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