Computing the Brain (eBook)

Front Cover 1
Computing the Brain: A Guide to Neuroinformatics 4
Copyright Page 5
Contents 6
Contributors 10
Preface 12
Part 1: Introduction 16
Chapter 1.1. NeuroInformatics: The Issues 18
1.1.1 Overview 18
1.1.2 Modeling and Simulation 23
1.1.3 Databases for Neuroscience Time Series 29
1.1.4 Visualization and Atlas-Based Databases 32
1.1.5 Data Management and Summary Databases 34
1.1.6 The NeuroInformatics Workbench 41
References 42
Chapter 1.2. Introduction to Databases 44
Abstract 44
1.2.1 An Overview of Database Management 44
1.2.2 Historical View of Key Database Developments 45
1.2.3 Relational Database Model 46
1.2.4 SQL 47
1.2.5 Object-Based Database Models 48
1.2.6 Object-Relational Database Model 52
1.2.7 An Overview of Federated Database Systems 53
References 54
Part 2: Modeling and Simulation 56
Chapter 2.1. Modeling the Brain 58
Abstract 58
2.1.1 Modeling Issues 58
2.1.2 Parietal-Premotor Interactions in the Control of Grasping 61
2.1.3 Basal Ganglia 63
2.1.4 Cerebellum 68
2.1.5 Hippocampus, Parietal Cortex, and Navigation 76
2.1.6 Discussion 82
References 82
Chapter 2.2. NSL Neural Simulation Language 86
2.2.1 Modeling and Simulation of Neural Networks 87
2.2.2 NSL Modules and Simulation 89
2.2.3 The NSL System 98
2.2.4 Simulating a Model—The Maximum Selector Model 100
2.2.5 Maximum Selector Model 100
2.2.6 Available Resources 104
References 104
Chapter 2.3. EONS: A Multi-Level Modeling System and Its Applications 106
2.3.1 Introduction 106
2.3.2 EONS Object Library 107
2.3.3 Protocol-Based Simulation 113
2.3.4 Conclusion 115
References 115
Chapter 2.4. Brain Imaging and Synthetic PET 118
Abstract 118
2.4.1 PET Imaging and Neurophysiology 118
2.4.2 Defining Synthetic PET 119
2.4.3 Example: A Model of Saccadic Eye Movements 120
2.4.4 Synthetic PET for Grasp Control 122
2.4.5 Discussion 126
References 127
Part 3: Databases for Neuroscience Time Series 130
Chapter 3.1. Repositories for the Storage of Experimental Neuroscience Data 132
3.1.1 Introduction 132
3.1.2 Protocols: A Data Model To Address Schema Complexity in Neuroscience 133
3.1.3 Considerations of the User Community 134
3.1.4 Building a Time-Series Database for In Vivo Neurophysiology: Cerebellum and Classical Conditioning 135
3.1.5 Building a Time-Series Database for In Vitro Neurophysiology: Long-Term Potentiation (LTP) in the Hippocampal Slice 140
3.1.6 Building a Database for Human Neuroimaging Data 144
3.1.7 Discussion 146
References 147
Chapter 3.2. Design Concepts for NeuroCore and NeuroScience Databases 150
3.2.1 Design Concepts for Neuroscience Databases 150
3.2.2 The Three Main Components of the NeuroCore Database 150
3.2.3 Detailed Description of the NeuroCore Database 152
Conclusion 165
References 165
Chapter 3.3. User Interaction with NeuroCore 166
3.3.1 Introduction 166
3.3.2 NeuroCore Schema Browser 168
3.3.3 Java Applet for Data Entry (JADE) 171
3.3.4 Database Browser 172
3.3.5 DataMunch 175
3.3.6 Discussion 176
References 178
Part 4: ATLAS-BASED DATABASES 180
Chapter 4.1. Interactive Brain Maps and Atlases 182
Abstract 182
4.1.1 General Features of Maps 182
4.1.2 Overall Structure of the Brain 183
4.1.3 Experimental Circuit-Tracing Methods 184
4.1.4 Atlases: Slice-Based Sampling and Standard Brains 184
4.1.5 Transferring Data from Experimental Brain to Standard (Atlas Reference) Brain 188
4.1.6 Toward Textual and Graphical Databases on the Web 189
4.1.7 Three-Dimensional Computer Graphics Models of the Brain 189
4.1.8 Two-Dimensional Flatmaps: Schematic Circuit Diagrams and Distribution Patterns 191
4.1.9 The Future: Atlases as Expandable Databases and Models 191
References 192
Chapter 4.2. Perspective: Geographical Information Systems 194
Abstract 194
4.2.1 Introduction 194
4.2.2 Overview of GIS 195
4.2.3 Atlas-Based Neuroscientific Data 199
4.2.4 Raster Data 200
4.2.5 Conclusion and Web Resources 201
References 202
Chapter 4.3. The Neuroanatomical Rat Brain Viewer (NeuARt) 204
4.3.1 Introduction 204
4.3.2 The NeuARt System 205
4.3.3 Discussion 215
References 215
Chapter 4.4. Neuro Slicer: A Tool for Registering 2-D Slice Data to 3-D Surface Atlases 218
Abstract 218
4.4.1 Introduction 218
4.4.2 Classification Criteria 219
4.4.3 Intrasubject Image Matching 220
4.4.4 Intersubject Image Matching 221
4.4.5 Neuro Slicer: USCBP Histological Registration Tool 223
References 228
Chapter 4.5. An Atlas-Based Database of Neurochemical Data 232
Abstract 232
4.5.1 Synaptic Neurotransmission: Molecular and Functional Aspects 232
4.5.2 Roles of Glutamatergic Synapses in LTP and LTD 233
4.5.3 Glutamate Receptor Regulation and Synaptic Plasticity 233
4.5.4 How To Build a Useful Neurochemical Database 234
4.5.5 Incorporating Neurochemical Data into the NeuroCore Repository of Empirical Data 237
4.5.6 Available Resources 240
4.5.7 Conclusion 242
References 242
Part 5: Data Management 244
Chapter 5.1. Federating Neuroscience Databases 246
Abstract 246
5.1.1 Introduction 246
5.1.2 Information Discovery 247
5.1.3 Semantic Heterogeneity Resolution 248
5.1.4 System-Level Interconnection 248
5.1.5 Characteristics of Sharing Patterns 249
5.1.6 System Architecture for Sharing Primitives/Tools 251
5.1.7 Federating Neuroscience Databases 252
References 253
Chapter 5.2. Dynamic Classification Ontologies 256
Abstract 256
5.2.1 Introduction 256
5.2.2 Heterogeneity 257
5.2.3 Common Ontology 259
5.2.4 Classification 261
5.2.5 Dynamic Classification Ontology 262
5.2.6 Mediators for Information Sharing 265
5.2.7 Conclusions 268
References 269
Chapter 5.3. Annotator: Annotation Technology for the WWW 270
Abstract 270
5.3.1 Introduction 270
5.3.2 Overview of Existing Annotation Software 271
5.3.3 Annotation Technology: An Integrative Approach 271
5.3.4 Annotator 276
References 278
Chapter 5.4. Management of Space in Hierarchical Storage Systems 280
Abstract 280
5.4.1 Introduction 280
5.4.2 Target Environment 283
5.4.3 Four Alternative Space Management Techniques 284
5.4.4 Performance Evaluation 291
5.4.5 Analytical Models 295
5.4.6 Conclusions 298
References 298
Part 6: Summary Databases and Model Repositories 300
Chapter 6.1. Summary Databases and Model Repositories 302
Abstract 302
6.1.1 The Database Typology and the NeuroInformatics Workbench 302
6.1.2 An Overall Perspective 303
6.1.3 General Considerations on Model Repositories 306
6.1.4 Brain Models on the Web 306
6.1.5 NeuroScholar 307
6.1.6 NeuroHomology 309
6.1.7 Future Plans 311
Chapter 6.2. Brain Models on the Web and the Need for Summary Data 312
Abstract 312
6.2.1 Storing Brain Models 312
6.2.2 The BMW-SDB Relationship 313
6.2.3 Reviewing a Model: The Dart Model of Prism Adaptation 314
6.2.4 Database Design 315
6.2.5 Accessing the Database 323
6.2.6 Future Plans 331
References 332
Chapter 6.3. Knowledge Mechanics and the Neuroscholar Project: A New Approach to Neuroscientific Theory 334
6.3.1 An Introduction to Knowledge Mechanics 334
6.3.2 Concept of “Theory” in Neuroscience 335
6.3.3 High-Level Software Requirements and Fundamental Design Concepts of the NeuroScholar System 336
6.3.4 Neuroscholar in Detail 342
6.3.5 The Significance of Knowledge Mechanics 348
References 349
Chapter 6.4. The NeuroHomology Database 352
6.4.1 Introduction: The Definition of the Concept of Homology in Neurobiology 352
6.4.2 Theory of Degrees of Homology 354
6.4.3 The NeuroHomology Database: Description 355
6.4.4 The NeuroHomology Database: Brain Structures 356
6.4.5 NeuroHomology Database: Connectivity Issues 358
6.4.6 The NeuroHomology Database: Homologies 361
6.4.7 Conclusion and Future Development 363
References 365
Appendices 368
Appendix A1. Introduction to Informix 370
Informix SQL Tutorial: A Practical Example 370
Appendix A2. NeuroCore Timeseries Datablade 374
Introduction 374
Internal Structure and Description 374
Support Functions 375
Additional SQL-Invoked Routines 375
Comparing Data 375
Discussion 376
Appendix A3. USCBP Development Team 378
Appendix B1. Informix SQL Quick Reference 380
Introduction 380
SQL Statements 380
Appendix C1. USC Brain Project Research Personnel 382
Appendix C2. Doctoral Theses from the USC Brain Project (May 1997–August 2) 384
Index 386