Computing the Brain: A Guide to Neuroinformatics
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Table of contents
- Computing the Brain: A Guide to Neuroinformaticsiii
- Copyright Pageiv
- Contentsv
- Contributorsix
- Prefacexi
- Part 1: Introduction1
- Chapter 1.1. NeuroInformatics: The Issues3
- 1.1.1 Overview3
- 1.1.2 Modeling and Simulation8
- 1.1.3 Databases for Neuroscience Time Series14
- 1.1.4 Visualization and Atlas-Based Databases17
- 1.1.5 Data Management and Summary Databases19
- 1.1.6 The NeuroInformatics Workbench26
- References27
- Chapter 1.2. Introduction to Databases29
- Abstract29
- 1.2.1 An Overview of Database Management29
- 1.2.2 Historical View of Key Database Developments30
- 1.2.3 Relational Database Model31
- 1.2.4 SQL32
- 1.2.5 Object-Based Database Models33
- 1.2.6 Object-Relational Database Model37
- 1.2.7 An Overview of Federated Database Systems38
- References39
- Part 2: Modeling and Simulation41
- Chapter 2.1. Modeling the Brain43
- Abstract43
- 2.1.1 Modeling Issues43
- 2.1.2 Parietal-Premotor Interactions in the Control of Grasping46
- 2.1.3 Basal Ganglia48
- 2.1.4 Cerebellum53
- 2.1.5 Hippocampus, Parietal Cortex, and Navigation61
- 2.1.6 Discussion67
- References67
- Chapter 2.2. NSL Neural Simulation Language71
- 2.2.1 Modeling and Simulation of Neural Networks72
- 2.2.2 NSL Modules and Simulation74
- 2.2.3 The NSL System83
- 2.2.4 Simulating a Model„The Maximum Selector Model85
- 2.2.5 Maximum Selector Model85
- 2.2.6 Available Resources89
- References89
- Chapter 2.3. EONS: A Multi-Level Modeling System and Its Applications91
- 2.3.1 Introduction91
- 2.3.2 EONS Object Library92
- 2.3.3 Protocol-Based Simulation98
- 2.3.4 Conclusion100
- References100
- Chapter 2.4. Brain Imaging and Synthetic PET103
- Abstract103
- 2.4.1 PET Imaging and Neurophysiology103
- 2.4.2 Defining Synthetic PET104
- 2.4.3 Example: A Model of Saccadic Eye Movements105
- 2.4.4 Synthetic PET for Grasp Control107
- 2.4.5 Discussion111
- References112
- Part 3: Databases for Neuroscience Time Series115
- Chapter 3.1. Repositories for the Storage of Experimental Neuroscience Data117
- 3.1.1 Introduction117
- 3.1.2 Protocols: A Data Model To Address Schema Complexity in Neuroscience118
- 3.1.3 Considerations of the User Community119
- 3.1.4 Building a Time-Series Database for In Vivo Neurophysiology: Cerebellum and Classical Conditio120
- 3.1.5 Building a Time-Series Database for In Vitro Neurophysiology: Long-Term Potentiation (LTP) in125
- 3.1.6 Building a Database for Human Neuroimaging Data129
- 3.1.7 Discussion131
- References132
- Chapter 3.2. Design Concepts for NeuroCore and NeuroScience Databases135
- 3.2.1 Design Concepts for Neuroscience Databases135
- 3.2.2 The Three Main Components of the NeuroCore Database135
- 3.2.3 Detailed Description of the NeuroCore Database137
- Conclusion150
- References150
- Chapter 3.3. User Interaction with NeuroCore151
- 3.3.1 Introduction151
- 3.3.2 NeuroCore Schema Browser153
- 3.3.3 Java Applet for Data Entry (JADE)156
- 3.3.4 Database Browser157
- 3.3.5 DataMunch160
- 3.3.6 Discussion161
- References163
- Part 4: ATLAS-BASED DATABASES165
- Chapter 4.1. Interactive Brain Maps and Atlases167
- Abstract167
- 4.1.1 General Features of Maps167
- 4.1.2 Overall Structure of the Brain168
- 4.1.3 Experimental Circuit-Tracing Methods169
- 4.1.4 Atlases: Slice-Based Sampling and Standard Brains169
- 4.1.5 Transferring Data from Experimental Brain to Standard (Atlas Reference) Brain173
- 4.1.6 Toward Textual and Graphical Databases on the Web174
- 4.1.7 Three-Dimensional Computer Graphics Models of the Brain174
- 4.1.8 Two-Dimensional Flatmaps: Schematic Circuit Diagrams and Distribution Patterns176
- 4.1.9 The Future: Atlases as Expandable Databases and Models176
- References177
- Chapter 4.2. Perspective: Geographical Information Systems179
- Abstract179
- 4.2.1 Introduction179
- 4.2.2 Overview of GIS180
- 4.2.3 Atlas-Based Neuroscientific Data184
- 4.2.4 Raster Data185
- 4.2.5 Conclusion and Web Resources186
- References187
- Chapter 4.3. The Neuroanatomical Rat Brain Viewer (NeuARt)189
- 4.3.1 Introduction189
- 4.3.2 The NeuARt System190
- 4.3.3 Discussion200
- References200
- Chapter 4.4. Neuro Slicer: A Tool for Registering 2-D Slice Data to 3-D Surface Atlases203
- Abstract203
- 4.4.1 Introduction203
- 4.4.2 Classification Criteria204
- 4.4.3 Intrasubject Image Matching205
- 4.4.4 Intersubject Image Matching206
- 4.4.5 Neuro Slicer: USCBP Histological Registration Tool208
- References213
- Chapter 4.5. An Atlas-Based Database of Neurochemical Data217
- Abstract217
- 4.5.1 Synaptic Neurotransmission: Molecular and Functional Aspects217
- 4.5.2 Roles of Glutamatergic Synapses in LTP and LTD218
- 4.5.3 Glutamate Receptor Regulation and Synaptic Plasticity218
- 4.5.4 How To Build a Useful Neurochemical Database219
- 4.5.5 Incorporating Neurochemical Data into the NeuroCore Repository of Empirical Data222
- 4.5.6 Available Resources225
- 4.5.7 Conclusion227
- References227
- Part 5: Data Management229
- Chapter 5.1. Federating Neuroscience Databases231
- Abstract231
- 5.1.1 Introduction231
- 5.1.2 Information Discovery232
- 5.1.3 Semantic Heterogeneity Resolution233
- 5.1.4 System-Level Interconnection233
- 5.1.5 Characteristics of Sharing Patterns234
- 5.1.6 System Architecture for Sharing Primitives/Tools236
- 5.1.7 Federating Neuroscience Databases237
- References238
- Chapter 5.2. Dynamic Classification Ontologies241
- Abstract241
- 5.2.1 Introduction241
- 5.2.2 Heterogeneity242
- 5.2.3 Common Ontology244
- 5.2.4 Classification246
- 5.2.5 Dynamic Classification Ontology247
- 5.2.6 Mediators for Information Sharing250
- 5.2.7 Conclusions253
- References254
- Chapter 5.3. Annotator: Annotation Technology for the WWW255
- Abstract255
- 5.3.1 Introduction255
- 5.3.2 Overview of Existing Annotation Software256
- 5.3.3 Annotation Technology: An Integrative Approach256
- 5.3.4 Annotator261
- References263
- Chapter 5.4. Management of Space in Hierarchical Storage Systems265
- Abstract265
- 5.4.1 Introduction265
- 5.4.2 Target Environment268
- 5.4.3 Four Alternative Space Management Techniques269
- 5.4.4 Performance Evaluation276
- 5.4.5 Analytical Models280
- 5.4.6 Conclusions283
- References283
- Part 6: Summary Databases and Model Repositories285
- Chapter 6.1. Summary Databases and Model Repositories287
- Abstract287
- 6.1.1 The Database Typology and the NeuroInformatics Workbench287
- 6.1.2 An Overall Perspective288
- 6.1.3 General Considerations on Model Repositories291
- 6.1.4 Brain Models on the Web291
- 6.1.5 NeuroScholar292
- 6.1.6 NeuroHomology294
- 6.1.7 Future Plans296
- Chapter 6.2. Brain Models on the Web and the Need for Summary Data297
- Abstract297
- 6.2.1 Storing Brain Models297
- 6.2.2 The BMW-SDB Relationship298
- 6.2.3 Reviewing a Model: The Dart Model of Prism Adaptation299
- 6.2.4 Database Design300
- 6.2.5 Accessing the Database308
- 6.2.6 Future Plans316
- References317
- Chapter 6.3. Knowledge Mechanics and the Neuroscholar Project: A New Approach to Neuroscientific The319
- 6.3.1 An Introduction to Knowledge Mechanics319
- 6.3.2 Concept of TheoryŽ in Neuroscience320
- 6.3.3 High-Level Software Requirements and Fundamental Design Concepts of the NeuroScholar System321
- 6.3.4 Neuroscholar in Detail327
- 6.3.5 The Significance of Knowledge Mechanics333
- References334
- Chapter 6.4. The NeuroHomology Database337
- 6.4.1 Introduction: The Definition of the Concept of Homology in Neurobiology337
- 6.4.2 Theory of Degrees of Homology339
- 6.4.3 The NeuroHomology Database: Description340
- 6.4.4 The NeuroHomology Database: Brain Structures341
- 6.4.5 NeuroHomology Database: Connectivity Issues343
- 6.4.6 The NeuroHomology Database: Homologies346
- 6.4.7 Conclusion and Future Development348
- References350
- Appendices353
- Appendix A1. Introduction to Informix355
- Informix SQL Tutorial: A Practical Example355
- Appendix A2. NeuroCore Timeseries Datablade359
- Introduction359
- Internal Structure and Description359
- Support Functions360
- Additional SQL-Invoked Routines360
- Comparing Data360
- Discussion361
- Appendix A3. USCBP Development Team363
- Appendix B1. Informix SQL Quick Reference365
- Introduction365
- SQL Statements365
- Appendix C1. USC Brain Project Research Personnel367
- Appendix C2. Doctoral Theses from the USC Brain Project (May 1997–August 2)369
- Index371
Computing the Brain provides readers with an integrated view of current informatics research related to the field of neuroscience. This book clearly defines the new work being done in neuroinformatics and offers information on resources available on the Web to researchers using this new technology. It contains chapters that should appeal to a multidisciplinary audience with introductory chapters for the nonexpert reader. Neuroscientists will find this book an excellent introduction to informatics technologies and the use of these technologies in their research. Computer scientists will be interested in exploring how these technologies might benefit the neuroscience community.
Key Features
* An integrated view of neuroinformatics for a multidisciplinary audience
* Explores and explains new work being done in neuroinformatics
* Cross-disciplinary with chapters for computer scientists and neuroscientists
* An excellent tool for graduate students coming to neuroinformatics research from diverse disciplines and for neuroscientists seeking a comprehensive introduction to the subject
* Discusses, in-depth, the structuring of masses of data by a variety of computational models
* Clearly defines computational neuroscience - the use of computational techniques and metaphors to investigate relations between neural structure and function
* Offers a guide to resources and algorithms that can be found on the Web
* Written by internationally renowned experts in the field
Key Features
* An integrated view of neuroinformatics for a multidisciplinary audience
* Explores and explains new work being done in neuroinformatics
* Cross-disciplinary with chapters for computer scientists and neuroscientists
* An excellent tool for graduate students coming to neuroinformatics research from diverse disciplines and for neuroscientists seeking a comprehensive introduction to the subject
* Discusses, in-depth, the structuring of masses of data by a variety of computational models
* Clearly defines computational neuroscience - the use of computational techniques and metaphors to investigate relations between neural structure and function
* Offers a guide to resources and algorithms that can be found on the Web
* Written by internationally renowned experts in the field