mechanisms of Memory 4
121 Where Do the Signals Relating to Memory Act Upon? 5
122 What Kind of Encoding is Used for Neural Signals? 6
123 What are the Variable Memory Elements? 7
124 How are Neural Signals Addressed in Memory? 8
1 3 Elementary Operations Implemented by Associative Memory 14
131 Associative Recall 14
132 Production of Sequences from the Associative Memory 16
133 On the Meaning of Background and Context 20
1 4 More Abstract Aspects of Memory 21
141 The Problem of Infinite-State Memory 21
142 Invariant Representations 22
143 Symbolic Representations 24
144 Virtual Images 25
145 The Logic of Stored Knowledge 27
2 Pattern Mathematics 30
2 1 Mathematical Notations and Methods 30
211 Vector Space Concepts 30
212 Matrix Notations 41
213 Further Properties of Matrices 44
214 Matrix Equations 48
215 Projection Operators 54
216 On Matrix Differential Calculus 57
2 2 Distance Measures for Patterns 59
XII Contents
221 Measures of Similarity and Distance in Vector Spaces 59
222 Measures of Similarity and Distance Between Symbol
Strings 63
223 More Accurate Distance Measures for Text 66
3 Classical Learning Systems 68
3 1 The Adaptive Linear Element (Adaline) 69
311 Description of Adaptation by the Stochastic
Approximation 71
3 2 ThePerceptron 72
3 3 The Learning Matrix 74
3 4 Physical Realization of Adaptive Weights 77
341 Perceptron and Adaline 77
342 Classical Conditioning 78
343 Conjunction Learning Switches 80
344 Digital Representation of Adaptive Circuits 80
345 Biological Components 81
4 A New Approach to Adaptive Filters 82
4 1 Survey of Some Necessary Functions 82
4 2 On the Transfer Function of the Neuron 84
4 3 Models for Basic Adaptive Units 90
431 On the Linearization of the Basic Unit 90
432 Various Cases of Adaptation Laws 91
433 Two Limit Theorems 98
434 The Novelty Detector 101
4 4 Adaptive Feedback Networks 104
441 The Autocorrelation Matrix Memory 105
442 The Novelty Filter 109
5 Self-Organizing Feature Maps 119
5 1 On the Feature Maps of the Brain 119
5 2 Formation of Localized Responses by Lateral Feedback 122
5 3 Computational Simplification of the Process 127
531 Definition of the Topology-Preserving Mapping 127
532A Simple Two-Dimensional Self-Organizing System 130
5 4 Demonstrations of Simple Topology-Preserving Mappings 133
541 Images of Various Distributions of Input Vectors 133
542 The Magic TV 137
543 Mapping by a Feeler Mechanism 139
5 5 TonotopicMap 140
Contents XIII
5 6 Formation of Hierarchical Representations 141
561 Taxonomy Example 141
562 Phoneme Map 142
5 7 Mathematical Treatment of Self-Organization 143
571 Ordering of Weights 144
572 Convergence Phase 150
5 8 Automatic Selection of Feature Dimensions 155
6 Optimal Associative Mappings 158
6 1 Transfer Function of an Associative Network 159
6 2 Autoassociative Recall as an Orthogonal Projection 160
621 Orthogonal Projections 160
622 Error-Correcting Properties of Projections 161
6 3 The Novelty Filter 163
631 Two Examples of Novelty Filter 163
632 Novelty Filter as an Autoassociative Memory 165
6 4 Autoassociative Encoding 165
641 An Example of Autoassociative Encoding 166
6 5 Optimal Associative Mappings 167
651 The Optimal Linear Associative Mapping 168
652 Optimal Nonlinear Associative Mappings 172
6 6 Relationship Between Associative Mapping, Linear Regression,
and Linear Estimation 175
661 Relationship of the Associative Mapping to Linear
Regression 175
662 Relationship of the Regression Solution to the Linear
Estimator 176
6 7 Recursive Computation of the Optimal Associative Mapping 177
671 Linear Corrective Algorithms 178
672 Best Exact Solution (Gradient Projection) 179
673 Best Approximate Solution (Regression) 180
674 Recursive Solution in the General Case 182
6 8 Special Cases 183
681 The Correlation Matrix Memory 183
682 Relationship Between Conditional Averages and Optimal
Estimator 184
7 Pattern Recognition 185
7 1 Discriminant Functions 185
7 2 Statistical Formulation of Pattern Classification 187
7 3 Comparison Methods 190
XIV Contents
7 4 The Subspace Methods of Classification 192
741 The Basic Subspace Method 192
742 The Learning Subspace Method (LSM) 193
7 5 Learning Vector Quantization 199
7 6 Feature Extraction 202
7 7 Clustering 203
771 Simple Clustering (Optimization Approach) 204
772 Hierarchical Clustering (Taxonomy Approach) 205
7 8 Structural Pattern Recognition Methods 206
8 More About Biological Memory 210
8 1 Physiological Foundations of Memory 210
811 On the Mechanisms of Memory in Biological Systems 210
812 Structural Features of Some Neural Networks 213
813 Functional Features of Neurons 218
814 Modelling of the Synaptic Plasticity 222
815 Can the Memory Capacity Ensue from Synaptic Changes? 227
8 2 The Unified Cortical Memory Model 230
821 The Laminar Network Organization 230
822 On the Roles of Interneurons 232
823 Representation of Knowledge Over Memory Fields 233
824 Self-Controlled Operation of Memory 237
8 3 Collateral Reading 239
831 Physiological Results Relevant to Modelling 239
832 Related Modelling 240
9 Notes on Neural Computing 241
9 1 First Theoretical Views of Neural Networks 241
9 2 Motives for the Neural Computing Research 242
9 3 What Could the Purpose of the Neural Networks be? 245
9 4 Definitions of Artificial Neural Computing and General Notes
on Neural Modelling 249
9 5 Are the Biological Neural Functions Localized or Distributed? 253
9 6 Is Nonlinearity Essential to Neural Computing? 255
9 7 Characteristic Differences Between Neural and Digital
Computers 259
971 The Degree of Parallelism of the Neural Networks is Still
Higher than that of any Massively Parallel
Digital Computer 259
972 Why the Neural Signals Cannot be Approximated by
Boolean Variables 261
Concents XV
973 The Neural Circuits do not Implement Finite Automata 261
974 Undue Views of the Logic Equivalence of the Brain and
Computers on a High Level 263
9 8 Connectionist Models 264
9 9 How can the Neural Computers be Programmed? 267
10 Optical Associative Memories 269
10 1 Nonholographic Methods 269
10 2 General Aspects of Holographic Memories 271
10 3 A Simple Principle of Holographic Associative Memory 273
10 4 Addressing in Holographic Memories 275
10 5 Recent Advances of Optical Associative Memories 280
Bibliography on Pattern Recognition 285
References 289
Subject Index 301
|
| adam_txt |
Teuvo Kohonen
Self-Organization and
Associative Memory
Second Edition
With 99 Figures
Springer-Verlag Berlin Heidelberg New York
London Paris Tokyo
Contents
1 Various Aspects of Memory 1
1 1 On the Purpose and Nature of Biological Memory 1
111 Some Fundamental Concepts 1
112 The Classical Laws of Association 3
113 On Different Levels of Modelling 4
1 2 Questions Concerning the Fundamental Mechanisms of Memory 4
121 Where Do the Signals Relating to Memory Act Upon? 5
122 What Kind of Encoding is Used for Neural Signals? 6
123 What are the Variable Memory Elements? 7
124 How are Neural Signals Addressed in Memory? 8
1 3 Elementary Operations Implemented by Associative Memory 14
131 Associative Recall 14
132 Production of Sequences from the Associative Memory 16
133 On the Meaning of Background and Context 20
1 4 More Abstract Aspects of Memory 21
141 The Problem of Infinite-State Memory 21
142 Invariant Representations 22
143 Symbolic Representations 24
144 Virtual Images 25
145 The Logic of Stored Knowledge 27
2 Pattern Mathematics 30
2 1 Mathematical Notations and Methods 30
211 Vector Space Concepts 30
212 Matrix Notations 41
213 Further Properties of Matrices 44
214 Matrix Equations 48
215 Projection Operators 54
216 On Matrix Differential Calculus 57
2 2 Distance Measures for Patterns 59
XII Contents
221 Measures of Similarity and Distance in Vector Spaces 59
222 Measures of Similarity and Distance Between Symbol
Strings 63
223 More Accurate Distance Measures for Text 66
3 Classical Learning Systems 68
3 1 The Adaptive Linear Element (Adaline) 69
311 Description of Adaptation by the Stochastic
Approximation 71
3 2 ThePerceptron 72
3 3 The Learning Matrix 74
3 4 Physical Realization of Adaptive Weights 77
341 Perceptron and Adaline 77
342 Classical Conditioning 78
343 Conjunction Learning Switches 80
344 Digital Representation of Adaptive Circuits 80
345 Biological Components 81
4 A New Approach to Adaptive Filters 82
4 1 Survey of Some Necessary Functions 82
4 2 On the Transfer Function of the Neuron 84
4 3 Models for Basic Adaptive Units 90
431 On the Linearization of the Basic Unit 90
432 Various Cases of Adaptation Laws 91
433 Two Limit Theorems 98
434 The Novelty Detector 101
4 4 Adaptive Feedback Networks 104
441 The Autocorrelation Matrix Memory 105
442 The Novelty Filter 109
5 Self-Organizing Feature Maps 119
5 1 On the Feature Maps of the Brain 119
5 2 Formation of Localized Responses by Lateral Feedback 122
5 3 Computational Simplification of the Process 127
531 Definition of the Topology-Preserving Mapping 127
532A Simple Two-Dimensional Self-Organizing System 130
5 4 Demonstrations of Simple Topology-Preserving Mappings 133
541 Images of Various Distributions of Input Vectors 133
542 The Magic TV 137
543 Mapping by a Feeler Mechanism 139
5 5 TonotopicMap 140
Contents XIII
5 6 Formation of Hierarchical Representations 141
561 Taxonomy Example 141
562 Phoneme Map 142
5 7 Mathematical Treatment of Self-Organization 143
571 Ordering of Weights 144
572 Convergence Phase 150
5 8 Automatic Selection of Feature Dimensions 155
6 Optimal Associative Mappings 158
6 1 Transfer Function of an Associative Network 159
6 2 Autoassociative Recall as an Orthogonal Projection 160
621 Orthogonal Projections 160
622 Error-Correcting Properties of Projections 161
6 3 The Novelty Filter 163
631 Two Examples of Novelty Filter 163
632 Novelty Filter as an Autoassociative Memory 165
6 4 Autoassociative Encoding 165
641 An Example of Autoassociative Encoding 166
6 5 Optimal Associative Mappings 167
651 The Optimal Linear Associative Mapping 168
652 Optimal Nonlinear Associative Mappings 172
6 6 Relationship Between Associative Mapping, Linear Regression,
and Linear Estimation 175
661 Relationship of the Associative Mapping to Linear
Regression 175
662 Relationship of the Regression Solution to the Linear
Estimator 176
6 7 Recursive Computation of the Optimal Associative Mapping 177
671 Linear Corrective Algorithms 178
672 Best Exact Solution (Gradient Projection) 179
673 Best Approximate Solution (Regression) 180
674 Recursive Solution in the General Case 182
6 8 Special Cases 183
681 The Correlation Matrix Memory 183
682 Relationship Between Conditional Averages and Optimal
Estimator 184
7 Pattern Recognition 185
7 1 Discriminant Functions 185
7 2 Statistical Formulation of Pattern Classification 187
7 3 Comparison Methods 190
XIV Contents
7 4 The Subspace Methods of Classification 192
741 The Basic Subspace Method 192
742 The Learning Subspace Method (LSM) 193
7 5 Learning Vector Quantization 199
7 6 Feature Extraction 202
7 7 Clustering 203
771 Simple Clustering (Optimization Approach) 204
772 Hierarchical Clustering (Taxonomy Approach) 205
7 8 Structural Pattern Recognition Methods 206
8 More About Biological Memory 210
8 1 Physiological Foundations of Memory 210
811 On the Mechanisms of Memory in Biological Systems 210
812 Structural Features of Some Neural Networks 213
813 Functional Features of Neurons 218
814 Modelling of the Synaptic Plasticity 222
815 Can the Memory Capacity Ensue from Synaptic Changes? 227
8 2 The Unified Cortical Memory Model 230
821 The Laminar Network Organization 230
822 On the Roles of Interneurons 232
823 Representation of Knowledge Over Memory Fields 233
824 Self-Controlled Operation of Memory 237
8 3 Collateral Reading 239
831 Physiological Results Relevant to Modelling 239
832 Related Modelling 240
9 Notes on Neural Computing 241
9 1 First Theoretical Views of Neural Networks 241
9 2 Motives for the Neural Computing Research 242
9 3 What Could the Purpose of the Neural Networks be? 245
9 4 Definitions of Artificial Neural Computing and General Notes
on Neural Modelling 249
9 5 Are the Biological Neural Functions Localized or Distributed? 253
9 6 Is Nonlinearity Essential to Neural Computing? 255
9 7 Characteristic Differences Between Neural and Digital
Computers 259
971 The Degree of Parallelism of the Neural Networks is Still
Higher than that of any Massively Parallel
Digital Computer 259
972 Why the Neural Signals Cannot be Approximated by
Boolean Variables 261
Concents XV
973 The Neural Circuits do not Implement Finite Automata 261
974 Undue Views of the Logic Equivalence of the Brain and
Computers on a High Level 263
9 8 Connectionist Models 264
9 9 How can the Neural Computers be Programmed? 267
10 Optical Associative Memories 269
10 1 Nonholographic Methods 269
10 2 General Aspects of Holographic Memories 271
10 3 A Simple Principle of Holographic Associative Memory 273
10 4 Addressing in Holographic Memories 275
10 5 Recent Advances of Optical Associative Memories 280
Bibliography on Pattern Recognition 285
References 289
Subject Index 301 |
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| id | DE-604.BV022007749 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:11:42Z |
| indexdate | 2024-07-09T20:49:09Z |
| institution | BVB |
| isbn | 3540183140 0387183140 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015222381 |
| oclc_num | 16832565 |
| open_access_boolean | |
| owner | DE-706 |
| owner_facet | DE-706 |
| physical | XV, 312 S. graph. Darst. |
| publishDate | 1988 |
| publishDateSearch | 1988 |
| publishDateSort | 1988 |
| publisher | Springer |
| record_format | marc |
| series | Springer series in information sciences |
| series2 | Springer series in information sciences |
| spelling | Kohonen, Teuvo Verfasser aut Self-organization and associative memory 2. ed. Berlin [u.a.] Springer 1988 XV, 312 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Springer series in information sciences 8 Mémoire Ordinateurs - Mémoires associatives Systèmes auto-organisés Associative storage Memory Self-organizing systems Selbst organisierendes System (DE-588)4054424-2 gnd rswk-swf Mathematische Psychologie (DE-588)4126558-0 gnd rswk-swf Assoziativspeicher (DE-588)4124575-1 gnd rswk-swf Gedächtnis (DE-588)4019614-8 gnd rswk-swf Assoziatives Gedächtnis (DE-588)4290792-5 gnd rswk-swf Nervennetz (DE-588)4041638-0 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Selbstbestimmung (DE-588)4135889-2 gnd rswk-swf Selbsteinstellendes System (DE-588)4054398-5 gnd rswk-swf Modell (DE-588)4039798-1 gnd rswk-swf Psychologie (DE-588)4047704-6 gnd rswk-swf Selbstbestimmung (DE-588)4135889-2 s DE-604 Assoziatives Gedächtnis (DE-588)4290792-5 s Assoziativspeicher (DE-588)4124575-1 s Gedächtnis (DE-588)4019614-8 s 1\p DE-604 Künstliche Intelligenz (DE-588)4033447-8 s 2\p DE-604 Psychologie (DE-588)4047704-6 s 3\p DE-604 Nervennetz (DE-588)4041638-0 s Modell (DE-588)4039798-1 s 4\p DE-604 Mathematische Psychologie (DE-588)4126558-0 s 5\p DE-604 Selbsteinstellendes System (DE-588)4054398-5 s 6\p DE-604 Selbst organisierendes System (DE-588)4054424-2 s 7\p DE-604 Springer series in information sciences 8 (DE-604)BV000008063 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015222381&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 6\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 7\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kohonen, Teuvo Self-organization and associative memory Springer series in information sciences Mémoire Ordinateurs - Mémoires associatives Systèmes auto-organisés Associative storage Memory Self-organizing systems Selbst organisierendes System (DE-588)4054424-2 gnd Mathematische Psychologie (DE-588)4126558-0 gnd Assoziativspeicher (DE-588)4124575-1 gnd Gedächtnis (DE-588)4019614-8 gnd Assoziatives Gedächtnis (DE-588)4290792-5 gnd Nervennetz (DE-588)4041638-0 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Selbstbestimmung (DE-588)4135889-2 gnd Selbsteinstellendes System (DE-588)4054398-5 gnd Modell (DE-588)4039798-1 gnd Psychologie (DE-588)4047704-6 gnd |
| subject_GND | (DE-588)4054424-2 (DE-588)4126558-0 (DE-588)4124575-1 (DE-588)4019614-8 (DE-588)4290792-5 (DE-588)4041638-0 (DE-588)4033447-8 (DE-588)4135889-2 (DE-588)4054398-5 (DE-588)4039798-1 (DE-588)4047704-6 |
| title | Self-organization and associative memory |
| title_auth | Self-organization and associative memory |
| title_exact_search | Self-organization and associative memory |
| title_exact_search_txtP | Self-organization and associative memory |
| title_full | Self-organization and associative memory |
| title_fullStr | Self-organization and associative memory |
| title_full_unstemmed | Self-organization and associative memory |
| title_short | Self-organization and associative memory |
| title_sort | self organization and associative memory |
| topic | Mémoire Ordinateurs - Mémoires associatives Systèmes auto-organisés Associative storage Memory Self-organizing systems Selbst organisierendes System (DE-588)4054424-2 gnd Mathematische Psychologie (DE-588)4126558-0 gnd Assoziativspeicher (DE-588)4124575-1 gnd Gedächtnis (DE-588)4019614-8 gnd Assoziatives Gedächtnis (DE-588)4290792-5 gnd Nervennetz (DE-588)4041638-0 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Selbstbestimmung (DE-588)4135889-2 gnd Selbsteinstellendes System (DE-588)4054398-5 gnd Modell (DE-588)4039798-1 gnd Psychologie (DE-588)4047704-6 gnd |
| topic_facet | Mémoire Ordinateurs - Mémoires associatives Systèmes auto-organisés Associative storage Memory Self-organizing systems Selbst organisierendes System Mathematische Psychologie Assoziativspeicher Gedächtnis Assoziatives Gedächtnis Nervennetz Künstliche Intelligenz Selbstbestimmung Selbsteinstellendes System Modell Psychologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015222381&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000008063 |
| work_keys_str_mv | AT kohonenteuvo selforganizationandassociativememory |