Computational models for neuroscience: human cortical information processing

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.