Computational Models for Neuroscience : Human Cortical Information Processing

Robert Hecht-Nielsen

ISBN 10: 1852335939 / ISBN 13: 9781852335939

Editore: Springer London Dez 2002, 2002

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Neuware - Understanding how the human brain represents, stores, and processes information is one of the greatest unsolved mysteries of science today. The cerebral cortex is the seat of most of the mental capabilities that distinguish humans from other animals and, once understood, it will almost certainly lead to a better knowledge of other brain nuclei. Although neuroscience research has been underway for 150 years, very little progress has been made. What is needed is a key concept that will trigger a full understanding of existing information, and will also help to identify future directions for research. This book aims to help identify this key concept. Including contributions from leading experts in the field, it provides an overview of different conceptual frameworks that indicate how some pieces of the neuroscience puzzle fit together. It offers a representative selection of current ideas, concepts, analyses, calculations and computer experiments, and also looks at important advances such as the application of new modeling methodologies. Computational Models for Neuroscience will be essential reading for anyone who needs to keep up-to-date with the latest ideas in computational neuroscience, machine intelligence, and intelligent systems. It will also be useful background reading for advanced undergraduates and postgraduates taking courses in neuroscience and psychology.

Codice articolo 9781852335939

Dati bibliografici

• Titolo: Computational Models for Neuroscience : Human Cortical Information Processing
• Editore: Springer London Dez 2002
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 1852335939
• ISBN 13: 9781852335939
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 240 L x 162 A x 22 P (millimetri) 240 millimetri di larghezza per 162 millimetri di altezza per 22 millimetri di profondità
• Peso: 617 grammi

Descrizione articolo

Riassunto

Formal study of neuroscience (broadly defined) has been underway for millennia. For example, writing 2,350 years ago, Aristotle! asserted that association - of which he defined three specific varieties - lies at the center of human cognition. Over the past two centuries, the simultaneous rapid advancements of technology and (conse­ quently) per capita economic output have fueled an exponentially increasing effort in neuroscience research. Today, thanks to the accumulated efforts of hundreds of thousands of scientists, we possess an enormous body of knowledge about the mind and brain. Unfortunately, much of this knowledge is in the form of isolated factoids. In terms of "big picture" understanding, surprisingly little progress has been made since Aristotle. In some arenas we have probably suffered negative progress because certain neuroscience and neurophilosophy precepts have clouded our self-knowledge; causing us to become largely oblivious to some of the most profound and fundamental aspects of our nature (such as the highly distinctive propensity of all higher mammals to automatically seg­ ment all aspects of the world into distinct holistic objects and the massive reorganiza­ tion of large portions of our brains that ensues when we encounter completely new environments and life situations). At this epoch, neuroscience is like a huge collection of small, jagged, jigsaw puz­ zle pieces piled in a mound in a large warehouse (with neuroscientists going in and tossing more pieces onto the mound every month).

Contenuti

1 The Neurointeractive Paradigm: Dynamical Mechanics and the Emergence of Higher Cortical Function.- 1.1 Abstract.- 1.2 Introduction.- 1.3 Principles of Cortical Neurointeractivity.- 1.4 Dynamical Mechanics.- 1.5 The Neurointeractive Cycle.- 1.6 Developmental Emergence.- 1.7 Explaining Emergence.- 1.8 References.- 2 The Cortical Pyramidal Cell as a Set of Interacting Error Backpropagating Dendrites: Mechanism for Discovering Nature ’s Order.- 2.1 Abstract.- 2.2 Introduction.- 2.2.1 Defining the Problem.- 2.2.2 How Does the Brain Discover Orderly Relations?.- 2.3 Implementation of the Proposal.- 2.3.1 How Might Error Backpropagation Learning Be Implemented in Dendrites?.- 2.3.2 How Can Dendrites Be Set Up to Teach Each Other?.- 2.3.3 How to Divide Connections Among the Dendrites?.- 2.4 Cortical Minicolumnar Organization and SINBAD Neurons.- 2.5 Associationism.- 2.5.1 SINBAD as an Associationist Theory.- 2.5.2 Countering Nativist Arguments.- 2.6 Acknowledgements.- References.- 3 Performance of Intelligent Systems Governed by Internally Generated Goals.- 3.1 Abstract.- 3.2 Introduction.- 3.3 Perception as an Active Process.- 3.4 Nonlinear Dynamics of the Olfactory System.- 3.5 Chaotic Oscillations During Learning Novel Stimuli.- 3.6 Generalization and Consolidation of New Perceptions with Context.- 3.7 The Central Role of the Limbic System.- 3.8 Conclusions.- 3.9 Acknowledgements.- References.- 4 A Theory of Thalamocortex.- 4.1 Abstract.- 4.2 Active Neurons.- 4.3 Neuronal Connections within Thalamocortex.- 4.4 Cortical Regions.- 4.5 Feature Artractor Associative Memory Neural Network.- 4.6 Antecedent Support Associative Memory Neural Network.- 4.7 Hierarchical Abstractor Associative Memory Neural Network.- 4.8 Consensus Building.- 4.9 Brain Command Loop.- 4.10 Testing this Theory.- 4.11 Acknowledgements.- Appendix A: Sketch of an Analysis of the Simplified Feature Artractor Associative Memory Neural Network.- Appendix B: Experiments with a Simplified Antecedent Support Associative Memory Neural Network.- Appendix C: An Experiment with Consensus Building.- References.- 5 Elementary Principles of Nonlinear Synaptic Transmission.- 5.1 Abstract.- 5.2 Introduction.- 5.3 Frequency-dependent Synaptic Transmission.- 5.4 Nonlinear Synapses Enable Temporal Integration.- 5.5 Temporal Information.- 5.6 Packaging Temporal Information.- 5.7 Size of Temporal Information Packages.- 5.8 Classes of Temporal Information Packages.- 5.9 Emergence of the Population Signal.- 5.10 Recurrent Neural Networks.- 5.11 Combining Temporal Information in Recurrent Networks.- 5.12 Organization of Synaptic Parameters.- 5.13 Learning Dynamics, Learning to Predict.- 5.14 Redistribution of Synaptic Efficacy.- 5.15 Optimizing Synaptic Prediction.- 5.16 A Nested Learning Algorithm.- 5.17 Retrieving Memories from Nonlinear Synapses.- 5.18 Conclusion.- 5.19 Acknowledgements.- Appendix A: Sherrington ’s Leap.- Appendix B: Functional Significance.- Appendix C: Visual Patch Recordings.- Appendix D: Biophysical Basis of Parameters.- Appendix E: Single Connection, Many Synapses.- Appendix F: The Model.- Appendix G: Synaptic Classes.- Appendix H: Paired Pulses.- Appendix I: Digitization of Synaptic Parameters.- Appendix J: Steady State.- Appendix K: Inhibitory Synapses.- Appendix L: Lack of Boundaries.- Appendix M: Speed of RI Accumulation.- Appendix N: Network Efficiency.- Appendix O: The Binding Problem of the Binding Problem.- References.- 6 The Development of Cortical Models to Enable Neural-based Cognitive Architectures.- 6.1 Introduction.- 6.1.1 Computational Neuroscience Paradigms and Predictions.- 6.2 The Challenge of Cognitive Architectures.- 6.2.1 General Cognitive Skills.- 6.2.2 A Survey of Current Cognitive Architectures.- 6.2.3 Assumptions and Limitations of Current Cognitive Architectures.- 6.3 The Prospects for a Neural-based Cognitive Architecture.- 6.3.1 Limitations of Artificial Neural Networks.- 6.3.2 Biological Networks Emerging from Computational Neuroscience: Sensory and Motor Modules.- 6.3.3 Forebrain Systems Supporting Cortical Function.- 6.4 Elements of a General Cortical Model.- 6.4.1 Single Neuron Models or Processor Elements.- 6.4.2 Microcircuitry.- 6.4.3 Dynamic Synaptic Connectivity.- 6.4.4 Ensemble Dynamics and Coding.- 6.4.5 Transient Coherent Structures and Cognitive Dynamics.- 6.5 Promising Models and their Capabilities.- 6.5.1 Biologically Based Cortical Systems.- 6.5.2 A Cortical System Based on Neurobiology, Biological Principles and Mathematical Analysis: Cortronics.- 6.5.3 Connectionist Architectures with Biological Principles: The Convergence of Cognitive Science and Computational Neuroscience.- 6.6 The Challenges of Demonstrating Cognitive Ability.- 6.6.1 Robotics and Autonomous Systems.- 6.7 Co-development Strategies for Automated Systems and Human Performers.- 6.8 Acknowledgements.- References.- 7 The Behaving Human Neocortex as a Dynamic Network of Networks.- 7.1 Abstract.- 7.2 Neural Organization Across Scales.- 7.3 Network of Networks (NoN) Model.- 7.3.1 Architecture.- 7.3.2 Model Formulation.- 7.3.3 NoN Properties.- 7.3.4 NoN Contributions.- 7.4 Neurobiological Predicatability and Falsifiability.- 7.5 Implications for Neuroengineering.- 7.6 Concluding Remarks.- 7.7 Acknowledgements.- References.- 8 Towards Global Principles of Brain Processing.- 8.1 Abstract.- 8.2 Introduction.- 8.3 What Could Brain Principles Look Like?.- 8.4 Structural Modeling.- 8.5 Static Activation Study Results.- 8.6 The Motion After-Effect (MAE).- 8.7 The Three-Stage Model of Consciousness.- 8.8 The CODAM Model of Consciousness.- 8.9 Principles of the Global Brain.- 8.10 The Thinking Brain.- 8.11 Discussion.- 8.12 Acknowledgement.- References.- 9 The Neural Networks for Language in the Brain: Creating LAD.- 9.1 Abstract.- 9.2 Introduction.- 9.3 The ACTION Net Model of TSSG.- 9.4 Phrase Structure Analyzers.- 9.5 Generativity of the Adjectival Phrase Analyzer.- 9.6 Complexity of Phrase Structure Analysis.- 9.7 Future Directions in the Construction of LAD.- 9.8 Conclusions.- References.- 10 Cortical Belief Networks.- 10.1 Abstract.- 10.1 Introduction.- 10.1 An Example.- 10.1 Representing Distributions in Populations.- 10.1 Basis Function Representations.- 10.1 Generative Representations.- 10.1 Standard Bayesian Approach.- 10.1 Distributional Population Coding.- 10.1 Applying Distributional Population Coding.- 10.1.1 Population Analysis.- 10.1.1 Decoding Transparent Motion.- 10.1.1 Decision Noise.- 10.1.1 Lateral Interactions.- 10.1 Cortical Belief Network.- 10.1 Discussion.- 10.1 Acknowledgements.- 10.1 References.