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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010343862 | QP357.5 B35 2015 | Open Access Book | Book | Searching... |
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Summary
Summary
The vast differences between the brain's neural circuitry and a computer's silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction.
Drawing on several decades of progress in computational neuroscience, together with recent results in Bayesian and reinforcement learning methodologies, Ballard factors the brain's principal computational issues in terms of their natural place in an overall hierarchy. Each of these factors leads to a fresh perspective. A neural level focuses on the basic forebrain functions and shows how processing demands dictate the extensive use of timing-based circuitry and an overall organization of tabular memories. An embodiment level organization works in reverse, making extensive use of multiplexing and on-demand processing to achieve fast parallel computation. An awareness level focuses on the brain's representations of emotion, attention and consciousness, showing that they can operate with great economy in the context of the neural and embodiment substrates.
Author Notes
Dana H. Ballard is Professor in the Department of Computer Sciences at the University of Texas at Austin, where he has appointments in Psychology, the Institute for Neuroscience, and the Center for Perceptual Systems. He is the author of An Introduction to Natural Computation (MIT Press.)
Table of Contents
Series Foreword | p. ix |
Preface | p. xi |
Acknowledgments | p. xiii |
Part 1 Setting the Stage | p. 1 |
1 Brain Computation | p. 3 |
1.1 Introducing the Brain | p. 7 |
1.2 Computational Abstraction | p. 13 |
1.3 Different than Silicon | p. 21 |
1.4 The Brain's Tricks for Fast Computation | p. 25 |
1.5 More Powerful than a Computer? | p. 30 |
1.6 Do Humans Have Non-Turing Abilities? | p. 34 |
1.7 Summary | p. 38 |
2 Brain Overview | p. 41 |
2.1 Spinal Cord and Brainstem | p. 44 |
2.2 The Forebrain: An Overview | p. 54 |
2.3 Cortex: Long-Term Memory | p. 60 |
2.4 Basal Ganglia: The Program Sequencer | p. 63 |
2.5 Thalamus: Input and Output | p. 68 |
2.6 Hippocampus: Program Modifications | p. 70 |
2.7 Amygdal: Rating what's Important | p. 76 |
2.8 How the Brain Programs itself | p. 78 |
2.9 Summary | p. 80 |
Part 2 Neurons, Circuits, and Subsystems | p. 81 |
3 Neurons and Circuits | p. 83 |
3.1 Signaling Strategies | p. 85 |
3.2 Receptive Fields | p. 89 |
3.3 Modeling Receptive Field Formation | p. 95 |
3.4 Spike Codes for Cortical Neurons | p. 102 |
3.5 Reflexive Behaviors | p. 109 |
3.6 Summary | p. 112 |
3.7 Appendix: Neuron Behaviors | p. 109 |
4 Cortical Memory | p. 127 |
4.1 Table Lookup Strategies | p. 128 |
4.2 The Cortical Map Concept | p. 135 |
4.3 Hierarchies of Maps | p. 139 |
4.4 What Does the Cortex Represent? | p. 146 |
4.5 Computational Models | p. 154 |
4.6 Summary | p. 160 |
5 Programs via Reinforcement | p. 163 |
5.1 Evaluating a Program | p. 168 |
5.2 Reinforcement Learning Algorithms | p. 173 |
5.3 Learning in the Basal Ganglia | p. 177 |
5.4 Learning to Set Cortical Synapses | p. 186 |
5.5 Learning to Play Backgammon | p. 192 |
5.6 Backgammon as an Abstract Model | p. 199 |
5.7 Summary | p. 200 |
Part 3 Embodiment of Behavior | p. 201 |
6 Sensory-Motor Routines | p. 203 |
6.1 Human Vision Is Specialized | p. 204 |
6.2 Routines | p. 210 |
6.3 Human Embodiment Overview | p. 214 |
6.4 Evidence for Visual Routines | p. 219 |
6.5 Changing the Agenda | p. 230 |
6.6 Discussion and Summary | p. 232 |
7 Motor Routines | p. 235 |
7.1 Motor Computation Basics | p. 238 |
7.2 Biological Movement Organization | p. 240 |
7.3 Cortex: Movement Plans | p. 248 |
7.4 Cerebellum: Checking Expectations | p. 253 |
7.5 Spinal Cord: Coding the Movement Library | p. 255 |
7.6 Reading Human Movement Data | p. 263 |
7.7 Summary | p. 272 |
8 Operating System | p. 275 |
8.1 A Hierarchical Cognitive Architecture | p. 279 |
8.2 Program Execution | p. 283 |
8.3 Humanoid Avatar Models | p. 289 |
8.4 Module Multiplexing | p. 293 |
8.5 Program Arbitration | p. 298 |
8.6 Alerting | p. 305 |
8.7 Program Indexing | p. 307 |
8.8 Credit Assignment | p. 309 |
8.9 Implications of a Modular Architecture | p. 313 |
8.10 Summary | p. 316 |
Part 4 Awareness | p. 319 |
9 Decision Making | p. 321 |
9.1 The Coding of Decisions | p. 322 |
9.2 Deciding in Noisy Environments | p. 325 |
9.3 Social Decision Making | p. 330 |
9.4 Populations of Game Players | p. 341 |
9.5 Summary | p. 345 |
10 Emotions | p. 349 |
10.1 Triune Phylogeny | p. 351 |
10.2 Emotions and the Body | p. 354 |
10.3 Somatic Marker Theory | p. 361 |
10.4 The Amygdala's Special Role | p. 366 |
10.5 Computational Perspectives | p. 369 |
10.6 Summary | p. 373 |
11 Consciousness | p. 377 |
11.1 Being a Model | p. 378 |
11.2 Simulation | p. 392 |
11.3 What Is Consciousness For? | p. 402 |
11.4 Summary | p. 406 |
Notes | p. 411 |
References | p. 413 |
Index | p. 435 |