Introduction to neural and cognitive modeling:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Mahwah, NJ [u.a.]
Erlbaum
2000
|
| Edition: | 2. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Nebentitel: Neural and cognitive modeling |
| Physical Description: | XIX, 491 S. Ill., graph. Darst. |
| ISBN: | 080582006X 0805820051 |
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Record in the Search Index
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| adam_text | Contents
(Sections listed in italics consist of equations and exercises, essential for classroom use but not for
descriptive reading, and supplementary sources.)
Flow Chart of the Book ix
Preface xi
Some Technical Conventions Used in This Book xv
Preface to the Second Edition xvii
Chapter 1
Brain and Machine: The Same Principles? 1
What Are Neural Networks? 1
Is Biological Realism a Virtue? 3
What Are Some Principles of Neural Network Theory? 4
Methodological Considerations 9
Chapter 2
Historical Outline 11
2.1. Digital Approaches 11
The McCulloch Pitts Network 12
Early Approaches to Modeling Learning: Hull and Hebb 16
Rosenblatt s Perceptrons 17
Some Experiments With Perceptrons 21
The Divergence of Artificial Intelligence and Neural Modeling 24
2.2. Continuous and Random Net Approaches 26
Rashevsky s Work 26
Early Random Net Models 29
Reconciling Randomness and Specificity 30
Definitions and Detailed Rules for Rosenblatt s Perceptrons 32
Detailed Description: Perceptron to Discriminate Vertical
Versus Horizontal 33
Exercises for Chapter 2 38
Some Additional Sources 40
Chapter 3
Associative Learning and Synaptic Plasticity 41
3.1. Physiological Bases for Learning 41
3.2. Rules for Associative Learning 43
Outstars and Other Early Models of Grossberg 43
Anderson s Connection Matrices 49
Kohonen s Early Work 53
3.3. Learning Rules Related to Changes in Node Activities 55
Klopf s Hedonistic Neurons and the Sutton Barto Learning Rule 55
Error Correction and Back Propagation 57
v
vi CONTENTS
The Differential Hebbian Idea 60
Gated Dipole Theory 61
3.4. Associative Learning of Patterns 65
Kohonen s Recent Work: Autoassociation and Heteroassociation 68
Kosko s Bidirectional Associative Memory 70
Equations for Networks in Chapter 3, and Physiological Details 73
Detailed Description: The Gated Dipole 74
Exercises for Chapter 3 88
Some Additional Sources 94
Chapter 4
Competition, Lateral Inhibition, and Short Term Memory 95
4.1. Contrast Enhancement, Competition, and Normalization 96
Hartline and Ratliff s Work, and Other Early Visual Models 100
Nonrecurrent Versus Recurrent Lateral Inhibition 102
4.2. Lateral Inhibition and Excitation Between Sensory Representations 105
Wilson and Cowan s Work 105
Work of Grossberg and Colleagues 108
Work of Amari and Colleagues 112
Energy Functions in the Cohen Grossberg and Hopfield Tank Models 114
The Implications of Approach to Equilibrium 117
Networks With Synchronized Oscillations 118
4.3. Visual Pattern Recognition Models 119
Visual Illusions 119
Boundary Detection Versus Feature Detection 125
Binocular and Stereoscopic Vision 128
Visual Motion 132
Comparison of Grossberg s and Marr s Approaches 133
4.4. Uses of Lateral Inhibition in Higher Level Processing 134
Equations for Networks in Chapter 4 136
Detailed Description: Shunting Lateral Inhibition With
Faster than Linear Signal Function 137
Exercises for Chapter 4 146
Some Additional Sources 1 S3
Chapter 5
Conditioning, Attention, and Reinforcement 155
5.1. Network Models of Classical Conditioning 157
Early Work: Brindley and Uttley 157
Rescorla and Wagner s Psychological Model 160
Grossberg: Drive Representations and Synchronization 161
Aversive Conditioning and Extinction 165
Differential Hebbian Theory Versus Gated Dipole Theory 166
5.2. Attention and Short Term Memory in Conditioning Models 168
Grossberg s Approach to Attention 169
Sutton and Barto s Approach: Blocking and Interstimulus Interval Effects 171
CONTENTS vii
Some Contrasts Between the Grossberg and Sutton Barto Approaches 175
Further Connections With Invertebrate Neurophysiology 176
Further Connections With Vertebrate Neurophysiology 181
Gated Dipoles, Aversive Conditioning, and Timing 181
Equations for Networks in Chapter 5 184
Detailed Description: Sutton Barto Equations 185
Exercises for Chapter 5 193
Some Additional Sources 196
Chapter 6
Coding and Categorization 198
6.1. Interactions Between Short and Long Term Memory in Code Development 199
Malsburg s Model With Synaptic Conservation 199
Grossberg s Model With Pattern Normalization 205
Mathematical Results of Grossberg and Amari 208
Feature Detection Models With Stochastic Elements 210
From Feature Coding to Categorization 211
6.2. Supervised Classification Models 212
The Back Propagation Network and its Variants 213
The RCE Model 221
6.3. Unsupervised Classification Models 223
The Rumelhart Zipser Competitive Learning Algorithm 223
Adaptive Resonance Theory 226
Edehnan and Neural Darwinism 235
6.4. Models that Combine Supervised and Unsupervised Parts 237
ARTMAP and Other Supervised Adaptive Resonance Networks 237
Brain State in a Box (BSB) Models 240
6.5. Translation and Scale Invariance 244
6.6. Processing Spatiotemporal Patterns 247
Equations for Networks in Chapter 6 251
Detailed Description: Adaptive Resonance Theory 252
Exercises for Chapter 6 270
Some Additional Sources 2 78
Chapter 7
Optimization, Control, Decision, and Knowledge Representation 280
7.1. Optimization and Control 2 81
Classical Optimization Problems 282
Simulated Annealing and Boltzmann Machines 285
Motor Control: The Example of Eye Movements 288
Motor Control: Arm Movements 293
Speech Recognition and Synthesis 299
Robotic and Other Industrial Control Problems 302
7.2. Decision Making and Knowledge Representation 306
What, If Anything, Do Biological Organisms Optimize? 306
Affect, Habit, and Novelty in Neural Network Theories 307
viii CONTENTS
Knowledge Representation: Letters and Words 310
Knowledge Representation: Concepts and Inference 314
7.3. Neural Control Circuits, Mental Illness, and Brain Areas 321
Overarousal, Underarousal, Parkinsonism, and Depression 322
Frontal Lobe Function and Dysfunction 326
Disruption of Cognitive Motivational Interactions 327
Impairment of Motor Task Sequencing 332
Disruption of Context Processing 333
Models of Specific Brain Areas 335
Models of the Cerebellum 336
Models of the Hippocampus 337
Models of the Basal Ganglia 341
Models of the Cerebral Cortex 342
Equations for Networks in Chapter 7 345
Detailed Description: Wisconsin Card Sorting Test Model 346
Exercises for Chapter 7 359
Some Additional Sources 363
Chapter 8
A Few Recent Technical Advances 365
8.1. Some Toy and Real World Computing Applications 366
Pattern Recognition 367
Knowledge Engineering 368
Financial Engineering 369
Oddball Applications 370
8.2. Some Neurobiological Discoveries 371
Appendix 1
Basic Facts of Neurobiology 375
The Neuron 375
Synapses, Transmitters, Messengers, and Modulators 380
Invertebrate and Vertebrate Nervous Systems 384
Functions of Vertebrate Subcortical Regions 387
Functions of the Mammalian Cerebral Cortex 393
Appendix 2
Difference And Differential Equations in Neural Networks 396
Example: The Sutton Barto Difference Equations 396
Differential Versus Difference Equations 399
Outstar Equations: Network Interpretation and Numerical Implementation 403
The Chain Rule and Back Propagation 410
Dynamical Systems: Steady States, Limit Cycles, and Chaos 413
References 417
Author Index 464
Subject Index 476
|
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| discipline | Psychologie Medizin |
| edition | 2. ed. |
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| spelling | Levine, Daniel S. Verfasser aut Introduction to neural and cognitive modeling Daniel S. Levine 2. ed. Mahwah, NJ [u.a.] Erlbaum 2000 XIX, 491 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Nebentitel: Neural and cognitive modeling Neural networks (Computer science) Neural networks (Neurobiology) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Kognition (DE-588)4031630-0 gnd rswk-swf Modell (DE-588)4039798-1 gnd rswk-swf Nervennetz (DE-588)4041638-0 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 s Neuronales Netz (DE-588)4226127-2 s DE-604 Nervennetz (DE-588)4041638-0 s Modell (DE-588)4039798-1 s 1\p DE-604 Kognition (DE-588)4031630-0 s Computersimulation (DE-588)4148259-1 s 2\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008646673&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 |
| spellingShingle | Levine, Daniel S. Introduction to neural and cognitive modeling Neural networks (Computer science) Neural networks (Neurobiology) Neuronales Netz (DE-588)4226127-2 gnd Computersimulation (DE-588)4148259-1 gnd Kognition (DE-588)4031630-0 gnd Modell (DE-588)4039798-1 gnd Nervennetz (DE-588)4041638-0 gnd Kognitiver Prozess (DE-588)4140177-3 gnd |
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| title | Introduction to neural and cognitive modeling |
| title_auth | Introduction to neural and cognitive modeling |
| title_exact_search | Introduction to neural and cognitive modeling |
| title_full | Introduction to neural and cognitive modeling Daniel S. Levine |
| title_fullStr | Introduction to neural and cognitive modeling Daniel S. Levine |
| title_full_unstemmed | Introduction to neural and cognitive modeling Daniel S. Levine |
| title_short | Introduction to neural and cognitive modeling |
| title_sort | introduction to neural and cognitive modeling |
| topic | Neural networks (Computer science) Neural networks (Neurobiology) Neuronales Netz (DE-588)4226127-2 gnd Computersimulation (DE-588)4148259-1 gnd Kognition (DE-588)4031630-0 gnd Modell (DE-588)4039798-1 gnd Nervennetz (DE-588)4041638-0 gnd Kognitiver Prozess (DE-588)4140177-3 gnd |
| topic_facet | Neural networks (Computer science) Neural networks (Neurobiology) Neuronales Netz Computersimulation Kognition Modell Nervennetz Kognitiver Prozess |
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