Cerebral cortex: principles of operation
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| Format: | Buch |
| Sprache: | English |
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Oxford
Oxford University Press
2016
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| Ausgabe: | First edition |
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xix, 958 Seiten Illustrationen, Diagramme |
| ISBN: | 9780198820345 9780198784852 |
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| 245 | 1 | 0 | |a Cerebral cortex |b principles of operation |c Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK |
| 250 | |a First edition | ||
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Datensatz im Suchindex
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| adam_text | Contents
1 Introduction 1
1.1 Principles of operation of the cerebral cortex: introduction and plan 1
1.2 Neurons 4
1.3 Neurons in a network 6
1.4 Synaptic modification 8
1.5 Long-term potentiation and long-term depression 9
1.6 Distributed representations 14
1.6.1 Definitions 14
1.6.2 Advantages of different types of coding 15
1.7 Neuronal network approaches versus connectionism 16
1.8 introduction to three neuronal network architectures 17
1.9 Systems-level analysis of brain function 18
1.9.1 Ventral cortical visual stream 19
1.9.2 Dorsal cortical visual stream 21
1.9.3 Hippocampal memory system 23
1.9.4 Frontal lobe systems 23
1.9.5 Brodmann areas 24
1.10 The fine structure of the cerebral neocortex 27
1.10.1 The fine structure and connectivity of the neocortex 27
1.10.2 Excitatory cells and connections 27
1.10.3 Inhibitory cells and connections 29
1.10.4 Quantitative aspects of cortical architecture 32
1.10.5 Functional pathways through the cortical layers 34
1.10.6 The scale of lateral excitatory and inhibitory effects, and modules 38
1.11 Highlights 39
2 Hierarchical organization 40
2.1 Introduction 40
2.2 Hierarchical organization in sensory systems 41
2.2.1 Hierarchical organization in the ventral visual system 41
2.2.2 Hierarchical organization in the dorsal visual system 46
2.2.3 Hierarchical organization of taste processing 48
2.2.4 Hierarchical organization of olfactory processing 57
2.2.5 Hierarchical multimodal convergence of taste, olfaction, and vision 59
2.2.6 Hierarchical organization of auditory processing 64
2.3 Hierarchical organization of reward value processing 67
2.4 Hierarchical organization of connections to the frontal lobe for short-term memory 68
2.5 Highlights 69
3 Localization of function 72
3.1 Hierarchical processing 72
vill I Contents
3.2 Short-range neocortical recurrent collaterals 72
3.3 Topographic maps 72
3.4 Modularity 72
3.5 Lateralization of function 73
3.6 Ventral and dorsal cortical areas 73
3.7 Highlights 74
4 Recurrent collateral connections and attractor networks 75
4.1 Introduction 75
4.2 Attractor networks implemented by the recurrent collaterals 75
4.3 Evidence for attractor networks implemented by recurrent collateral connections 76
4.3.1 Short-term Memory 77
4.3.2 Long-term Memory 80
4.3.3 Decision-Making 80
4.4 The storage capacity of attractor networks 80
4.5 A global attractor network in hippocampal CA3, but local in neocortex 81
4.6 The speed of operation of cortical attractor networks 83
4.7 Dilution of recurrent collateral cortical connectivity 83
4.8 Self-organizing topographic maps in the neocortex 85
4.9 Attractors formed by forward and backward connections between cortical areas? 85
4.10 Interacting attractor networks 86
4.11 Highlights 90
5 The noisy cortex: stochastic dynamics, decisions, and memory 91
5.1 Reasons why the brain is inherently noisy and stochastic 91
5.2 Attractor networks, energy landscapes, and stochastic neurodynamics 95
5.3 A multistable system with noise 98
5.4 Stochastic dynamics and the stability of short-term memory 101
5.4.1 Analysis of the stability of short-term memory 103
5.4.2 Stability and noise in a mpdel of short-term memory 104
5.5 Long-term memory recall 106
5.6 Stochastic dynamics and probabilistic decision-making in an attractor network 106
5.6.1 Decision-making in an attractor network 107
5.6.2 Theoretical framework: a probabilistic attractor network 107
5.6.3 Stationary multistability analysis: mean-field 110
5.6.4 (ntegrate-and-fire simulations of decision-making: spiking dynamics 112
5.6.5 Reaction times of the neuronal responses 116
5.6.6 Percentage correct 117
5.6.7 Finite-size noise effects 117
5.6.8 Comparison with neuronal data during decision-making 119
5.6.9 Testing the model of decision-making with human functional neuroimaging 122
5.6.10 Decisions based on confidence in one’s decisions: self-monitoring 129
5.6.11 Decision-making with multiple alternatives 131
5.6.12 The matching law 132
5.6.13 Comparison with other models of decision-making 132
5.7 Perceptual decision-making and rivalry 134
5.8 Symmetry-breaking 135
Contents I IX
5.9 The evolutionary utility of probabilistic choice 135
5.10 Selection between conscious vs unconscious decision-making, and free will 136
5.11 Creative thought 137
5.12 Unpredictable behaviour 138
5.13 Predicting a decision before the evidence is applied 138
5.14 Highlights 140
6 Attention, short-term memory, and biased competition 141
6.1 Bottom-up attention 141
6.2 Top-down attention ֊ biased competition 143
6.2.1 The biased competition hypothesis 143
6.2.2 Biased competition ֊ single neuron studies 145
6.2.3 Non-spatiaf attention 147
6.2.4 Biased competition-fMRI 149
6.2.5 A basic computational module for biased competition 149
6.2.6 Architecture of a model of attention 150
6.2.7 Simulations of basic experimental findings 154
6.2.8 Object recognition and spatial search 158
6.2.9 The neuronal and biophysical mechanisms of attention 163
6.2.10 ‘Serial’ vs ‘parallel’ attentions processing 167
6.3 Top-down attention ֊ biased activation 171
6.3.1 Selective attention can selectively activate different cortical areas 171
6.3.2 Sources of the top-down modulation of attention 173
6.3.3 Granger causality used to investigate the source of the top-down biasing 174
6.3.4 Top-down cognitive modulation 175
6.3.5 A top-down biased activation model of attention 178
6.4 Conclusions 181
6.5 Highlights 184
7 Diluted connectivity 186
7.1 Introduction 186
7.2 Diluted connectivity and the storage capacity of attractor networks 187
7.2.1 The autoassociative or attractor network architecture being studied 187
7.2.2 The storage capacity of attractor networks with diluted connectivity 188
7.2.3 The network simulated 190
7.2.4 The effects of diluted connectivity on the capacity of attractor networks 192
7.2.5 Synthesis of the effects of diluted connectivity in attractor networks 197
7.3 The effects of dilution on the capacity of pattern association networks 198
7.4 The effects of dilution on the performance of competitive networks 201
7.4.1 Competitive Networks 201
7.4.2 Competitive networks without learning but with diluted connectivity 202
7.4.3 Competitive networks with learning and with diluted connectivity 203
7.4.4 Competitive networks with learning and with full (undiluted) connectivity 205
7.4.5 Overview and implications of diluted connectivity in competitive networks 206
7.5 The effects of dilution on the noise in attractor networks 207
7.6 Highlights 207
8 Coding principles 209
8.1 Types of encoding 209
X ļ Contents
8.2 Place coding with sparse distributed firing rate representations 210
8.2.1 Reading the code used by single neurons 210
8.2.2 Understanding the code provided by populations of neurons 214
8.3 Synchrony, coherence, and binding 221
8.4 Principles by which the representations are formed 222
8.5 Information encoding in the human cortex 223
8.6 Highlights 226
3 Synaptic modification for learning 227
9.1 Introduction 227
9.2 Associative synaptic modification implemented by long-term potentiation 227
9.3 Forgetting in associative neural networks, and memory reconsolidation 228
9.3.1 Forgetting 228
9.3.2 Factors that influence synaptic modification 230
9.3.3 Reconsolidation 232
9.4 Spike-timing dependent plasticity 233
9.5 Long-term synaptic depression in the cerebellar cortex 233
9.6 Reward prediction error learning 234
9.6.1 Blocking and delta-rule learning 234
9.6.2 Dopamine neuron firing and reward prediction error learning 234
9.7 Highlights 240
10 Synaptic and neuronal adaptation and facilitation 241
10.1 Mechanisms for neuronal adaptation and synaptic depression and facilitation 241
10.1.1 Sodium inactivation leading to neuronal spike-frequency adaptation 241
10.1.2 Calcium activated hyper-polarizing potassium current 242
10.1.3 Short-term synaptic depression and facilitation 243
10.2 Short-term depression of thalamic input to the cortex 244
10.3 Relatively little adaptation in primate cortex when it is operating normally 244
10.4 Acetylcholine, noradrenaline, and other modulators of adaptation and facilitation 247
10.4.1 Acetylcholine 247
10.4.2 Noradrenergic neurons 248
10.5 Synaptic depression and sensory-specific satiety 249
10.6 Neuronal and synaptic adaptation, and the memory for sequential order 250
10.7 Destabilization of short-term memory by adaptation or synaptic depression 250
10.8 Non-reward computation in the orbitofrontal cortex using synaptic depression 251
10.9 Synaptic facilitation and a multiple-item short-term memory 253
10.10 Synaptic facilitation in decision-making 253
10.11 Highlights 254
11 Backprojections in the neocortex 255
11.1 Architecture 255
11.2 Learning 257
11.3 Recall 258
11.4 Semantic priming 259
11.5 Top-down Attention 259
11.6 Autoassociative storage, and constraint satisfaction 261
Contents | XI
11.7 Highlights 261
12 Memory and the hippocampus 262
12.1 Introduction 262
12.2 Hippocampal circuitry and connections 262
12.3 The hippocampus and episodic memory 262
12.4 Autoassociation in the CA3 network for episodic memory 263
12.5 The dentate gyrus as a pattern separation mechanism, and neurogenesis 265
12.6 Rodent place cells vs primate spatial view cells 265
12.7 Backprojections, and the recall of information from the hippocampus to neocortex 266
12.8 Subcortical structures connected to the hippocampo-cortical memory system 267
12.9 Highlights 267
13 Limited neurogenesis in the adult cortex 269
13.1 No neurogenesis in the adult neocortex 269
13.2 Limited neurogenesis in the adult hippocampal dentate gyrus 269
13.3 Neurogenesis in the chemosensing receptor systems 270
13.4 Highlights 271
14 Invariance learning and vision 272
14.1 Hierarchical cortical organization with convergence 272
14.2 Feature combinations 272
14.3 Sparse distributed representations 273
14.4 Self-organization by feedforward processing without a teacher 273
14.5 Learning guided by the statistics of the visual inputs 274
14.6 Bottom up saliency 275
14.7 Lateral interactions shape receptive fields 276
14.8 Top-down selective attention vs feedforward processing 277
14.9 Topological maps to simplify connectivity 278
14.10 Biologically decodable output representations 279
14.11 Highlights 279
15 Emotion, motivation, reward value, pleasure, and their mechanisms 281
15.1 Emotion, reward value, and their evolutionary adaptive utility 281
15.2 Motivation and reward value 283
15.3 Principles of cortical design for emotion and motivation 283
15.4 Objects are first represented independently of reward value 284
15.5 Specialized systems for face identity and expression processing in primates 286
15.6 Unimodai processing to the object level before multimodal convergence 287
15.7 A common scale for reward value 287
15.8 Sensory-specific satiety 287
15.9 Economic value is represented in the orbitofrontal cortex 288
15.10 Neuroeconomics vs classical microeconomics 288
15.11 Output systems influenced by orbitofrontal cortex reward value representations 289
15.12 Decision-making about rewards in the anterior orbitofrontal cortex 291
xil | Contents
15.13 Probabilistic emotion-related decision-making 292
15.14 Non-reward, error, neurons in the orbitofrontal cortex 292
15.15 Reward reversal learning in the orbitofrontal cortex 296
15.16 Dopamine neurons and emotion 301
15.17 The explicit reasoning system vs the emotional system 301
15.18 Pleasure 302
15.19 Personality relates to differences in sensitivity to rewards and punishers 302
15.20 Highlights 303
16 Noise in the cortex, stability, psychiatric disease, and aging 305
16.1 Stochastic noise, attractor dynamics, and schizophrenia 305
16.1.1 Introduction 305
16.1.2 A dynamical systems hypothesis of the symptoms of schizophrenia 307
16.1.3 The depth of the basins of attraction: mean-field flow analysis 308
16.1.4 Decreased stability produced by reduced NMDA conductances 309
16.1.5 Increased distractibility produced by reduced NMDA conductances 311
16.1.6 Synthesis: network instability and schizophrenia 312
16.2 Stochastic noise, attractor dynamics, and obsessive-compulsive disorder 316
16.2.1 Introduction 316
16.2.2 A hypothesis about obsessive-compulsive disorder 317
16.2.3 Glutamate and increased depth of the basins of attraction 319
16.2.4 Synthesis on obsessive-compulsive disorder 322
16.3 Stochastic noise, attractor dynamics, and depression 325
16.3.1 Introduction 325
16.3.2 A non-reward attractor theory of depression 328
16.3.3 Evidence consistent with the theory 329
16.3.4 Relation to other brain systems implicated in depression 331
16.3.5 Implications for treatments 332
16.3.6 Mania and bipolar disorder 333
16.4 Stochastic noise, attractor dynamics, and aging 335
16.4.1 NMDA receptor hypofunction 335
16.4.2 Dopamine 338
16.4.3 Impaired synaptic modification 338
16.4.4 Cholinergic function and memory 339
16.5 Highlights 343
17 Syntax and Language 345
17.1 Neurodynamical hypotheses about language and syntax 345
17.1.1 Binding by synchrony? 345
17.1.2 Syntax using a place code 346
17.1.3 Temporal trajectories through a state space of attractors 347
17.1.4 Hypotheses about the implementation of language in the cerebral cortex 347
17.2 Tests of the hypotheses - a model 351
17.2.1 Attractor networks with stronger forward than backward connections 351
17.2.2 The operation of a single attractor network module 353
17.2.3 Spike frequency adaptation mechanism 355
17.3 Tests of the hypotheses ֊ findings with the model 355
17.3.1 A production system 355
17.3.2 A decoding system 356
17.4 Evaluation of the hypotheses 359
Contents) xiii
17.5 Highlights 363
18 Evolutionary trends in cortical design and principles of operation 364
18.1 Introduction 364
18.2 Different types of cerebral neocortex: towards a computational understanding 364
18.2.1 Neocortex or isocortex 365
18.2.2 Olfactory (pyriform) cortex 371
18.2.3 Hippocampal cortex 374
18.3 Addition of areas in the neocortical hierarchy 376
18.4 Evolution of the orbitofrontal cortex 378
18.5 Evolution of the taste and flavour system 379
18.5.1 Principles 379
18.5.2 Taste processing in rodents 380
18.6 Evolution of the temporal lobe cortex 381
18.7 Evolution of the frontal lobe cortex 382
18.8 Highlights 382
19 Genetics and self-organization build the cortex 385
19.1 Introduction 385
19.2 Hypotheses about the genes that build cortical neural networks 386
19.3 Genetic selection of neuronal network parameters 390
19.4 Simulation of the evolution of neural networks using a genetic algorithm 391
19.4.1 The neural networks 391
19.4.2 The specification of the genes 392
19.4.3 The genetic algorithm, and general procedure 397
19.4.4 Pattern association networks 398
19.4.5 Autoassociative networks 400
19.4.6 Competitive networks 400
19.5 Evaluation of the gene-based evolution of single-layer networks 401
19.6 The gene-based evolution of multi-layer cortical systems 403
19.7 Highlights 404
20 Cortex versus basal ganglia design for selection 406
20.1 Systems-level architecture of the basal ganglia 406
20.2 What computations are performed by the basal ganglia? 408
20.3 How do the basal ganglia perform their computations? 410
20.4 Comparison of selection in the basal ganglia and cerebral cortex 413
20.5 Highlights 415
21 Sleep and Dreaming 416
21.1 Is sleep necessary for cortical function? 416
21.2 Is sleep involved in memory consolidation? 417
21.3 Dreams 418
21.4 Highlights 419
22 Which cortical computations underlie consciousness? 420
22.1 Introduction 420
xiv ¡Contents
22.2 A Higher-Order Syntactic Thought (HOST) theory of consciousness 421
22.2.1 Multiple routes to action 421
22.2.2 A computational hypothesis of consciousness 423
22.2.3 Adaptive value of processing that is related to consciousness 425
22.2.4 Symbol grounding 426
22.2.5 Qualia 428
22.2.6 Pathways 429
22.2.7 Consciousness and causality 430
22.2.8 Consciousness and higher-order syntactic thoughts 431
22.3 Selection between conscious vs unconscious decision-making systems 432
22.3.1 Dual major routes to action: implicit and explicit 432
22.3.2 The Selfish Gene vs The Selfish Phenotype 439
22.3.3 Decision-making between the implicit and explicit systems 440
22.4 Determinism 441
22.5 Free will 442
22.6 Content and meaning in representations 443
22.7 The causal role of consciousness and the relation between the mind and the brain 445
22.8 Comparison with other theories of consciousness 447
22.8.1 Higher-order thought theories 447
22.8.2 Oscillations and temporal binding 449
22.8.3 A high neural threshold for information to reach consciousness 450
22.8.4 James-Lange theory and Damasio’s somatic marker hypothesis 451
22.8.5 LeDoux’s approach to emotion and consciousness 451
22.8.6 Panksepp’s approach to emotion and consciousness 452
22.8.7 Global workspace theories of consciousness 452
22.8.8 Monitoring and consciousness 452
22.9 Highlights 453
23 Cerebellar cortex 455
23.1 Introduction 455
23.2 Architecture of the cerebellum 456
23.2.1 The connections of the parallel fibres onto the Purkinje cells 456
23.2.2 The climbing fibre input to the Purkinje cell 457
23.2.3 The mossy fibre to granule cell connectivity 457
23.3 Modifiable synapses of parallel fibres onto Purkinje cell dendrites 460
23.4 The cerebellar cortex as a perceptron 460
23.5 Highlights: differences between cerebral and cerebellar cortex microcircuitry 461
24 The hippocampus and memory 463
24.1 Introduction 463
24.2 Systems-level functions of the hippocampus 464
24.2.1 Systems-level anatomy 465
24.2.2 Evidence from the effects of damage to the hippocampus 467
24.2.3 The necessity to recall information from the hippocampus 468
24.2.4 Systems-level neurophysiology of the primate hippocampus 470
24.2.5 Head direction cells in the presubiculum 478
24.2.6 Perirhinal cortex, recognition memory, and long-term familiarity memory 479
24.3 A theory of the operation of hippocampal circuitry as a memory system 486
24.3.1 Hippocampal circuitry 487
24.3.2 Entorhinal cortex 488
Contents
XV
24.3.3 САЗ as an auto association memory 490
24.3.4 Dentate granule cells 509
24.3.5 CAt cells 515
24.3.6 Recoding in CA1 to facilitate retrieval to the neocortex 515
24.3.7 BacKprojections to the neocortex, memory recall, and consolidation 520
24.3.8 Backprojections to the neocortex - quantitative aspects 523
24.3.9 Simulations of hippocampal operation 526
24.3.10 The learning of spatial view and place cell representations 528
24.3.11 Linking the inferior temporal visual cortex to spatial view and pface cells 529
24.3.12 A scientific theory of the art of memory: scientia artis memoriae 531
24.4 Tests of the theory of hippocampal cortex operation 531
24.4.1 Dentate gyrus (DG) subregion of the hippocampus 531
24.4.2 CA3 subregion of the hippocampus 535
24.4.3 CA1 subregion of the hippocampus 542
24.5 Evaluation of the theory of hippocampal cortex operation 546
24.5.1 Tests of the theory by hippocampal system subregion analyses 546
24.5.2 Comparison with other theories of hippocampal function 548
24.6 Highlights 552
25 Invariant visual object recognition learning 554
25.1 Introduction 554
25.2 Invariant representations of faces and objects in the inferior temporal visual cortex 555
25.2.1 Processing to the inferior temporal cortex in the primate visual system 555
25.2.2 Translation invariance and receptive field size 556
25.2.3 Reduced translation invariance in natural scenes 557
25.2.4 Size and spatial frequency invariance 560
25.2.5 Combinations of features in the correct spatial configuration 561
25.2.6 A view-invariant representation 562
25.2.7 Learning in the inferior temporal cortex 565
25.2.8 Distributed encoding 568
25.2.9 Face expression, gesture, and view 572
25.2.10 Specialized regions in the temporal cortical visual areas 572
25.3 Approaches to invariant object recognition 576
25.3.1 Featurespaces 577
25.3.2 Structural descriptions and syntactic pattern recognition 578
25.3.3 Template matching and the alignment approach 580
25.3.4 Invertible networks that can reconstruct their inputs 581
25.3.5 Feature hierarchies 582
25.4 Hypotheses about object recognition mechanisms 582
25.5 Computational issues in feature hierarchies 586
25.5.1 The architecture of Vis Net 587
25.5.2 Initial experiments with VisNet 596
25.5.3 The optimal parameters for the temporal trace used in the learning rule 603
25.5.4 Different forms of the trace learning rule, and error correction 604
. 25.5.5 The issue of feature binding, and a solution 612
25.5.6 Operation in a cluttered environment 624
25.5.7 Learning 3D transforms 631
25.5.8 Capacity of the architecture, and an attractor implementation 636
25.5.9 Vision in natural scenes ֊ effects of background versus attention 643
25.5.10 The representation of multiple objects in a scene 651
25.5.11 Learning invariant representations using spatial continuity 653
25.5.12 Lighting invariance 654
XVI I Contents
25.5.13 Invariant global motion in the dorsal visual system 656
25.5.14 Deformation-invariant object recognition 656
25.5.15 Learning invariant representations of scenes and places 657
25.5.16 Finding and recognising objects in natural scenes 659
25.6 Further approaches to invariant object recognition 663
25.6.1 Other types of slow learning 663
25.6.2 HMAX 663
25.6.3 Sigma-Pi synapses 668
25.6.4 Deep learning 668
25.7 Visuo-spatial scratchpad memory, and change blindness 669
25.8 Processes involved in object identification 670
25.9 Highlights 671
26 Synthesis 674
26.1 Principles of cortical operation, not a single theory 674
26.2 Levels of explanation, and the mind-brain problem 674
26.3 Brain computation compared to computation on a digital computer 676
26.4 Understanding how the brain works 681
26.5 Synthesis on principles of operation of the cerebral cortex 683
26.5.1 Hierarchical organization 683
26.5.2 Localization of function 684
26.5.3 Recurrent collaterals and attractor networks 684
26.5.4 The noisy cortex 685
26.5.5 Top-down attention 685
26.5.6 Diluted connectivity 685
26.5.7 Sparse distributed graded firing rate encoding 685
26.5.8 Synaptic modification 686
26.5.9 Adaptation and facilitation 686
26.5.10 Backprojections 686
26.5.11 Neurogenesis 687
26.5.12 Binding and syntax 687
26.5.13 Evolution of the cerebral cortex 687
26.5.14 Genetic specification of cortical design 687
26.5.15 The cortical systems for emotion 688
26.5.16 Memory systems 688
26.5.17 Visual cortical processing for invariant visual object recognition 689
26.5.18 Cortical lamination, operation, and evolution 689
26.6 Highlights 692
A Introduction to linear algebra for neural networks 694
A.1 Vectors 694
A.1.1 The inner or dot product of two vectors 694
A.1.2 The length of a vector 695
A.1.3 Normalizing the length of a vector 696
A.1.4 The angle between two vectors: the normalized dot product 696
A. 1.5 The outer product of two vectors 697
A.1.6 Linear and non-linear systems 698
A.1.7 Linear combinations, linear independence, and linear separability 699
A.2 Application to understanding simple neural networks 700
A.2.1 Capability and limitations of single-layer networks 701
A.2.2 Non-linear networks: neurons with non-linear activation functions 703
Contents! xvii
A.2.3 Non-linear networks: neurons with non-linear activations 704
Neuronal network models 706
B.1 Introduction 706
B.2 Pattern association memory 706
B.2.1 Architecture and operation 707
B.2.2 A simple model 710
B.2,3 The vector interpretation 712
B.2.4 Properties 713
B.2.5 Prototype extraction, extraction of central tendency, and noise reduction 716
B.2.6 Speed 716
B.2.7 Local learning rule 717
B.2.8 Implications of different types of coding for storage in pattern associators 722
B.3 Autoassociation or attractor memory 723
B.3.1 Architecture and operation 724
B.3.2 Introduction to the analysis of the operation of auto association networks 725
B.3.3 Properties 727
B.3.4 Use of autoassociation networks in the brain 733
B.4 Competitive networks, including self-organizing maps 734
B.4.1 Function 734
B.4.2 Architecture and algorithm 735
B.4.3 Properties 736
B.4.4 Utility of competitive networks in information processing by the brain 741
B.4,5 Guidance of competitive learning 743
B.4.6 Topographic map formation 745
B.4.7 Invariance learning by competitive networks 749
B.4.8 Radial Basis Function networks 751
B.4.9 Further details of the algorithms used in competitive networks 752
B.5 Continuous attractor networks 756
B.5.1 Introduction 756
B.5.2 The generic model of a continuous attractor network 758
B.5.3 Learning the synaptic strengths in a continuous attractor network 759
B.5.4 The capacity of a continuous attractor network: multiple charts 761
B.5.5 Continuous attractor models: path integration 761
B.5.6 Stabilization of the activity packet within a continuous attractor network 764
B.5.7 Continuous attractor networks in two or more dimensions 766
B.5.8 Mixed continuous and discrete attractor networks 767
B.6 Network dynamics: the integrate-and-fire approach 767
B.6.1 From discrete to continuous time 768
B.6.2 Continuous dynamics with discontinuities 769
B.6.3 An integrate-and-fire implementation 773
B.6.4 The speed of processing of attractor networks 774
B.6.5 The speed of processing of a four-layer hierarchical network 777
B.6.6 Spike response model 780
B.7 Network dynamics: introduction to the mean-field approach 781
B.8 Mean-field based neurodynamics 783
B.8.1 Population activity 783
B.8.2 The mean-field approach used in a model of decision-making 785
B.8.3 The model parameters used in the mean-field analyses of decision-making 787
B.8.4 A basic computational module based on biased competition 788
B.8.5 Muitimodular neurodynamicai architectures 789
B.9 Sequence memory implemented by adaptation in an attractor network 791
xviii (Contents
B.10 Error correction networks
B.10.1 Architecture and general description 792
B. 10.2 Generic algorithm for a one-layer error correction network 793
B. 10.3 Capability and limitations of single-layer error-correcting networks 793
B.10.4 Properties 797
B.11 Error backpropagation multilayer networks 799
B.11.1 Introduction 799
B.11.2 Architecture and algorithm 799
B.11.3 Properties of multilayer networks trained by error backpropagation 802
B.12 Biologically plausible networks vs backpropagation 803
B.13 Convolution networks 804·
B.14 Contrastive Hebbian learning: the Boltzmann machine 806
B.15 Deep Belief Networks 807
B.16 Reinforcement learning 807
B.16.1 Associative reward-penalty algorithm of Barto and Sutton 808
B.16.2 Reward prediction error or delta rule learning, and classical conditioning 810
B. 16.3 Temporal Difference (TD) learning 811
B. 17 Highlights 814
C Information theory, and neuronal encoding 815
C. 1 Information theory 816
C. 1.1 The information conveyed by definite statements 816
C.1.2 Information conveyed by probabilistic statements 817
C.1.3 Information sources, information channels, and information measures 818
C.1.4 The information carried by a neuronal response and its averages 819
C.1.5 The information conveyed by continuous variables 822
C.2 The information carried by neuronal responses 824
C.2.1 The limited sampling problem 824
C.2.2 Correction procedures for limited sampling 825
C.2.3 The information from multiple cells: decoding procedures 826
C.2.4 Information in the correlations between cells: a decoding approach 830
C.2.5 Information in the correlations between ceils: second derivative approach 835
C.3 Information theory results 838
C.3.1 The sparseness of the distributed encoding used by the brain 839
C.3.2 The information from single neurons 850
C.3.3 The information from single neurons: temporal codes versus rate codes 852
C.3.4 The information from single neurons: the speed of information transfer 854
C.3.5 The information from multiple cells: independence versus redundancy 866
C.3.6 Should one neuron be as discriminative as the whole organism? 870
C.3.7 The information from multiple cells: the effects of cross-correlations 871
C. 3.8 Conclusions on cortical neuronal encoding 875
C.4 Information theory terms - a short glossary 879
C. 5 Highlights 880
D Simulation software for neuronal network models 881
D. 1 Introduction 881
D.2 Autoassociation or attractor networks 881
D. 2.1 Running the simulation 881
D.2.2 Exercises 883
D.3 Pattern association networks 884
Contents I xix
D.3.1 Running the simulation 884
D.3.2 Exercises 886
D.4 Competitive networks and Self-Organizing Maps 886
D.4.1 Running the simulation 886
D.4.2 Exercises 888
D.5 Highlights 889
References 890
Index 950
|
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| spelling | Rolls, Edmund T. Verfasser aut Cerebral cortex principles of operation Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK First edition Oxford Oxford University Press 2016 xix, 958 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Hirnfunktion (DE-588)4159930-5 gnd rswk-swf Großhirnrinde (DE-588)4072114-0 gnd rswk-swf Großhirnrinde (DE-588)4072114-0 s Hirnfunktion (DE-588)4159930-5 s DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029070947&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rolls, Edmund T. Cerebral cortex principles of operation Hirnfunktion (DE-588)4159930-5 gnd Großhirnrinde (DE-588)4072114-0 gnd |
| subject_GND | (DE-588)4159930-5 (DE-588)4072114-0 |
| title | Cerebral cortex principles of operation |
| title_auth | Cerebral cortex principles of operation |
| title_exact_search | Cerebral cortex principles of operation |
| title_full | Cerebral cortex principles of operation Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK |
| title_fullStr | Cerebral cortex principles of operation Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK |
| title_full_unstemmed | Cerebral cortex principles of operation Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK |
| title_short | Cerebral cortex |
| title_sort | cerebral cortex principles of operation |
| title_sub | principles of operation |
| topic | Hirnfunktion (DE-588)4159930-5 gnd Großhirnrinde (DE-588)4072114-0 gnd |
| topic_facet | Hirnfunktion Großhirnrinde |
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