Computational Models for Neuroscience – Human Cortical Information Processing

Abstract

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 Neurosci