Brain-mind machinery : brain-inspired computing and mind opening

Brain and mind continue to be a topic of enormous scientific interest. With the recent advances in measuring instruments such as two-photon laser scanning microscopy and fMRI, the neuronal connectivity and circuitry of how the brain's various regions are hierarchically interconnected and organized are better understood now than ever before. By reverse engineering the brain, computer scientists hope to build cognitively intelligent systems that will revolutionize the artificial intelligence paradigm. Brain-Mind Machinery provides a walkthrough to the world of brain-inspired computing and mind-related questions. Bringing together diverse viewpoints and expertise from multidisciplinary communities, the book explores the human quest to build a thinking machine with human-like capabilities. Readers will acquire a first-hand understanding of the brain and mind mechanisms and machineries, as well as how much we have progressed in and how far we are from building a truly general intelligent system like the human brain. Contents: The Brain: The Center of Attraction; Neurons and Synapses: The Key to Memory and Learning; The Cortex Architecture: The Building Block of Intelligence; Many Faces of Memories -- Investigating the Human Multiple Memory Systems; Learning Like a Human: How Does Learning Take Place in Our Brain?; Emotion and Cognition; Laminar Computing; Probabilistic Computing: The Bayesian Mind; Thinking Machine: Higher Theories of Brain and Commonsense Knowledge Generation; Modeling the Entire Brain: Biologically Inspired Cognitive Architectures; Are We There? What Can the Computer Do Today and Tomorrow?; Brain -- A Forest Not Totally Explored: What are Some of the Issues?; Understanding the Brain to Build Intelligent Systems; Conclusions -- The Mind That Matters. CONTENTS 12 Preface 6 Acknowledgement 10 Chapter 1: The Brain: The Center of Attraction 14 How is the Brain Organized? Specificity Implies Regionalization and Specialization 15 How Does the Brain Wire Itself? 24 What is the “computational power” of the brain? 26 Summary 29 Chapter 2: Neurons and Synapses: The Key to Memory and Learning 31 How Do Neurons Communicate and Affect Our Learning and Memory? 33 How Does Synaptic Plasticity Give Rise to Learning and Memory? 44 Modeling the Neurons and Synapses 47 Chapter 3: The Cortex Architecture: The Building Block of Intelligence 50 How is the Cortex Structurally Arranged? 51 Other Design Factors in the Cortex 56 Computational Model of the Cortex Architecture — Model After the Hierarchical Structure 59 Case Study — Why the Brain Can Discriminate and Recognize Pictures in a Glance 68 How Does the Prefrontal Cortex Perform Decision-Making? 71 Summary 73 Chapter 4: Many Faces of Memories — Investigating the Human Multiple Memory Systems 75 Memory Systems Based on Information Storage Time 76 Memory Systems Based on the Type of Information Stored 80 Declarative Memory 82 Semantic Memory 82 Episodic Memory 82 Non-Declarative Memory 83 Role of Hippocampus in Memory 85 Let Us Sleep Over It 87 Interesting Observation of Human Memory 88 Human Memory in Chronological Age 91 Summary 92 Chapter 5: Learning Like a Human: How Does Learning Take Place in Our Brain? 94 How Do Humans Learn? 95 How Plasticity and Stability Give Rise to Learning 96 Forms of Human Learning 99 Perceptual Learning 100 Stimulus-Response Learning (S-R Learning) 105 Computational Techniques Based on S-R Learning 107 Motor Learning 108 Relational Learning 110 Dopamine’s Role in Learning 111 Cognitive Learning 112 Summary 115 Chapter 6: Emotion and Cognition 117 Introduction 117 What are the Emotions? 119 What is the Possible Emotional Circuitry in the Brain? 120 What is Emotional State? 129 How Can We Model Emotion and Cognition? 133 How Do Emotions Affect Our Cognitive Abilities? 139 Conclusions 140 Chapter 7: Laminar Computing 142 What, How, and Why Laminar Computing? 142 The Neocortex has a Laminar Pattern 143 Top Down, Bottom-Up, and Horizontal Interactions 148 Shunting Networks 151 Folded Feedback 153 Complementary Properties 155 The “Drum-up” to Laminar Computing 157 From Vision to Cognition 164 Chapter 8: Probabilistic Computing: The Bayesian Mind 166 Probabilistic Model of Cognitive Process 168 Bayesian Networks 173 Conclusions 175 Chapter 9: Thinking Machine: Higher Theories of Brain and Commonsense Knowledge Generation 176 Can a Machine Think and Have a Mind? 178 Multiple Layers of Machinery 179 Multiple Intelligences 181 Triarchic Theory of Intelligence 183 Conceptual Blending Theory 185 Commonsense Knowledge Representation 186 The Wise Machine — AI with Wisdom 189 Summary 193 Chapter 10: Modeling the Entire Brain: Biologically Inspired Cognitive Architectures 195 Integrated Cognitive Architecture 198 SOAR Architecture 200 ACT-R Architecture 203 ICARUS Architecture 206 BDI Architecture 209 Subsumption Architecture 212 CLARION Architecture 214 Functional Comparison of the Six Cognitive Architectures 217 Conclusions 221 Chapter 11: Are We There? What Can the Computer Do Today and Tomorrow? 223 Data and Communication Capability 223 Physical Capabilitya 224 Vision Capability 226 Artistic Capability 226 Mental Capability 227 General Task Capabilityb 229 Could a Machine Pass the Turing Test? 230 Chapter 12: Brain — A Forest Not Totally Explored: What are Some of the Issues? 235 Many Theories and Views of How the Brain and Mind Work 236 Open Issues 239 Measuring Instruments in Neuroscience 243 Combination of Instruments 253 Lesion Method 254 Conclusions 255 Chapter 13: Understanding the Brain to Build Intelligent Systems 257 What are the Design Principles of the Brain? 258 Dual Augmentation Strategy 265 Computational Power 267 Conclusion 267 Chapter 14: Conclusions — The Mind That Matters 268 What are Theories of the Mind? 269 The Mind that Matters 273 Glossary 276 Notes 288 References 346 Index 378