Neuromimetic semantics: coordination, quantification, and collective predicates
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| Format: | Buch |
| Sprache: | English |
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Amsterdam [u.a.]
Elsevier
2004
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| Ausgabe: | 1. ed. |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXV, 527 S. Ill., graph. Darst. |
| ISBN: | 0444502084 |
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| 100 | 1 | |a Howard, Harry |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Neuromimetic semantics |b coordination, quantification, and collective predicates |c Harry Howard |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2004 | |
| 300 | |a XXV, 527 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
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| adam_text | Table of contents xv
Table of contents
Preface v
Acknowledgements xiv
Table of contents xv
1. Modest vs. robust theories of semantics 1
1.1. The problem 1
1.1.1. Modest vs. robust semantic theories 2
1.1.2. A modest solution: counting 2
1.1.3. Finite automata for the logical coordinators 5
1.1.4. A generalization to the logical quantifiers 7
The problem of time 8
1.1.6. Set-theoretical alternatives 8
1.1.7. What about modularity? 9
1.2. Vision as an example of natural computation 9
1.2.1. The retinogeniculate pathway 10
1.2.2. Primary visual cortex 15
1.2.2.1. Simple VI cells 19
1.2.2.2. Complex VI cells 23
1.2.2.3. The essential VI circuit: selection and generalization 26
1.2.2.4. Recoding to eliminate redundancy 28
1.2.3. Beyond primary visual cortex 35
1.2.3.1. Feedforward along the dorsal and ventral streams 35
1.2.3.2. Feedback 38
1.2.3.2.1. Generative models and Bayesian inference 38
1.2.3.2.2. Context 44
1.2.3.2.3. Selective attention and dendritic processing 47
1.2.4. Overview of the visual system 52
1.2.4.1. Preprocessing to extract invariances 52
1.2.4.2. Mereotopological organization 53
1.3. Some desiderata of natural computation 55
1.3.1. Amit on biological plausibility 55
1.3.2. Shastri on the logical problem of intelligent computation 56
1.3.3. Touretzky and Eliasmith on knowledge representation 57
1.3.4. Strong, vs. weak modularity 59
1.4. How to evaluate competing proposals 61
1.4.1. Levels of analysis 61
1.4.1.1. Marr s three levels of analysis 61
1.4.1.2. Tri-level analysis in the light of computational neuroscience ... 63
xvi Table of contents
1.4.1.3. The computational environment 65
1.4.1.4. Accounting for the desiderata of natural computation 66
1.4.2. Levels of adequacy 67
1.4.2.1. Chomsky s levels of adequacy of a grammar 68
1.4.2.2. Adequacy of natural (linguistic) computation 68
1.4.3. Levels of adequacy as levels of analysis 70
1.4.4. Summary of five-level theory 71
1.5. The competence/performance distinction 73
1.5.1. Competence and tri-level theory 74
1.5.2. Problems with the competence / performance distinction 75
1.5.3. A nongenerative/experiential alternative 76
1.6. Our story of coordination and quantification 78
1.6.1. The environmental causes of linguistic meaning 78
1.6.2. Preprocessing to extract correlational invariances 80
1.6.3. Back to natural computation and experiential linguistics 82
1.7. Where to go next 83 ^
2. Single neuron modeling 84
2.1. Basic electrical properties of the cell membrane 84
2.1.1. The structure of the cell membrane 84
2.1.2. Ion channels and chemical and electrical gradients 85
2.2. Models of the somatic membrane 87
2.2.1. The four-equation, Hodgkin-Huxley model 87
2.2.2. Electrical and hydraulic models of the cell membrane 88
2.2.2.1. The main voltage equation (at equilibrium) 89
2.2.2.2. The action potential and the main voltage equation 92
2.2.2.3. The three conductance equations 92
2.2.2.4. Hodgkin-Huxley oscillations 96
2.2.2.5. Simplifications and approximations 98
2.2.3. From four to two 99
2.2.3.1. Rate-constant interactions eliminate two variables 99
2.2.3.2. The fast-slow system 100
2.2.3.3. The FitzHugh-Nagumo model 102
2.2.3.4. FitzHugh-Nagumo models of Type I neurons 107
2.2.3.5. Neuron typology 108
2.2.4. From two to one: The integrate-and-fire model 110
2.2.4.1. Temporal or correlational coding Ill
2.2.5. From one to zero: Firing-rate models 112
2.2.6. Summary and transition 113
2.3. The integration of signals within a cell and dendrites 114
2.3.1. Dendrites 114
2.3.2. Passive cable models of dendritic electrical function 115
2.3.2.1. Equivalent cables/cylinders 116
2.3.2.2. Passive cable properties and neurite typology 117
Table of contents xvii
2.4. Transmission of signals from cell to cell: the synapse 119
2.4.1. Chemical modulation of synaptic transmission 120
2.4.2. Synaptic efficacy 122
2.4.3. Synaptic plasticity, long-term potentiation, and learning 123
2.4.4. Models of diffusion 125
2.4.5. Calcium accumulation and diffusion in spines 129
2.5. Summary: the classical neuromimetic model 130
2.5.1. The classical model 132
2.5.2. Activation functions 133
2.6. Expanded models 135
2.6.1. Excitable dendrites 136
2.6.1.1. Voltage-gated channels and compartmental models 136
2.6.1.2. Retrograde impulse spread 138
2.6.1.3. Dendritic spines as logic gates 138
2.6.2. Synaptic stability 139
2.6.3. The alternative of synaptic (or spinal) clustering 140
2.7. Summary and transition 141
3. Logical measures 143
3.1. Measure theory 143
3.1.1. Unsigned measures 143
3.1.2. Unsigned measures and the problem of complementation 146
3.1.3. Signed measures, signed algebras, and signed lattices 147
3.1.4. Response to those who do not believe in signs 150
3.1.5. Bivalent vs. trivalent logic 151
3.1.6. An interim summary to introduce the notion of spiking measures... 153
3.1.7. The logical operators as measures 155
3.2. Logical-operator measures 156
3.2.1. Conditional cardinality 156
3.2.1.1. Cardinality invariance 159
3.2.2. Statistics 161
3.2.2.1. Initial concepts: mean, deviation, variance 162
3.2.2.2. Covariance and correlation 164
3.2.2.3. Summary 167
3.2.3. Probability 167
3.2.3.1. Unconditional probability 167
3.2.3.2. Conditional probability and the logical quantifiers 169
3.2.3.3. Signed probability and the negative quantifiers 170
3.2.4. Information 171
3.2.4.1. Syntactic information 171
3.2.4.2. Entropy and conditional entropy 172
3.2.4.3. Semantic information 174
3.2.5. Vector algebra 175
3.2.5.1. Vectors 175
xviii Table of contents
3.2.5.2. Length and angle in polar space 178
3.2.5.3. Normalization of logical operator space 179
3.2.5.3.1. Logical operators as rays 179
3.2.5.3.2. Scalar multiplication 180
3.2.5.3.3. Normalization of a vector, sines and cosines 181
3.2.5.4. Vector space and vector semantics 182
3.2.6. Bringing statistics and vector algebra together 183
3.3. The order topology of operator measures 185
3.3.1. A one-dimensional order topology 185
3.3.2. A two-dimensional order topology 187
3.3.3. The order-theoretic definition of a lattice 188
3.4. Discreteness and convexity 189
3.4.1. Voronoi tesselation 190
3.4.2. Vector quantization 191
3.4.3. Voronoi regions as attractor basins 193
3.4.4. Tesselation and quantization: from continuous to discrete 194
3.4.5. Convexity and categorization 195
3.5. Semantic definitions of the logical operators 196
3.5.1. Logical operators as convex regions 197
3.5.2. Logical operators as edge and polarity detectors 197
3.5.2.1. Logical operators as edge detectors 197
3.5.2.2. Logical operators as polarity detectors 198
3.5.3. Summary and comparison to Horn s scale 200
3.5.4. Flaws in the word-to-scale mapping hypothesis? 202
3.5.4.1. Vague quantifiers 202
3.6. The usage of logical operators 203
3.6.1. Negative uninformativeness 203
3.6.2. Quantifying negative uninformativeness 205
3.6.3. Horn on implicatures 206
3.6.4. Quantifying the Q implicature 207
3.6.5. Rarity and trivalent logic 208
3.6.6. Quantifying the usage of logical quantifiers 208
3.7. Summary: What is logicality? 211
4. The representation of coordinator meanings 213
4.1. The coordination of major categories 213
4.2. Phrasal coordination 214
4.2.1. The application of nominals to verbals, and vice versa 214
4.2.1.1. Verbal predicates as patterns in a space of observations 214
4.2.1.2. Coordinated names and other DPs 215
4.2.1.3. A first mention of coordination and collectivity 217
4.2.1.4. Common nouns as patterns in a space 218
4.2.1.5. Coordinated common nouns 218
4.2.1.6. Coordinated verbs 219
Table of contents xix
4.2.1.7. Coordination beyond the monovalent predicate 220
4.2.1.8. Multiple coordination and respectively 221
4.2.2. Modification 222
4.2.2.1. Coordinated adjectivals 222
4.2.2.2. Coordinated adverbials 224
4.2.3. Summary of phrasal coordination and vector semantics 224
4.3. Clausal coordination 224
4.3.1. Conjunction reduction as vector addition 225
4.3.2. Coordination vs. juxtaposition and correlation 226
4.3.2.1. Asymmetric coordination 226
4.3.2.2. Kehler s coherence relations 228
4.3.2.2.1. The data structure 229
4.3.2.2.2. Coherence relations of Resemblance 230
4.3.2.2.3. Coherence relations of Cause-Effect 236
4.3.2.2.4. Coherence relations of Contiguity 239
4.3.2.2.5. Summary 241
4.3.2.3. Asymmetric coordination in Relevance Theory 242
4.3.2.4. The Common-Topic Constraint 243
4.3.3. Summary of clausal coordination 244
4.4. Lexicalization of the logical operators 245
4.4.1. The sixteen logical connectives 246
4.4.2. Conversational implicature: from sixteen to three 246
4.4.3. Neuromimetics: from sixteen to four 247
4.5. OR versus XOR 248
4.6. Summary 250
5. Neuromimetic networks for coordinator meanings 252
5.1. A first step towards pattern-classification semantics 252
5.2. Learning rules and cerebral subsystems 254
5.3. Error-correction and hyperplane learning 257
5.3.1. McCulloch and Pitts (1943) on the logical connectives 258
5.3.2. Single-layer perceptron (SLP) networks 260
5.3.2.1. SLP classification of the logical coordinators 261
5.3.2.2. SLP error correction 264
5.3.2.3. SLPs and unnormalized coordinators 265
5.3.2.4. SLPs for the normalized logical coordinators 268
5.3.2.5. Linear separability and XOR 269
5.3.3. Multilayer perceptron (MLP) and backpropagation (BP) networks ..270
5.3.3.1. Multilayer perceptrons 270
5.3.3.2. Sigmoidal transfer functions and the neurons that use them...271
5.3.3.3. Learning by backpropagation of errors 271
5.3.4. The implausibility of non-local learning rules 272
5.3.5. Summary 273
5.4. Unsupervised learning 273
xx Table of contents
5.4.1. The Hebbian learning rule 274
5.4.2. Instar networks 275
5.4.2.1. Introduction to the instar rule 276
5.4.2.2. An instar simulation of the logical coordinators 278
5.4.3. Unsupervised competitive learning 279
5.4.3.1. A competitive simulation of the logical coordinators 280
5.4.3.2. Quantization, Voronoi tesselation, and convexity 281
5.5. Supervised competitive learning: LVQ 282
5.5.1. A supervised competitive network and how it works 282
5.5.2. An LVQ simulation of the logical coordinators 284
5.5.3. Interim summary and comparison of LVQ to MLP 285
5.5.4. LVQ in a broader perspective 286
5.6. Dendritic processing 287
5.6.1. From synaptic to dendritic processing 288
5.6.2. Clustering of spines on a dendrite 289
5.7. Summary 294
6. The representation of quantifier meanings 295
6.1. The transition from coordination to quantification 295
6.1.1. Logical similarities 295
6.1.2. Conjunctive vs. disjunctive contexts 295
6.1.3. When coordination * quantification 297
6.1.4. Infinite quantification 299
6.2. Generalized quantifier theory 299
6.2.1. Introduction to quantifier meanings 299
6.2.2. A set-theoretic perspective on generalized quantifiers 301
6.2.3. QUANT, EXT, CONS, and the Tree of Numbers 301
6.2.3.1. Quantity 302
6.2.3.2. Extension 303
6.2.3.3. Conservativity 304
6.2.3.4. The Tree of Numbers 305
6.2.4. The neuromimetic perspective 309
6.2.4.1. IN-PI x IPPlNI vs. INI x IPI 310
6.2.4.2. The form of a quantified clause: Quantifier Raising 310
6.2.4.3. Another look at the constraints 313
6.2.4.3.1 Quantity and two streams of semantic processing 313
6.2.4.3.2 Extension and normalization 313
6.2.4.3.3 CONS and labeled lines 314
6.2.5. The origin, presupposition failure, and non-correlation 316
6.2.6. Triviality 317
6.2.6.1. Triviality and object recognition 319
6.2.6.2. Continuity of non-triviality and logicality 320
6.2.6.3. Continuity and the order topology 321
6.2.7. Finite means for infinite domains 322
Table of contents xxi
6.2.7.1. FIN, density, and approximation 322
6.3. Strict vs. loose readings of universal quantifiers 323
6.4. Summary 324
7. ANNs for quantifier learning and recognition 326
7.1. LVQ for quantifier learning and recognition 326
7.1.1. Perfect data, less than perfect data, and convex decision regions 326
7.1.2. Weight decay and lateral inhibition 328
7.1.3. Accuracy and generalization 329
7.2. Strict universal quantification as decorrelation 331
7.2.1. Three-dimensional data 331
7.2.2. Antiphase complementation 332
7.2.3. Selective attention 333
7.2.4. Summary: AND-NOT logic 335
7.3. Invariant extraction in L2 335
7.4. Summary 337
8. Inferences among logical operators 339
8.1. Inferences among logical operators 339
8.1.1. The Square of Opposition for quantifiers 340
8.1.2. A Square of Opposition for coordinators 342
8.1.3. Reasoning and cognitive psychology 345
8.1.3.1. Syntactic/proof-theoretic deduction 345
8.1.3.2. Semantic/model-theoretic deduction and Mental Models 346
8.1.3.3. Modest vs. robust deduction? 347
8.2. Spreading Activation Grammar 348
8.2.1. Shastri on connectionist reasoning 348
8.2.2. Jackendoff (2002) on the organization of a grammar 349
8.2.3. Spreading Activation Grammar 350
8.2.4. Interactive Competition and Activation 352
8.2.4.1. An example 354
8.2.4.2. The calculation of input to a unit 354
8.2.4.3. The calculation of change in activation of a unit 355
8.2.4.4. The evolution of change in activation of a network 355
8.2.5. Activation spreading from semantics to phonology 357
8.2.5.1. The challenge of negation 358
8.2.6. Activation spreading from phonology to semantics 360
8.2.7. Extending the network beyond the preprocessing module 361
8.3. Spreading activation and the Square of Opposition 362
8.3.1. Subaltern oppositions 362
8.3.2. Contradictory oppositions 363
8.3.3. (Sub)contrary oppositions 365
8.4. NALL and temporal limits on natural operators 366
8.4.1. Comparisons to other approaches 367
xxii Table of contents
8.5. Summary 368
9. The failure of subalternacy: reciprocity and center-oriented constructions...369
9.1. Constructions which block the subaltern implication 369
9.1.1. Classes of collectives and symmetric predicates 370
9.2. Reciprocity 371
9.2.1. A logical / diagrammatic representation of reciprocity 371
9.2.2. A distributed, k-bit encoding of anaphora 376
9.2.3. Anaphora in SAG 376
9.2.4. Comments on the SAG analysis of anaphora 377
9.2.5. The contextual elimination of reciprocal links 379
9.2.6. The failure of reciprocal subalternacy 380
9.2.7. Reflexives and reciprocals pattern together 381
9.3. Center-oriented constructions 382
9.3.1. Initial characterization and paths 382
9.3.2. Centrifugal constructions 384
9.3.2.1. Verbs of intersection 384
9.3.2.2. Resultative together 386
9.3.2.3. Verbs of congregation 387
9.3.2.4. Summary of centrifugal constructions 389
9.3.3. Centripetal constructions 390
9.3.3.1. Verbs of separation 390
9.3.3.2. Verbs of extraction and the ablative alternation 392
9.3.3.3. Resultative apart 394
9.3.3.4. Verbs of dispersion 394
9.3.3.5. Summary of centripetal constructions 395
9.4. Center-oriented constructions as paths 396
9.4.1. Covert reciprocity 398
9.4.2. The failure of center-oriented subalternacy 399
9.4.3. Path2 and gestalt locations 399
9.4.4. The comitative/ablative alternation 401
9.5. Summary 401
10. Networks of real neurons 403
10.1. Neurolinguistic networks 403
10.1.1. A brief introduction to the localization of language 403
10.1.1.1. Broca s aphasia and Broca s region 403
10.1.1.2. Wernicke s aphasia and Wernicke s region 405
10.1.1.3. Other regions 406
10.1.1.4. The Wernicke-Lichtheim-Geschwind boxological model 407
10.1.1.5. Cytoarchitecture and Brodmann s areas 408
10.1.1.6. Cytoarchitecture and post-mortem observations 409
10.1.1.7. A lop-sided view of language 410
10.1.1.8. The advent of commissurotomy 410
Table of contents xxiii
10.1.1.9. Experimental commissurotomy 411
10.1.1.9.1. Dichotic listening 411
10.1.1.9.2. An aside on the right-ear advantage 412
10.1.1.9.3. A left-ear advantage for prosody 413
10.1.1.10. Pop-culture lateralization and beyond 413
10.1.1.11. Neuroimaging 416
10.1.1.11.1. CT and PET 416
10.1.1.11.2. MRI and fMRI 416
10.1.1.11.3. Results for language 418
10.1.1.12. Computational modeling 419
10.1.2. Localization of the logical operators 420
10.1.2.1. Word comprehension and the lateralization 420
10.1.2.2. Where are content words stored? 423
10.1.2.3. Where are function words stored? 424
10.1.2.4. Function-words and anterior/posterior computation 425
10.1.2.4.1. Goertzel s dual network model 426
10.1.2.5. Some evidence for the weak modularity of language circuits 427
10.1.2.6. Where does this put the logical operators? 429
10.1.2.7. BA 44 vs. BA 47 429
10.1.3. Summary 430
10.2. From learning to memory 430
10.2.1. Types of memory 430
10.2.1.1. Quantitative memory: memory storage 430
10.2.1.2. Qualitative memory: declarative vs. non-declarative 432
10.2.1.3. Synthesis 434
10.2.2. A network for episodic memory 435
10.2.2.1. The hippocampal formation and Shastri s SMRITI 437
10.2.2.2. Long-term memory, the hippocampus, and COORs 439
10.2.2.2.1. The dentate gyms 439
10.2.2.2.2. An integrate-and-fire alternative 440
10.2.2.2.3. The dentate gyrus and coordinator meanings 441
10.2.2.3. Discussion 446
10.3. Summary 447
11. Three generations of Cognitive Science 448
11.1. Gen I: The Disembodied and Unimaginative Mind 449
11.1.1. A first order grammar 449
11.1.1.1. First order logic 449
11.1.1.2. First order syntax 449
11.1.1.3. First order semantics 451
11.1.2. More on the ontology 452
11.1.3. Classical categorization and semantic features 453
11.1.4. Objectivist metaphysics 454
11.1.5. An example: the spatial usage of in 455
xxiv Table of contents
11.2. Reactions to Gen 1 456
11.2.1. Problems with classical categorization 456
11.2.2. Problems with set-theory as an ontology 456
11.2.3. Problems with symbolicism 457
11.3. Genii: The Embodied and Imaginative Mind 457
11.3.1. Prototype-based categorization 458
11.3.2. Image-schematic semantics 458
11.3.3. Image-schemata and spatial in 460
11.3.4. Image-schematic quantification 461
11.4. Reactions to Gen II 462
11.4.1. The math phobia of image-schematic semantics 462
11.4.2. Problems with prototype-based categorization 464
11.4.3. The biological plausibility of the Second Generation 464
11.5. Gen III: The Imaged and Simulated Brain 464
11.5.1. The microstructure of cognition 465
11.5.2. Mereotopology during the transition 466
11.5.2.1. Gardenfors conceptual spaces 466
11.5.2.1.1. Conceptual Spaces 466
11.5.2.1.2. Properties in conceptual space 467
11.5.2.1.3. Prototypes and Voronoi tesselation 468
11.5.2.1.4. Conclusions 469
11.5.2.2. Smith s and Eschenbach s mereotopology 469
11.5.2.2.1. Mereology + topology 469
11.5.2.2.2. Mereotopological notions of Eschenbach (1994) 469
11.5.2.2.3. LVQ mereotopology 472
11.5.3. Intelligent computation and LVQ mereotopology 473
11.5.3.1. Neural plausibility 473
11.5.3.1.1. Interactivity 474
11.5.3.1.2. Cross-domain generality 475
11.5.3.2. Self-organization 475
11.5.3.2.1. Density matching and statistical sensitivity 476
11.5.3.2.2. Approximation of the input space 476
11.5.3.2.3. Topological ordering and associativity 477
11.5.3.2.4. Implicit rule learning 477
11.5.3.2.5. Emergent behavior 477
11.5.3.3. Flexibility of reasoning 477
11.5.3.3.1. Graceful degradation 477
11.5.3.3.2. Content-addressability 477
11.5.3.3.3. Pattern completion 478
11.5.3.3.4. Generalization to novel inputs 479
11.5.3.3.5. Potential for abstraction 479
11.5.3.4. Structured relationships 479
11.5.3.5. Exemplar-based categorization 479
11.5.3.6. LVQ and the evolution of language 480
Table of contents xxv
11.6. Summary 481
References 483
Index 515
|
| any_adam_object | 1 |
| author | Howard, Harry |
| author_facet | Howard, Harry |
| author_role | aut |
| author_sort | Howard, Harry |
| author_variant | h h hh |
| building | Verbundindex |
| bvnumber | BV026611675 |
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| ctrlnum | (OCoLC)55036941 (DE-599)BVBBV026611675 |
| dewey-full | 401/.43 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 401 - Philosophy and theory |
| dewey-raw | 401/.43 |
| dewey-search | 401/.43 |
| dewey-sort | 3401 243 |
| dewey-tens | 400 - Language |
| discipline | Sprachwissenschaft Literaturwissenschaft |
| edition | 1. ed. |
| format | Book |
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| id | DE-604.BV026611675 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:15:50Z |
| institution | BVB |
| isbn | 0444502084 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022166256 |
| oclc_num | 55036941 |
| open_access_boolean | |
| owner | DE-188 DE-19 DE-BY-UBM |
| owner_facet | DE-188 DE-19 DE-BY-UBM |
| physical | XXV, 527 S. Ill., graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Howard, Harry Verfasser aut Neuromimetic semantics coordination, quantification, and collective predicates Harry Howard 1. ed. Amsterdam [u.a.] Elsevier 2004 XXV, 527 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022166256&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Howard, Harry Neuromimetic semantics coordination, quantification, and collective predicates |
| title | Neuromimetic semantics coordination, quantification, and collective predicates |
| title_auth | Neuromimetic semantics coordination, quantification, and collective predicates |
| title_exact_search | Neuromimetic semantics coordination, quantification, and collective predicates |
| title_full | Neuromimetic semantics coordination, quantification, and collective predicates Harry Howard |
| title_fullStr | Neuromimetic semantics coordination, quantification, and collective predicates Harry Howard |
| title_full_unstemmed | Neuromimetic semantics coordination, quantification, and collective predicates Harry Howard |
| title_short | Neuromimetic semantics |
| title_sort | neuromimetic semantics coordination quantification and collective predicates |
| title_sub | coordination, quantification, and collective predicates |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022166256&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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