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Computational Models for Neuroscience: Human Cortical Information Processing 2003 ed. [Kõva köide]

  • Formaat: Hardback, 299 pages, kaal: 631 g, 27 Illustrations, black and white; XIX, 299 p. 27 illus., 1 Hardback
  • Ilmumisaeg: 03-Dec-2002
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852335939
  • ISBN-13: 9781852335939
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  • Formaat: Hardback, 299 pages, kaal: 631 g, 27 Illustrations, black and white; XIX, 299 p. 27 illus., 1 Hardback
  • Ilmumisaeg: 03-Dec-2002
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852335939
  • ISBN-13: 9781852335939
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).
Contributors xvii
The Neurointeractive Paradigm: Dynamical Mechanics and the Emergence of Higher Cortical Function
1(24)
Abstract
1(1)
Introduction
2(2)
Principles of Cortical Neurointeractivity
4(5)
Dynamical Mechanics
9(4)
The Neurointeractive Cycle
13(5)
Developmental Emergence
18(3)
Explaining Emergence
21(1)
References
22(3)
The Cortical Pyramidal Cell as a Set of Interacting Error Backpropagating Dendrites: Mechanism for Discovering Nature's Order
25(40)
Abstract
25(1)
Introduction
26(5)
Defining the Problem
27(1)
How Does the Brain Discover Orderly Relations?
28(3)
Implementation of the Proposal
31(21)
How Might Error Backpropagation Learning Be Implemented in Dendrites?
31(5)
How Can Dendrites Be Set Up to Teach Each Other?
36(6)
How to Divide Connections Among the Dendrites?
42(10)
Cortical Minicolumnar Organization and SINBAD Neurons
52(4)
Associationism
56(4)
SINBAD as an Associationist Theory
56(1)
Countering Nativist Arguments
57(3)
Acknowledgements
60(5)
References
60(5)
Performance of Intelligent Systems Governed by Internally Generated Goals
65(20)
Abstract
65(1)
Introduction
65(2)
Perception as an Active Process
67(4)
Nonlinear Dynamics of the Olfactory System
71(3)
Chaotic Oscillations During Learning Novel Stimuli
74(2)
Generalization and Consolidation of New Perceptions with Context
76(2)
The Central Role of the Limbic System
78(3)
Conclusions
81(1)
Acknowledgements
82(3)
References
82(3)
A Theory of Thalamocortex
85(40)
Abstract
85(1)
Active Neurons
85(1)
Neuronal Connections within Thalamocortex
86(1)
Cortical Regions
87(1)
Feature Attractor Associative Memory Neural Network
87(7)
Antecedent Support Associative Memory Neural Network
94(5)
Hierarchical Abstractor Associative Memory Neural Network
99(5)
Consensus Building
104(3)
Brain Command Loop
107(3)
Testing this Theory
110(1)
Acknowledgements
111(14)
Appendix A: Sketch of an Analysis of the Simplified Feature Attractor Associative Memory Neural Network
112(3)
Appendix B: Experiments with a Simplified Antecedent Support Associative Memory Neural Network
115(3)
Appendix C: An Experiment with Consensus Building
118(2)
References
120(5)
Elementary Principles of Nonlinear Synaptic Transmission
125(46)
Abstract
125(1)
Introduction
125(3)
Frequency-dependent Synaptic Transmission
128(2)
Nonlinear Synapses Enable Temporal Integration
130(2)
Temporal Information
132(2)
Packaging Temporal Information
134(1)
Size of Temporal Information Packages
135(2)
Classes of Temporal Information Packages
137(4)
Emergence of the Population Signal
141(2)
Recurrent Neural Networks
143(1)
Combining Temporal Information in Recurrent Networks
144(2)
Organization of Synaptic Parameters
146(1)
Learning Dynamics, Learning to Predict
147(1)
Redistribution of Synaptic Efficacy
148(2)
Optimizing Synaptic Prediction
150(2)
A Nested Learning Algorithm
152(1)
Retrieving Memories from Nonlinear Synapses
153(1)
Conclusion
154(1)
Acknowledgements
155(16)
Appendix A: Sherrington's Leap
156(1)
Appendix B: Functional Significance
156(2)
Appendix C: Visual Patch Recordings
158(1)
Appendix D: Biophysical Basis of Parameters
158(1)
Appendix E: Single Connection, Many Synapses
159(1)
Appendix F: The Model
159(2)
Appendix G: Synaptic Classes
161(1)
Appendix H: Paired Pulses
161(1)
Appendix I: Digitization of Synaptic Parameters
162(1)
Appendix J: Steady State
162(1)
Appendix K: Inhibitory Synapses
163(1)
Appendix L: Lack of Boundaries
163(1)
Appendix M: Speed of RI Accumulation
164(1)
Appendix N: Network Efficiency
164(1)
Appendix O: The Binding Problem of the Binding Problem
164(1)
References
165(6)
The Development of Cortical Models to Enable Neural-based Cognitive Architectures
171(34)
Introduction
171(2)
Computational Neuroscience Paradigms and Predictions
172(1)
The Challenge of Cognitive Architectures
173(11)
General Cognitive Skills
173(2)
A Survey of Current Cognitive Architectures
175(8)
Assumptions and Limitations of Current Cognitive Architectures
183(1)
The Prospects for a Neural-based Cognitive Architecture
184(2)
Limitations of Artificial Neural Networks
184(1)
Biological Networks Emerging from Computational Neuroscience: Sensory and Motor Modules
184(1)
Forebrain Systems Supporting Cortical Function
184(2)
Elements of a General Cortical Model
186(3)
Single Neuron Models or Processor Elements
186(1)
Microcircuitry
186(1)
Dynamic Synaptic Connectivity
187(1)
Ensemble Dynamics and Coding
188(1)
Transient Coherent Structures and Cognitive Dynamics
189(1)
Promising Models and their Capabilities
189(6)
Biologically Based Cortical Systems
189(3)
A Cortical System Based on Neurobiology, Biological Principles and Mathematical Analysis: Cortronics
192(1)
Connectionist Architectures with Biological Principles: The Convergence of Cognitive Science and Computational Neuroscience
193(2)
The Challenges of Demonstrating Cognitive Ability
195(1)
Robotics and Autonomous Systems
196(1)
Co-development Strategies for Automated Systems and Human Performers
196(1)
Acknowledgements
197(8)
References
197(8)
The Behaving Human Neocortex as a Dynamic Network of Networks
205(16)
Abstract
205(1)
Neural Organization Across Scales
206(3)
Network of Networks (NoN) Model
209(5)
Architecture
209(2)
Model Formulation
211(1)
NoN Properties
212(2)
NoN Contributions
214(1)
Neurobiological Predicatability and Falsifiability
214(1)
Implications for Neuroengineering
215(1)
Concluding Remarks
216(1)
Acknowledgements
217(4)
References
217(4)
Towards Global Principles of Brain Processing
221(24)
Abstract
221(1)
Introduction
221(1)
What Could Brain Principles Look Like?
222(2)
Structural Modeling
224(2)
Static Activation Study Results
226(1)
The Motion After-Effect (MAE)
227(2)
The Three-Stage Model of Consciousness
229(4)
The CODAM Model of Consciousness
233(2)
Principles of the Global Brain
235(2)
The Thinking Brain
237(2)
Discussion
239(1)
Acknowledgement
240(5)
References
240(5)
The Neural Networks for Language in the Brain: Creating LAD
245(22)
Abstract
245(1)
Introduction
245(4)
The Action Net Model of TSSG
249(3)
Phrase Structure Analyzers
252(4)
Generativity of the Adjectival Phrase Analyzer
256(3)
Complexity of Phrase Structure Analysis
259(1)
Future Directions in the Construction of LAD
259(2)
Conclusions
261(6)
References
262(5)
Cortical Belief Networks
267(26)
Abstract
267(1)
Introduction
267(1)
An Example
268(2)
Representing Distributions in Populations
270(1)
Basis Function Representations
271(1)
Generative Representations
272(1)
Standard Bayesian Approach
272(1)
Distributional Population Coding
273(1)
Applying Distributional Population Coding
274(8)
Population Analysis
274(1)
Decoding Transparent Motion
274(3)
Decision Noise
277(3)
Lateral Interactions
280(2)
Cortical Belief Network
282(2)
Discussion
284(1)
Acknowledgements
285(1)
References
285(8)
Index 293