Computational models for neuroscience: human cortical information processing
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Springer
2003
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 299 S. Ill., graph. Darst. |
| ISBN: | 1852335939 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Contributors xvii
Chapter 1 The Neurointeractive Paradigm: Dynamical Mechanics
and the Emergence of Higher Cortical Function 1
1.1 Abstract 1
1.2 Introduction 2
1.3 Principles of Cortical Neurointeractivity 4
1.4 Dynamical Mechanics 9
1.5 The Neurointeractive Cycle 13
1.6 Developmental Emergence 18
1.7 Explaining Emergence 21
1.8 References 22
Chapter 2 The Cortical Pyramidal Cell as a Set of Interacting
Error Backpropagating Dendrites: Mechanism for
Discovering Nature s Order 25
2.1 Abstract 25
2.2 Introduction 26
2.2.1 Defining the Problem 27
2.2.2 How Does the Brain Discover Orderly Relations? 28
2.3 Implementation of the Proposal 31
2.3.1 How Might Error Backpropagation Learning Be Implemented
in Dendrites? 31
2.3.2 How Can Dendrites Be Set Up to Teach Each Other? 36
2.3.3 How to Divide Connections Among the Dendrites? 42
xii Contents
2.4 Cortical Minicolumnar Organization and SINBAD Neurons 52
2.5 Associationism 56
2.5.1 SINBAD as an Associationist Theory 56
2.5.2 Countering Nativist Arguments 57
2.6 Acknowledgements 60
References 60
Chapter 3 Performance of Intelligent Systems Governed by
Internally Generated Goals 65
3.1 Abstract 65
3.2 Introduction 65
3.3 Perception as an Active Process 67
3.4 Nonlinear Dynamics of the Olfactory System 71
3.5 Chaotic Oscillations During Learning Novel Stimuli.... 74
3.6 Generalization and Consolidation of New Perceptions with
Context 76
3.7 The Central Role of the Limbic System 78
3.8 Conclusions 81
3.9 Acknowledgements 82
References 82
Chapter 4 A Theory of Thalamocortex 85
4.1 Abstract 85
4.2 Active Neurons 85
4.3 Neuronal Connections within Thalamocortex 86
4.4 Cortical Regions 87
4.5 Feature Attractor Associative Memory Neural Network .. 87
4.6 Antecedent Support Associative Memory Neural Network 94
4.7 Hierarchical Abstractor Associative Memory Neural
Network 99
4.8 Consensus Building 104
4.9 Brain Command Loop 107
4.10 Testing this Theory 110
4.11 Acknowledgements Ill
Appendix A: Sketch of an Analysis of the Simplified Feature Attractor
Associative Memory Neural Network 112
Appendix B: Experiments with a Simplified Antecedent Support
Associative Memory Neural Network 115
Appendix C: An Experiment with Consensus Building 118
References 120
Chapter 5 Elementary Principles of Nonlinear Synaptic
Transmission 125
5.1 Abstract 125
5.2 Introduction 125
Contents xiii
5.3 Frequency dependent Synaptic Transmission 128
5.4 Nonlinear Synapses Enable Temporal Integration 130
5.5 Temporal Information 132
5.6 Packaging Temporal Information 134
5.7 Size of Temporal Information Packages 135
5.8 Classes of Temporal Information Packages 137
5.9 Emergence of the Population Signal 141
5.10 Recurrent Neural Networks 143
5.11 Combining Temporal Information in Recurrent Networks 144
5.12 Organization of Synaptic Parameters 146
5.13 Learning Dynamics, Learning to Predict 147
5.14 Redistribution of Synaptic Efficacy 148
5.15 Optimizing Synaptic Prediction 150
5.16 A Nested Learning Algorithm 152
5.17 Retrieving Memories from Nonlinear Synapses 153
5.18 Conclusion 154
5.19 Acknowledgements 155
Appendix A: Sherrington s Leap 156
Appendix B: Functional Significance 156
Appendix C: Visual Patch Recordings 158
Appendix D: Biophysical Basis of Parameters 158
Appendix E: Single Connection, Many Synapses 159
Appendix F: The Model 159
Appendix G: Synaptic Classes 161
Appendix H: Paired Pulses 161
Appendix I: Digitization of Synaptic Parameters 162
Appendix J: Steady State 162
Appendix K: Inhibitory Synapses 163
Appendix L: Lack of Boundaries 163
Appendix M: Speed of RI Accumulation 164
Appendix N: Network Efficiency 164
Appendix O: The Binding Problem of the Binding Problem 164
References 165
Chapter 6 The Development of Cortical Models to Enable Neural
based Cognitive Architectures 171
6.1 Introduction 171
6.1.1 Computational Neuroscience Paradigms and Predictions 172
6.2 The Challenge of Cognitive Architectures 173
6.2.1 General Cognitive Skills 173
6.2.2 A Survey of Current Cognitive Architectures 175
6.2.3 Assumptions and Limitations of Current Cognitive
Architectures 183
6.3 The Prospects for a Neural based Cognitive Architecture 184
6.3.1 Limitations of Artificial Neural Networks 184
6.3.2 Biological Networks Emerging from Computational
Neuroscience: Sensory and Motor Modules 184
xiv Contents
6.3.3 Forebrain Systems Supporting Cortical Function 184
6.4 Elements of a General Cortical Model 186
6.4.1 Single Neuron Models or Processor Elements 186
6.4.2 Microcircuitry 186
6.4.3 Dynamic Synaptic Connectivity 187
6.4.4 Ensemble Dynamics and Coding 188
6.4.5 Transient Coherent Structures and Cognitive Dynamics. 189
6.5 Promising Models and their Capabilities 189
6.5.1 Biologically Based Cortical Systems 189
6.5.2 A Cortical System Based on Neurobiology, Biological
Principles and Mathematical Analysis: Cortronics 192
6.5.3 Connectionist Architectures with Biological Principles: The
Convergence of Cognitive Science and Computational
Neuroscience 193
6.6 The Challenges of Demonstrating Cognitive Ability .... 195
6.6.1 Robotics and Autonomous Systems 196
6.7 Co development Strategies for Automated Systems and
Human Performers 196
6.8 Acknowledgements 197
References 197
Chapter 7 The Behaving Human Neocortex as a Dynamic Network
of Networks 205
7.1 Abstract 205
7.2 Neural Organization Across Scales 206
7.3 Network of Networks (NoN) Model 209
7.3.1 Architecture 209
7.3.2 Model Formulation 211
7.3.3 NoN Properties 212
7.3.4 NoN Contributions 214
7.4 Neurobiological Predicatability and Falsifiability 214
7.5 Implications for Neuroengineering 215
7.6 Concluding Remarks 216
7.7 Acknowledgements 217
References 217
Chapter 8 Towards Global Principles of Brain Processing... 221
8.1 Abstract 221
8.2 Introduction 221
8.3 What Could Brain Principles Look Like? 222
8.4 Structural Modeling 224
8.5 Static Activation Study Results 226
8.6 The Motion After Effect (MAE) 227
8.7 The Three Stage Model of Consciousness 229
8.8 The COD AM Model of Consciousness 233
Contents xv
8.9 Principles of the Global Brain 235
8.10 The Thinking Brain 237
8.11 Discussion 239
8.12 Acknowledgement 240
References 240
Chapter 9 The Neural Networks for Language in the Brain:
Creating LAD 245
9.1 Abstract 245
9.2 Introduction 245
9.3 The ACTION Net Model of TSSG 249
9.4 Phrase Structure Analyzers 252
9.5 Generativity of the Adjectival Phrase Analyzer 256
9.6 Complexity of Phrase Structure Analysis 259
9.7 Future Directions in the Construction of LAD 259
9.8 Conclusions 261
References 262
Chapter 10 Cortical Belief Networks 267
10.1 Abstract 267
10.1 Introduction 267
10.1 An Example 268
10.1 Representing Distributions in Populations 270
10.1 Basis Function Representations 271
10.1 Generative Representations 272
10.1 Standard Bayesian Approach 272
10.1 Distributional Population Coding 273
10.1 Applying Distributional Population Coding 274
10.1.1 Population Analysis 274
10.1.1 Decoding Transparent Motion 274
10.1.1 Decision Noise 277
10.1.1 Lateral Interactions 280
10.1 Cortical Belief Network 282
10.1 Discussion 284
10.1 Acknowledgements 285
10.1 References 285
Index 293
|
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| id | DE-604.BV014489679 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T19:03:05Z |
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| spelling | Computational models for neuroscience human cortical information processing Robert Hecht-Nielsen ... (eds.) London [u.a.] Springer 2003 XIX, 299 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Cortex cérébral rasuqam Neuroscience informatique rasuqam Réseau neuronal (Biologie) rasuqam Réseau neuronal (Informatique) rasuqam Simulation par ordinateur rasuqam Traitement de l'information par le cerveau rasuqam Cerebral Cortex physiology Cerebral cortex Computer simulation Computational neuroscience Computer Simulation Models, Neurological Nerve Net physiology Neural networks (Computer science) Neural networks (Neurobiology) Thalamus (DE-588)4184971-1 gnd rswk-swf Gehirn (DE-588)4019752-9 gnd rswk-swf Modell (DE-588)4039798-1 gnd rswk-swf Neurocomputer (DE-588)4200446-9 gnd rswk-swf Großhirnrinde (DE-588)4072114-0 gnd rswk-swf Nervennetz (DE-588)4041638-0 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Großhirnrinde (DE-588)4072114-0 s Nervennetz (DE-588)4041638-0 s Modell (DE-588)4039798-1 s DE-604 Thalamus (DE-588)4184971-1 s Gehirn (DE-588)4019752-9 s Neuronales Netz (DE-588)4226127-2 s Neurocomputer (DE-588)4200446-9 s Hecht-Nielsen, Robert 1947- Sonstige (DE-588)1047954923 oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009881814&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Computational models for neuroscience human cortical information processing Cortex cérébral rasuqam Neuroscience informatique rasuqam Réseau neuronal (Biologie) rasuqam Réseau neuronal (Informatique) rasuqam Simulation par ordinateur rasuqam Traitement de l'information par le cerveau rasuqam Cerebral Cortex physiology Cerebral cortex Computer simulation Computational neuroscience Computer Simulation Models, Neurological Nerve Net physiology Neural networks (Computer science) Neural networks (Neurobiology) Thalamus (DE-588)4184971-1 gnd Gehirn (DE-588)4019752-9 gnd Modell (DE-588)4039798-1 gnd Neurocomputer (DE-588)4200446-9 gnd Großhirnrinde (DE-588)4072114-0 gnd Nervennetz (DE-588)4041638-0 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4184971-1 (DE-588)4019752-9 (DE-588)4039798-1 (DE-588)4200446-9 (DE-588)4072114-0 (DE-588)4041638-0 (DE-588)4226127-2 |
| title | Computational models for neuroscience human cortical information processing |
| title_auth | Computational models for neuroscience human cortical information processing |
| title_exact_search | Computational models for neuroscience human cortical information processing |
| title_full | Computational models for neuroscience human cortical information processing Robert Hecht-Nielsen ... (eds.) |
| title_fullStr | Computational models for neuroscience human cortical information processing Robert Hecht-Nielsen ... (eds.) |
| title_full_unstemmed | Computational models for neuroscience human cortical information processing Robert Hecht-Nielsen ... (eds.) |
| title_short | Computational models for neuroscience |
| title_sort | computational models for neuroscience human cortical information processing |
| title_sub | human cortical information processing |
| topic | Cortex cérébral rasuqam Neuroscience informatique rasuqam Réseau neuronal (Biologie) rasuqam Réseau neuronal (Informatique) rasuqam Simulation par ordinateur rasuqam Traitement de l'information par le cerveau rasuqam Cerebral Cortex physiology Cerebral cortex Computer simulation Computational neuroscience Computer Simulation Models, Neurological Nerve Net physiology Neural networks (Computer science) Neural networks (Neurobiology) Thalamus (DE-588)4184971-1 gnd Gehirn (DE-588)4019752-9 gnd Modell (DE-588)4039798-1 gnd Neurocomputer (DE-588)4200446-9 gnd Großhirnrinde (DE-588)4072114-0 gnd Nervennetz (DE-588)4041638-0 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Cortex cérébral Neuroscience informatique Réseau neuronal (Biologie) Réseau neuronal (Informatique) Simulation par ordinateur Traitement de l'information par le cerveau Cerebral Cortex physiology Cerebral cortex Computer simulation Computational neuroscience Computer Simulation Models, Neurological Nerve Net physiology Neural networks (Computer science) Neural networks (Neurobiology) Thalamus Gehirn Modell Neurocomputer Großhirnrinde Nervennetz Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009881814&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hechtnielsenrobert computationalmodelsforneurosciencehumancorticalinformationprocessing |