| Preface |
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xi | |
| Acknowledgments |
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xv | |
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1 The Science of Cognition |
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1 | (32) |
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1 | (4) |
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5 | (9) |
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14 | (4) |
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1.4 Questions and Answers |
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18 | (7) |
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1.5 Nengo: An Introduction |
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25 | (8) |
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Part I How To Build A Brain |
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2 An Introduction to Brain Building |
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33 | (44) |
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33 | (7) |
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2.2 A Framework for Building a Brain |
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40 | (22) |
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43 | (9) |
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52 | (3) |
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55 | (5) |
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2.2.4 The Three Principles |
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60 | (2) |
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62 | (4) |
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2.4 Nengo: Neural Representation |
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66 | (11) |
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3 Biological Cognition: Semantics |
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77 | (44) |
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3.1 The Semantic Pointer Hypothesis |
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78 | (5) |
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3.2 What Is a Semantic Pointer? |
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83 | (1) |
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3.3 Semantics: An Overview |
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84 | (3) |
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87 | (3) |
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3.5 Deep Semantics for Perception |
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90 | (10) |
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3.6 Deep Semantics for Action |
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100 | (7) |
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3.7 The Semantics of Perception and Action |
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107 | (4) |
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3.8 Nengo: Neural Computations |
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111 | (10) |
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4 Biological Cognition-Syntax |
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121 | (42) |
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4.1 Structured Representations |
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121 | (1) |
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4.2 Binding Without Neurons |
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122 | (6) |
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128 | (5) |
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4.4 Manipulating Structured Representations |
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133 | (6) |
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4.5 Learning Structural Manipulations |
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139 | (2) |
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4.6 Clean-Up Memory and Scaling |
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141 | (5) |
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4.7 Example: Fluid Intelligence |
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146 | (6) |
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4.8 Deep Semantics for Cognition |
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152 | (4) |
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4.9 Nengo: Structured Representations in Neurons |
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156 | (7) |
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5 Biological Cognition-Control |
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163 | (46) |
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5.1 The Flow of Information |
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163 | (1) |
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164 | (5) |
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5.3 Basal Ganglia, Cortex, and Thalamus |
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169 | (3) |
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5.4 Example: Fixed Sequences of Actions |
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172 | (3) |
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5.5 Attention and the Routing of Information |
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175 | (8) |
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5.6 Example: Flexible Sequences of Actions |
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183 | (4) |
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187 | (4) |
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5.8 Example: The Tower of Hanoi |
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191 | (7) |
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5.9 Nengo: Question Answering |
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198 | (11) |
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6 Biological Cognition-Memory and Learning |
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209 | (38) |
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6.1 Extending Cognition Through Time |
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209 | (2) |
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211 | (4) |
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6.3 Example: Serial List Memory |
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215 | (4) |
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219 | (7) |
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6.5 Example: Learning New Actions |
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226 | (4) |
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6.6 Example: Learning New Syntactic Manipulations |
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230 | (11) |
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241 | (6) |
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7 The Semantic Pointer Architecture |
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247 | (48) |
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7.1 A Summary of the Semantic Pointer Architecture |
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247 | (2) |
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7.2 A Semantic Pointer Architecture Unified Network |
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249 | (9) |
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258 | (20) |
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258 | (1) |
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259 | (1) |
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7.3.3 Reinforcement Learning |
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260 | (3) |
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7.3.4 Serial Working Memory |
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263 | (1) |
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264 | (3) |
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267 | (2) |
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7.3.7 Rapid Variable Creation |
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269 | (3) |
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272 | (2) |
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274 | (4) |
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7.4 A Unified View: Symbols and Probabilities |
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278 | (6) |
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7.5 Nengo: Advanced Modeling Methods |
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284 | (11) |
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Part II Is That How You Build A Brain? |
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8 Evaluating Cognitive Theories |
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295 | (26) |
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295 | (1) |
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8.2 Core Cognitive Criteria |
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296 | (14) |
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8.2.1 Representational Structure |
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296 | (1) |
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297 | (1) |
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297 | (2) |
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299 | (1) |
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8.2.1.4 The Massive Binding Problem |
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300 | (1) |
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8.2.2 Performance Concerns |
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301 | (1) |
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8.2.2.1 Syntactic Generalization |
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301 | (2) |
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303 | (1) |
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304 | (1) |
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305 | (1) |
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306 | (2) |
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308 | (1) |
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8.2.3.1 Triangulation (Contact With More Sources of Data) |
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308 | (1) |
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309 | (1) |
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310 | (1) |
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8.4 Nengo Bonus: How to Build a Brain-a Practical Guide |
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311 | (10) |
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321 | (46) |
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321 | (19) |
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9.1.1 Adaptive Control of Thought-Rational |
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323 | (3) |
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9.1.2 Synchrony-Based Approaches |
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326 | (3) |
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9.1.3 Neural Blackboard Architecture |
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329 | (3) |
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9.1.4 The Integrated Connectionist/Symbolic Architecture |
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332 | (3) |
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335 | (3) |
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9.1.6 Dynamic Field Theory |
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338 | (2) |
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340 | (15) |
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9.2.1 Representational Structure |
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340 | (4) |
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9.2.2 Performance Concerns |
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344 | (6) |
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350 | (4) |
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354 | (1) |
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355 | (2) |
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357 | (6) |
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9.5 The SPA Versus the SOA |
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363 | (4) |
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10 Consequences and Challenges |
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367 | (20) |
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368 | (4) |
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372 | (2) |
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374 | (2) |
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376 | (4) |
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380 | (4) |
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384 | (3) |
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A Mathematical Notation and Overview |
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387 | (8) |
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387 | (1) |
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388 | (1) |
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389 | (1) |
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A.4 Basis of a Vector Space |
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390 | (2) |
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A.5 Linear Transformations on Vectors |
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392 | (1) |
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A.6 Time Derivatives for Dynamics |
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393 | (2) |
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B Mathematical Derivations for the NEF |
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395 | (6) |
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395 | (2) |
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395 | (1) |
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396 | (1) |
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397 | (1) |
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398 | (3) |
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C Further Details on Deep Semantic Models |
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401 | (4) |
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401 | (2) |
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403 | (2) |
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D Mathematical Derivations for the Semantic Pointer Architecture |
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405 | (8) |
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D.1 Binding and Unbinding Holographic Reduced Representations |
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405 | (2) |
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D.2 Learning High-Level Transformations |
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407 | (1) |
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D.3 Ordinal Serial Encoding Model |
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408 | (1) |
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D.4 Spike-Timing Dependent Plasticity |
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408 | (2) |
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D.5 Number of Neurons for Representing Structure |
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410 | (3) |
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E Semantic Pointer Architecture Model Details |
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413 | (4) |
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413 | (4) |
| Bibliography |
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417 | (30) |
| Index |
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447 | |