Memory, attention, and decision-making: a unifying computational neuroscience approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2008
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 804 S. Ill., graph. Darst. |
| ISBN: | 9780199232703 |
Internformat
MARC
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| 100 | 1 | |a Rolls, Edmund T. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Memory, attention, and decision-making |b a unifying computational neuroscience approach |c Edmund T. Rolls |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2008 | |
| 300 | |a XVI, 804 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Memory | |
| 650 | 4 | |a Attention | |
| 650 | 4 | |a Decision making | |
| 650 | 4 | |a Memory / physiology | |
| 650 | 4 | |a Attention / physiology | |
| 650 | 4 | |a Brain / physiology | |
| 650 | 4 | |a Computer Simulation | |
| 650 | 4 | |a Decision Making / physiology | |
| 650 | 7 | |a Aandacht |2 gtt | |
| 650 | 7 | |a Besluitvorming |2 gtt | |
| 650 | 7 | |a Geheugen |2 gtt | |
| 650 | 7 | |a Neuropsychologie |2 gtt | |
| 650 | 4 | |a Attention | |
| 650 | 4 | |a Attention |x physiology | |
| 650 | 4 | |a Brain |x physiology | |
| 650 | 4 | |a Computer Simulation | |
| 650 | 4 | |a Decision Making |x physiology | |
| 650 | 4 | |a Decision making | |
| 650 | 4 | |a Memory | |
| 650 | 4 | |a Memory |x physiology | |
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Datensatz im Suchindex
| _version_ | 1804137206890102784 |
|---|---|
| adam_text | Contents
Introduction
1
1.1
Introduction
and overview
1
1.2
Neurons
4
1.3
Neurons in a network
5
1.4
Synaptic modification
7
1.5
Long-term potentiation and long-term depression
8
1.6
Distributed representations
13
1.6.1
Definitions
13
1.6.2
Advantages of different types of coding
14
1.7 Neuronal
network approaches versus connectionism
15
1.8
Introduction to three
neuronal
network architectures
16
.,9
Systems-level analysis of brain function
17
1.10
The fine structure of the cerebral neocortex
22
1.10.1
The fine structure and connectivity of the neocortex
22
1.10.2
Excitatory cells and connections
22
1.10.3
Inhibitory cells and connections
24
1.10.4
Quantitative aspects of cortical architecture
26
1.10.5
Functional pathways through the cortical layers
28
1.10.6
The scale of lateral excitatory and inhibitory effects, and the con¬
cept of modules
30
1.11
Backprojections in the cortex
31
1.11.1
Architecture
31
1.11.2
Learning
32
1.11.3
Recall
34
1.11.4
Semantic priming
35
1.11.5
Attention
35
1.11.6
Autoassociative
storage, and constraint satisfaction
35
The hippocampus and memory
37
2.1
Introduction
37
2.2
Systems-level functions of the hippocampus
38
2.2.1
Systems-level anatomy
38
2.2.2
Evidence from the effects of damage to the hippocampus
40
2.2.3
The necessity to recall information from the hippocampus
42
2.2.4
Systems-level neurophysiology of the primate hippocampus
43
2.2.5
Head direction cells in the presubiculum
49
viii
I
Contents
2.2.6
Perirhinal cortex, recognition memory, and long-term familiarity
memory
51
2.3
A theory of the operation of hippocampal circuitry as a memory system
57
2.3.1
Hippocampal circuitry
58
2.3.2
CA3 as an
autoassociation
memory
58
2.3.3
Dentate granule cells
75
2.3.4
The learning of spatial view and place cell representations from
visual inputs
79
2.3.5
Linking the inferior temporal visual cortex to spatial view and place
cell representations
81
2.3.6
CA1 cells
82
2.3.7
Backprojections to the neocortex
-
a hypothesis
86
2.3.8
Backprojections to the neocortex
-
quantitative aspects
88
2.3.9
Simulations of hippocampal operation
90
2.4
Tests of the theory
91
2.4.1
Dentate gyrus (DG) subregion of the hippocampus
91
2.4.2
CA3 subregion of the hippocampus
94
2.4.3
CA1 subregion of the hippocampus
101
2.5
Evaluation of the theory of hippocampal function
105
2.5.1
Quantitative aspects of the model
105
2.5.2
Tests of the theory by hippocampal system subregion analyses
106
2.5.3
Comparison with other theories of hippocampal function
109
3
Reward- and punishment-related learning; emotion and mot¬
ivation
113
3.1
Introduction
113
3.2
Associative processes involved in reward- and punishment-related learn¬
ing
116
3.2.1
Pavlovian or classical conditioning
116
3.2.2
Instrumental learning
118
3.3
Overview of brain processes involved in reward and punishment learning
121
3.4
Representations of primary reinforcers
124
3.4.1
Taste
125
3.4.2
Smell
125
3.4.3
Pleasant and painful touch
126
3.4.4
Visual stimuli
128
3.5
Representing potential secondary reinforcers
129
3.5.1
The requirements of the representation
130
3.5.2
High capacity
133
3.5.3
Objects, and not their reward and punishment associations, are
represented in the inferior temporal visual cortex
133
3.5.4
Object representations
135
3.6
The orbitofrontal cortex
136
Contents
I
ix
3.6.1
Historical background
136
3.6.2
Topology
137
3.6.3
Connections
139
3.6.4
Effects of damage to the orbitofrontal cortex
140
3.6.5
Neurophysiology and functional neuroimaging of the orbitofrontal
cortex
142
3.6.6
The human orbitofrontal cortex
175
3.6.7
A computational basis for stimulus-reinforcer association learning
and reversal in the orbitofrontal cortex
184
3.6.8
Executive functions of the orbitofrontal cortex
186
3.7
The amygdala
187
3.7.1
Connections of the amygdala
188
3.7.2
Effects of amygdala lesions
190
3.7.3 Neuronal
activity in the primate amygdala to reinforcing stimuli
196
3.7.4
Responses of these amygdala neurons to reinforcing and novel
stimuli
202
3.7.5 Neuronal
responses in the amygdala to faces
204
3.7.6
Evidence from humans
205
3.7.7
Amygdala summary
210
3.8
The cingulate cortex
211
3.8.1
Anterior or perigenual cingulate cortex, reward, and affect
212
3.8.2
Mid-cingulate cortex, the cingulate motor area, and action-
outcome learning
217
3.9
Human brain imaging investigations of mood and depression
219
3.10
Output pathways for reward- and punisher-guided behaviour, including
emotional responses
220
3.10.1
The
autonomie
and endocrine systems
220
3.10.2
Motor systems for implicit responses, including the basal ganglia,
reinforcement learning, and dopamine
221
3.10.3
Output systems for explicit responses to emotional stimuli
248
3.10.4
Basal forebrain and
hypothalamus
249
3.10.5
Basal forebrain cholinergic neurons
249
3.10.6
Noradrenergic neurons
251
3.10.7
Opiate reward systems, analgesia, and food reward
252
3.11
Effects of emotion on cognitive processing and memory
253
3.12
Laterally effects in human reward and emotional processing
257
3.13
Summary
259
4
Invariant visual object recognition learning
262
4.1
Introduction
262
4.2
Invariant representations of faces and objects in the inferior temporal visual
cortex
262
χ Ι
Contents
4.2.1
Processing to the inferior temporal cortex in the primate visual
system
263
4.2.2
Translation
invariance
and receptive field size
264
4.2.3
Reduced translation
invariance
in natural scenes, and the selec¬
tion of a rewarded object
265
4.2.4
Size and spatial frequency
invariance
268
4.2.5
Combinations of features in the correct spatial configuration
269
4.2.6
A view-invariant representation
270
4.2.7
Learning in the inferior temporal cortex
274
4.2.8
Distributed encoding
276
4.2.9
Face expression, gesture, and view
281
4.2.10
Specialized regions in the temporal cortical visual areas
281
4.3
Approaches to invariant object recognition
285
4.3.1
Feature spaces
286
4.3.2
Structural descriptions and syntactic pattern recognition
287
4.3.3
Template matching and the alignment approach
289
4.3.4
Invertible networks that can reconstruct their inputs
290
4.3.5
Feature hierarchies
290
4.4
Hypotheses about object recognition mechanisms
295
4.5
Computational issues in feature hierarchies
298
4.5.1
The architecture of VisNet
299
4.5.2
Initial experiments with VisNet
307
4.5.3
The optimal parameters for the temporal trace used in the learning
rule
314
4.5.4
Different forms of the trace learning rule, and their relation to error
correction and temporal difference learning
315
4.5.5
The issue of feature binding, and a solution
324
4.5.6
Operation in a cluttered environment
335
4.5.7
Learning
3D
transforms
342
4.5.8
Capacity of the architecture, and incorporation of a trace rule into
a recurrent architecture with object attractors
347
4.5.9
Vision in natural scenes
-
effects of background versus attention
354
4.5.10
The representation of multiple objects in a scene
362
4.5.11
Learning invariant representations using spatial continuity: Con¬
tinuous Spatial Transformation learning
364
4.5.12
Lighting
invariance
365
4.5.13
Invariant global motion in the dorsal visual system
367
4.6
Further approaches to invariant object recognition
367
4.7
Visuo-spatial scratchpad memory, and change blindness
370
4.8
Processes involved in object identification
372
4.9
Conclusions
373
5
Short-term memory
375
Contents
I
xi
5.1
Cortical short-term memory systems and attractor networks
375
5.2
Prefrontal cortex short-term memory networks, and their relation to per¬
ceptual networks
378
5.3
Computational details of the model of short-term memory
381
5.4
Computational necessity for a separate, prefrontal cortex, short-term
memory system
383
5.5
Synaptic modification is needed to set up but not to reuse short-term
memory systems
384
5.6
What, where, and object-place combination short-term memory in the
prefrontal cortex
384
6
Attention, short-term memory, and biased competition
386
6.1
Introduction
386
6.2
The classical view: the spotlight metaphor and feature integration theory
387
6.3
Biased competition
-
single neuron studies
391
6.3.1
Neurophysiology of attention
392
6.3.2
The role of competition
394
6.3.3
Evidence for attentional bias
396
6.3.4
Non-spatial attention
396
6.3.5
High-resolution buffer hypothesis
398
6.4
Biased competition
-
fMRI
398
6.4.1
Neuroimaging of attention
399
6.4.2
Attentional effects in the absence of visual stimulation
399
6.5
A basic computational module for biased competition
401
6.6
Architecture of a model of attention
402
6.7
Simulations of basic experimental findings
407
6.7.1
Simulations of single-cell experiments
408
6.7.2
Simulations of fMRI experiments
410
6.8
Object recognition and spatial search
411
6.8.1
Dynamics of spatial attention and object recognition
414
6.8.2
Dynamics of object attention and visual search
416
6.9
The
neuronal
and biophysical mechanisms of attention
417
6.10
Linking computational and psychophysical data: serial vs parallel pro¬
cessing
421
6.10.1
Serial vs parallel search
421
6.10.2
Visual conjunction search
424
6.11
Linking computational and neuropsychological data on attention
429
6.11.1
The neglect syndrome
429
6.11.2
A model of visual spatial neglect
430
6.11.3
Disengagement of attention in neglect
437
6.11.4
Extinction and visual search
438
6.12
Conclusions
440
6.13
Attention
-
a formal model
443
xii Contents
7
Probabilistic decision-making
449
7.1
Introduction
449
7.2
The
neuronal
data underlying the vibrotactile discrimination
451
7.3
Theoretical framework: a probabilistic attractor network
452
7.4
Stationary multistability analysis: mean-field
455
7.5
Non-stationary probabilistic analysis: spiking dynamics
458
7.6
Properties of this model of decision-making
465
7.7
Applications of this model of decision-making
469
7.8
The integrate-and-fire formulation used in the model of decision-making
470
7.9
The mean-field approach used in the model of decision-making
472
7.10
The model parameters used in the simulations of decision-making
474
8
Action selection by biased attractor competition in the pre-
frontal cortex
475
8.1
Introduction
475
8.2
A hierarchical attractor model of action selection
476
8.3
Setting up the synaptic connectivity for the prefrontal cortex
480
8.4
Pharmacology of attention, decision-making, and action selection
483
8.5
Application to a neurodynamical systems hypothesis of schizophrenia
485
8.6
Conclusions
494
9
Reward, decision, and action reversal using attractor dyn¬
amics
496
9.1
Stimulus-reinforcer association learning and reversal
496
9.2
Reversal of action selection
504
9.3
Sequence memory
506
9.4
Conclusions
508
10
Decision-making
509
10.1
Selection of mainly
autonomie
responses, and their classical conditioning
509
10.2
Selection of approach or withdrawal, and their classical conditioning
509
10.3
Selection of fixed stimulus-response habits
510
10.4
Selection of arbitrary behaviours to obtain goals, action-outcome learning,
and emotional learning
510
10.5
The roles of the prefrontal cortex in decision-making and attention
511
10.5.1
Prefrontal attentional influences on perceptual processing
512
10.5.2
Attentional influences on mapping from stimuli to responses
512
10.5.3
Executive control
513
10.5.4
Disorders of attention and decision-making
514
10.6
Neuroeconomics, reward magnitude, expected value, and expected utility
515
10.6.1
Expected utility
Rí
expected value
=
probability multiplied by
reward magnitude
515
10.6.2
Delay of reward, emotional choice, and rational choice
516
10.6.3
Reward prediction error, temporal difference error, and choice
518
Contents
I
xiii
10.6.4
Reciprocal altruism, strong reciprocity, generosity, and altruistic
punishment
519
10.7
Dual routes to action, and decision-making
523
10.8
Apostasie
529
A Introduction to linear algebra for neural networks
531
A.1 Vectors
531
A.1.1 The inner or dot product of two vectors
531
A.
1.2
The length of a vector
532
A.1
.3
Normalizing the length of a vector
533
A.
1.4
The angle between two vectors: the normalized dot product
533
A.1
.5
The outer product of two vectors
534
A.
1.6
Linear and non-linear systems
535
A.1.
7
Linear combinations of vectors, linear independence, and linear
separability
536
A.2 Application to understanding simple neural networks
538
A.2.1 Capability and limitations of single-layer networks: linear separa¬
bility and capacity
538
A.2.2 Non-linear networks: neurons with non-linear activation functions
540
A.2.3 Non-linear networks: neurons with non-linear activations
541
В
Neural network models
543
B.1 Introduction
543
B.2 Pattern association memory
543
B.2.1 Architecture and operation
544
B.2.2 A simple model
547
B.2.3 The vector interpretation
549
B.2.4 Properties
550
B.2.5 Prototype extraction, extraction of central tendency, and noise
reduction
553
B.2.6 Speed
553
B.2.7 Local learning rule
554
B.2.8 Implications of different types of coding for storage in pattern
as¬
sociatore
559
B.3
Autoassociation
or attractor memory
560
B.3.1 Architecture and operation
560
B.3.2 Introduction to the analysis of the operation of
autoassociation
networks
562
B.3.3 Properties
564
B.3.4 Use of
autoassociation
networks in the brain
570
B.4 Competitive networks, including self-organizing maps
571
B.4.1 Function
571
B.4.2 Architecture and algorithm
572
B.4.3 Properties
573
xiv Contents
В,4.4
Utility
of competitive networks
¡η
information processing by the
brain
578
B.4.5 Guidance of competitive learning
579
B.4.6 Topographic map formation
582
B.4.7
Invariance
learning by competitive networks
586
B.4.8 Radial Basis Function networks
588
B.4.9 Further details of the algorithms used in competitive networks
589
B.5 Continuous attractor networks
593
B.5.1 Introduction
593
B.5.2 The generic model of a continuous attractor network
595
B.5.3 Learning the synaptic strengths between the neurons that imple¬
ment a continuous attractor network
595
B.5.4 The capacity of a continuous attractor network: multiple charts
and packets
598
B.5.5 Continuous attractor models: path integration
598
B.5.6 Stabilization of the activity packet within the continuous attractor
network when the agent is stationary
601
B.5.7 Continuous attractor networks in two or more dimensions
603
B.5.8 Mixed continuous and discrete attractor networks
603
B.6 Network dynamics: the integrate-and-fire approach
604
B.6.1 From discrete to continuous time
604
B.6.2 Continuous dynamics with discontinuities
606
B.6.3 An integrate-and-fire implementation
610
B.6.4 Simulation of
f MRI
signals: hemodynamic convolution of synaptic
activity
611
B.6.5 The speed of processing of one-layer attractor networks with
integrate-and-fire neurons
613
B.6.6 The speed of processing of a four-layer hierarchical network with
integrate-and-fire attractor dynamics in each layer
616
B.6.7 Spike response model
619
B.7 Network dynamics: introduction to the mean-field approach
620
B.8 Mean-field based
neurodynamics
621
B.8.1 Population activity
622
B.8.2 A basic computational module based on biased competition
624
B.8.3 Multimodular neurodynamical architectures
625
B.9 Interacting attractor networks
627
B.1
0
Sequence memory implemented by adaptation in an attractor network
631
B.1
1
Error correction networks
631
B.
11.1
Architecture and general description
632
B.1
1.2
Generic algorithm for a one-layer error correction network)
632
B.
11.3
Capability and limitations of single-layer error-correcting networks
633
B.1
1.4
Properties
636
B.1
2
Error backpropagation multilayer networks
638
Contents
I
xv
В.
12,1
Introduction
638
В.12.2
Architecture and algorithm
639
В.
12.3
Properties of multilayer networks trained by error backpropagation
640
B.13 Biologically plausible networks
641
B.14
Contrastive
Hebbian learning: the Boltzmann machine
642
B.15 Reinforcement learning
644
B.15.1 Associative reward-penalty algorithm of Barto and Sutton
645
B.15.2 Reward prediction error or delta rule learning, and classical con¬
ditioning
646
B.15.3 Temporal Difference (TD) learning
647
B.16 Forgetting in associative neural networks and in the brain, and memory
reconsolidation
650
В.
17
Brain computation compared to computation on a digital computer
654
Information theory, and
neuronal
encoding
659
C.1 Information theory
660
C.1
.1
The information conveyed by definite statements
660
C.1.
2
Information conveyed by probabilistic statements
661
С
1.3
Information sources, information channels, and information mea¬
sures
662
C.1
.4
The information carried by
a neuronal
response and its averages
663
C.1
.5
The information conveyed by continuous variables
666
C.2 The information carried by
neuronal
responses
668
C.2.1 The limited sampling problem
668
C.2.2 Correction procedures for limited sampling
669
C.2.3 The information from multiple cells: decoding procedures
670
C.2.4 Information in the correlations between the spikes of different
cells: a decoding approach
674
C.2.5 Information in the correlations between the spikes of different
cells: a second derivative approach
679
C.3 Information theory results
682
C.3.1 The sparseness of the distributed encoding used by the brain
683
C.3.2 The information from single neurons
693
C.3.3 The information from single neurons: temporal codes versus rate
codes within the spike train of a single neuron
697
C.3.4 The information from single neurons: the speed of information
transfer
698
C.3.5 The information from multiple cells: independent information ver¬
sus redundancy across cells
709
C.3.6 Should one neuron be as discriminative as the whole organism,
in object encoding systems?
713
C.3.7 The information from multiple cells: the effects of cross-
correlations between cells
715
xvi Contents
C.3.8
Conclusions on cortical
neuronal
encoding
719
C.4 Information theory terms
-
a short glossary
723
О
Glossary
724
References
726
Index
782
E
Colour Plates
789
|
| adam_txt |
Contents
Introduction
1
1.1
Introduction
and overview
1
1.2
Neurons
4
1.3
Neurons in a network
5
1.4
Synaptic modification
7
1.5
Long-term potentiation and long-term depression
8
1.6
Distributed representations
13
1.6.1
Definitions
13
1.6.2
Advantages of different types of coding
14
1.7 Neuronal
network approaches versus connectionism
15
1.8
Introduction to three
neuronal
network architectures
16
.,9
Systems-level analysis of brain function
17
1.10
The fine structure of the cerebral neocortex
22
1.10.1
The fine structure and connectivity of the neocortex
22
1.10.2
Excitatory cells and connections
22
1.10.3
Inhibitory cells and connections
24
1.10.4
Quantitative aspects of cortical architecture
26
1.10.5
Functional pathways through the cortical layers
28
1.10.6
The scale of lateral excitatory and inhibitory effects, and the con¬
cept of modules
30
1.11
Backprojections in the cortex
31
1.11.1
Architecture
31
1.11.2
Learning
32
1.11.3
Recall
34
1.11.4
Semantic priming
35
1.11.5
Attention
35
1.11.6
Autoassociative
storage, and constraint satisfaction
35
The hippocampus and memory
37
2.1
Introduction
37
2.2
Systems-level functions of the hippocampus
38
2.2.1
Systems-level anatomy
38
2.2.2
Evidence from the effects of damage to the hippocampus
40
2.2.3
The necessity to recall information from the hippocampus
42
2.2.4
Systems-level neurophysiology of the primate hippocampus
43
2.2.5
Head direction cells in the presubiculum
49
viii
I
Contents
2.2.6
Perirhinal cortex, recognition memory, and long-term familiarity
memory
51
2.3
A theory of the operation of hippocampal circuitry as a memory system
57
2.3.1
Hippocampal circuitry
58
2.3.2
CA3 as an
autoassociation
memory
58
2.3.3
Dentate granule cells
75
2.3.4
The learning of spatial view and place cell representations from
visual inputs
79
2.3.5
Linking the inferior temporal visual cortex to spatial view and place
cell representations
81
2.3.6
CA1 cells
82
2.3.7
Backprojections to the neocortex
-
a hypothesis
86
2.3.8
Backprojections to the neocortex
-
quantitative aspects
88
2.3.9
Simulations of hippocampal operation
90
2.4
Tests of the theory
91
2.4.1
Dentate gyrus (DG) subregion of the hippocampus
91
2.4.2
CA3 subregion of the hippocampus
94
2.4.3
CA1 subregion of the hippocampus
101
2.5
Evaluation of the theory of hippocampal function
105
2.5.1
Quantitative aspects of the model
105
2.5.2
Tests of the theory by hippocampal system subregion analyses
106
2.5.3
Comparison with other theories of hippocampal function
109
3
Reward- and punishment-related learning; emotion and mot¬
ivation
113
3.1
Introduction
113
3.2
Associative processes involved in reward- and punishment-related learn¬
ing
116
3.2.1
Pavlovian or classical conditioning
116
3.2.2
Instrumental learning
118
3.3
Overview of brain processes involved in reward and punishment learning
121
3.4
Representations of primary reinforcers
124
3.4.1
Taste
125
3.4.2
Smell
125
3.4.3
Pleasant and painful touch
126
3.4.4
Visual stimuli
128
3.5
Representing potential secondary reinforcers
129
3.5.1
The requirements of the representation
130
3.5.2
High capacity
133
3.5.3
Objects, and not their reward and punishment associations, are
represented in the inferior temporal visual cortex
133
3.5.4
Object representations
135
3.6
The orbitofrontal cortex
136
Contents
I
ix
3.6.1
Historical background
136
3.6.2
Topology
137
3.6.3
Connections
139
3.6.4
Effects of damage to the orbitofrontal cortex
140
3.6.5
Neurophysiology and functional neuroimaging of the orbitofrontal
cortex
142
3.6.6
The human orbitofrontal cortex
175
3.6.7
A computational basis for stimulus-reinforcer association learning
and reversal in the orbitofrontal cortex
184
3.6.8
Executive functions of the orbitofrontal cortex
186
3.7
The amygdala
187
3.7.1
Connections of the amygdala
188
3.7.2
Effects of amygdala lesions
190
3.7.3 Neuronal
activity in the primate amygdala to reinforcing stimuli
196
3.7.4
Responses of these amygdala neurons to reinforcing and novel
stimuli
202
3.7.5 Neuronal
responses in the amygdala to faces
204
3.7.6
Evidence from humans
205
3.7.7
Amygdala summary
210
3.8
The cingulate cortex
211
3.8.1
Anterior or perigenual cingulate cortex, reward, and affect
212
3.8.2
Mid-cingulate cortex, the cingulate motor area, and action-
outcome learning
217
3.9
Human brain imaging investigations of mood and depression
219
3.10
Output pathways for reward- and punisher-guided behaviour, including
emotional responses
220
3.10.1
The
autonomie
and endocrine systems
220
3.10.2
Motor systems for implicit responses, including the basal ganglia,
reinforcement learning, and dopamine
221
3.10.3
Output systems for explicit responses to emotional stimuli
248
3.10.4
Basal forebrain and
hypothalamus
249
3.10.5
Basal forebrain cholinergic neurons
249
3.10.6
Noradrenergic neurons
251
3.10.7
Opiate reward systems, analgesia, and food reward
252
3.11
Effects of emotion on cognitive processing and memory
253
3.12
Laterally effects in human reward and emotional processing
257
3.13
Summary
259
4
Invariant visual object recognition learning
262
4.1
Introduction
262
4.2
Invariant representations of faces and objects in the inferior temporal visual
cortex
262
χ Ι
Contents
4.2.1
Processing to the inferior temporal cortex in the primate visual
system
263
4.2.2
Translation
invariance
and receptive field size
264
4.2.3
Reduced translation
invariance
in natural scenes, and the selec¬
tion of a rewarded object
265
4.2.4
Size and spatial frequency
invariance
268
4.2.5
Combinations of features in the correct spatial configuration
269
4.2.6
A view-invariant representation
270
4.2.7
Learning in the inferior temporal cortex
274
4.2.8
Distributed encoding
276
4.2.9
Face expression, gesture, and view
281
4.2.10
Specialized regions in the temporal cortical visual areas
281
4.3
Approaches to invariant object recognition
285
4.3.1
Feature spaces
286
4.3.2
Structural descriptions and syntactic pattern recognition
287
4.3.3
Template matching and the alignment approach
289
4.3.4
Invertible networks that can reconstruct their inputs
290
4.3.5
Feature hierarchies
290
4.4
Hypotheses about object recognition mechanisms
295
4.5
Computational issues in feature hierarchies
298
4.5.1
The architecture of VisNet
299
4.5.2
Initial experiments with VisNet
307
4.5.3
The optimal parameters for the temporal trace used in the learning
rule
314
4.5.4
Different forms of the trace learning rule, and their relation to error
correction and temporal difference learning
315
4.5.5
The issue of feature binding, and a solution
324
4.5.6
Operation in a cluttered environment
335
4.5.7
Learning
3D
transforms
342
4.5.8
Capacity of the architecture, and incorporation of a trace rule into
a recurrent architecture with object attractors
347
4.5.9
Vision in natural scenes
-
effects of background versus attention
354
4.5.10
The representation of multiple objects in a scene
362
4.5.11
Learning invariant representations using spatial continuity: Con¬
tinuous Spatial Transformation learning
364
4.5.12
Lighting
invariance
365
4.5.13
Invariant global motion in the dorsal visual system
367
4.6
Further approaches to invariant object recognition
367
4.7
Visuo-spatial scratchpad memory, and change blindness
370
4.8
Processes involved in object identification
372
4.9
Conclusions
373
5
Short-term memory
375
Contents
I
xi
5.1
Cortical short-term memory systems and attractor networks
375
5.2
Prefrontal cortex short-term memory networks, and their relation to per¬
ceptual networks
378
5.3
Computational details of the model of short-term memory
381
5.4
Computational necessity for a separate, prefrontal cortex, short-term
memory system
383
5.5
Synaptic modification is needed to set up but not to reuse short-term
memory systems
384
5.6
What, where, and object-place combination short-term memory in the
prefrontal cortex
384
6
Attention, short-term memory, and biased competition
386
6.1
Introduction
386
6.2
The classical view: the spotlight metaphor and feature integration theory
387
6.3
Biased competition
-
single neuron studies
391
6.3.1
Neurophysiology of attention
392
6.3.2
The role of competition
394
6.3.3
Evidence for attentional bias
396
6.3.4
Non-spatial attention
396
6.3.5
High-resolution buffer hypothesis
398
6.4
Biased competition
-
fMRI
398
6.4.1
Neuroimaging of attention
399
6.4.2
Attentional effects in the absence of visual stimulation
399
6.5
A basic computational module for biased competition
401
6.6
Architecture of a model of attention
402
6.7
Simulations of basic experimental findings
407
6.7.1
Simulations of single-cell experiments
408
6.7.2
Simulations of fMRI experiments
410
6.8
Object recognition and spatial search
411
6.8.1
Dynamics of spatial attention and object recognition
414
6.8.2
Dynamics of object attention and visual search
416
6.9
The
neuronal
and biophysical mechanisms of attention
417
6.10
Linking computational and psychophysical data: 'serial' vs 'parallel' pro¬
cessing
421
6.10.1
'Serial' vs 'parallel' search
421
6.10.2
Visual conjunction search
424
6.11
Linking computational and neuropsychological data on attention
429
6.11.1
The neglect syndrome
429
6.11.2
A model of visual spatial neglect
430
6.11.3
Disengagement of attention in neglect
437
6.11.4
Extinction and visual search
438
6.12
Conclusions
440
6.13
Attention
-
a formal model
443
xii Contents
7
Probabilistic decision-making
449
7.1
Introduction
449
7.2
The
neuronal
data underlying the vibrotactile discrimination
451
7.3
Theoretical framework: a probabilistic attractor network
452
7.4
Stationary multistability analysis: mean-field
455
7.5
Non-stationary probabilistic analysis: spiking dynamics
458
7.6
Properties of this model of decision-making
465
7.7
Applications of this model of decision-making
469
7.8
The integrate-and-fire formulation used in the model of decision-making
470
7.9
The mean-field approach used in the model of decision-making
472
7.10
The model parameters used in the simulations of decision-making
474
8
Action selection by biased attractor competition in the pre-
frontal cortex
475
8.1
Introduction
475
8.2
A hierarchical attractor model of action selection
476
8.3
Setting up the synaptic connectivity for the prefrontal cortex
480
8.4
Pharmacology of attention, decision-making, and action selection
483
8.5
Application to a neurodynamical systems hypothesis of schizophrenia
485
8.6
Conclusions
494
9
Reward, decision, and action reversal using attractor dyn¬
amics
496
9.1
Stimulus-reinforcer association learning and reversal
496
9.2
Reversal of action selection
504
9.3
Sequence memory
506
9.4
Conclusions
508
10
Decision-making
509
10.1
Selection of mainly
autonomie
responses, and their classical conditioning
509
10.2
Selection of approach or withdrawal, and their classical conditioning
509
10.3
Selection of fixed stimulus-response habits
510
10.4
Selection of arbitrary behaviours to obtain goals, action-outcome learning,
and emotional learning
510
10.5
The roles of the prefrontal cortex in decision-making and attention
511
10.5.1
Prefrontal attentional influences on perceptual processing
512
10.5.2
Attentional influences on mapping from stimuli to responses
512
10.5.3
Executive control
513
10.5.4
Disorders of attention and decision-making
514
10.6
Neuroeconomics, reward magnitude, expected value, and expected utility
515
10.6.1
Expected utility
Rí
expected value
=
probability multiplied by
reward magnitude
515
10.6.2
Delay of reward, emotional choice, and rational choice
516
10.6.3
Reward prediction error, temporal difference error, and choice
518
Contents
I
xiii
10.6.4
Reciprocal altruism, strong reciprocity, generosity, and altruistic
punishment
519
10.7
Dual routes to action, and decision-making
523
10.8
Apostasie
529
A Introduction to linear algebra for neural networks
531
A.1 Vectors
531
A.1.1 The inner or dot product of two vectors
531
A.
1.2
The length of a vector
532
A.1
.3
Normalizing the length of a vector
533
A.
1.4
The angle between two vectors: the normalized dot product
533
A.1
.5
The outer product of two vectors
534
A.
1.6
Linear and non-linear systems
535
A.1.
7
Linear combinations of vectors, linear independence, and linear
separability
536
A.2 Application to understanding simple neural networks
538
A.2.1 Capability and limitations of single-layer networks: linear separa¬
bility and capacity
538
A.2.2 Non-linear networks: neurons with non-linear activation functions
540
A.2.3 Non-linear networks: neurons with non-linear activations
541
В
Neural network models
543
B.1 Introduction
543
B.2 Pattern association memory
543
B.2.1 Architecture and operation
544
B.2.2 A simple model
547
B.2.3 The vector interpretation
549
B.2.4 Properties
550
B.2.5 Prototype extraction, extraction of central tendency, and noise
reduction
553
B.2.6 Speed
553
B.2.7 Local learning rule
554
B.2.8 Implications of different types of coding for storage in pattern
as¬
sociatore
559
B.3
Autoassociation
or attractor memory
560
B.3.1 Architecture and operation
560
B.3.2 Introduction to the analysis of the operation of
autoassociation
networks
562
B.3.3 Properties
564
B.3.4 Use of
autoassociation
networks in the brain
570
B.4 Competitive networks, including self-organizing maps
571
B.4.1 Function
571
B.4.2 Architecture and algorithm
572
B.4.3 Properties
573
xiv Contents
В,4.4
Utility
of competitive networks
¡η
information processing by the
brain
578
B.4.5 Guidance of competitive learning
579
B.4.6 Topographic map formation
582
B.4.7
Invariance
learning by competitive networks
586
B.4.8 Radial Basis Function networks
588
B.4.9 Further details of the algorithms used in competitive networks
589
B.5 Continuous attractor networks
593
B.5.1 Introduction
593
B.5.2 The generic model of a continuous attractor network
595
B.5.3 Learning the synaptic strengths between the neurons that imple¬
ment a continuous attractor network
595
B.5.4 The capacity of a continuous attractor network: multiple charts
and packets
598
B.5.5 Continuous attractor models: path integration
598
B.5.6 Stabilization of the activity packet within the continuous attractor
network when the agent is stationary
601
B.5.7 Continuous attractor networks in two or more dimensions
603
B.5.8 Mixed continuous and discrete attractor networks
603
B.6 Network dynamics: the integrate-and-fire approach
604
B.6.1 From discrete to continuous time
604
B.6.2 Continuous dynamics with discontinuities
606
B.6.3 An integrate-and-fire implementation
610
B.6.4 Simulation of
f MRI
signals: hemodynamic convolution of synaptic
activity
611
B.6.5 The speed of processing of one-layer attractor networks with
integrate-and-fire neurons
613
B.6.6 The speed of processing of a four-layer hierarchical network with
integrate-and-fire attractor dynamics in each layer
616
B.6.7 Spike response model
619
B.7 Network dynamics: introduction to the mean-field approach
620
B.8 Mean-field based
neurodynamics
621
B.8.1 Population activity
622
B.8.2 A basic computational module based on biased competition
624
B.8.3 Multimodular neurodynamical architectures
625
B.9 Interacting attractor networks
627
B.1
0
Sequence memory implemented by adaptation in an attractor network
631
B.1
1
Error correction networks
631
B.
11.1
Architecture and general description
632
B.1
1.2
Generic algorithm for a one-layer error correction network)
632
B.
11.3
Capability and limitations of single-layer error-correcting networks
633
B.1
1.4
Properties
636
B.1
2
Error backpropagation multilayer networks
638
Contents
I
xv
В.
12,1
Introduction
638
В.12.2
Architecture and algorithm
639
В.
12.3
Properties of multilayer networks trained by error backpropagation
640
B.13 Biologically plausible networks
641
B.14
Contrastive
Hebbian learning: the Boltzmann machine
642
B.15 Reinforcement learning
644
B.15.1 Associative reward-penalty algorithm of Barto and Sutton
645
B.15.2 Reward prediction error or delta rule learning, and classical con¬
ditioning
646
B.15.3 Temporal Difference (TD) learning
647
B.16 Forgetting in associative neural networks and in the brain, and memory
reconsolidation
650
В.
17
Brain computation compared to computation on a digital computer
654
Information theory, and
neuronal
encoding
659
C.1 Information theory
660
C.1
.1
The information conveyed by definite statements
660
C.1.
2
Information conveyed by probabilistic statements
661
С
1.3
Information sources, information channels, and information mea¬
sures
662
C.1
.4
The information carried by
a neuronal
response and its averages
663
C.1
.5
The information conveyed by continuous variables
666
C.2 The information carried by
neuronal
responses
668
C.2.1 The limited sampling problem
668
C.2.2 Correction procedures for limited sampling
669
C.2.3 The information from multiple cells: decoding procedures
670
C.2.4 Information in the correlations between the spikes of different
cells: a decoding approach
674
C.2.5 Information in the correlations between the spikes of different
cells: a second derivative approach
679
C.3 Information theory results
682
C.3.1 The sparseness of the distributed encoding used by the brain
683
C.3.2 The information from single neurons
693
C.3.3 The information from single neurons: temporal codes versus rate
codes within the spike train of a single neuron
697
C.3.4 The information from single neurons: the speed of information
transfer
698
C.3.5 The information from multiple cells: independent information ver¬
sus redundancy across cells
709
C.3.6 Should one neuron be as discriminative as the whole organism,
in object encoding systems?
713
C.3.7 The information from multiple cells: the effects of cross-
correlations between cells
715
xvi Contents
C.3.8
Conclusions on cortical
neuronal
encoding
719
C.4 Information theory terms
-
a short glossary
723
О
Glossary
724
References
726
Index
782
E
Colour Plates
789 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Rolls, Edmund T. |
| author_facet | Rolls, Edmund T. |
| author_role | aut |
| author_sort | Rolls, Edmund T. |
| author_variant | e t r et etr |
| building | Verbundindex |
| bvnumber | BV022960383 |
| callnumber-first | Q - Science |
| callnumber-label | QP406 |
| callnumber-raw | QP406 |
| callnumber-search | QP406 |
| callnumber-sort | QP 3406 |
| callnumber-subject | QP - Physiology |
| classification_rvk | CP 4000 CP 4300 |
| classification_tum | PSY 480f |
| ctrlnum | (OCoLC)171542057 (DE-599)BVBBV022960383 |
| dewey-full | 612.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 612 - Human physiology |
| dewey-raw | 612.8 |
| dewey-search | 612.8 |
| dewey-sort | 3612.8 |
| dewey-tens | 610 - Medicine and health |
| discipline | Psychologie Medizin |
| discipline_str_mv | Psychologie Medizin |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV022960383 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:04:30Z |
| indexdate | 2024-07-09T21:08:37Z |
| institution | BVB |
| isbn | 9780199232703 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016164761 |
| oclc_num | 171542057 |
| open_access_boolean | |
| owner | DE-12 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-20 DE-11 |
| owner_facet | DE-12 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-20 DE-11 |
| physical | XVI, 804 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Rolls, Edmund T. Verfasser aut Memory, attention, and decision-making a unifying computational neuroscience approach Edmund T. Rolls 1. publ. Oxford [u.a.] Oxford Univ. Press 2008 XVI, 804 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Memory Attention Decision making Memory / physiology Attention / physiology Brain / physiology Computer Simulation Decision Making / physiology Aandacht gtt Besluitvorming gtt Geheugen gtt Neuropsychologie gtt Attention physiology Brain physiology Decision Making physiology Memory physiology Physiologische Psychologie (DE-588)4076126-5 gnd rswk-swf Aufmerksamkeit (DE-588)4068943-8 gnd rswk-swf Gedächtnis (DE-588)4019614-8 gnd rswk-swf Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Aufmerksamkeit (DE-588)4068943-8 s Physiologische Psychologie (DE-588)4076126-5 s DE-604 Gedächtnis (DE-588)4019614-8 s Entscheidungsfindung (DE-588)4113446-1 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016164761&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rolls, Edmund T. Memory, attention, and decision-making a unifying computational neuroscience approach Memory Attention Decision making Memory / physiology Attention / physiology Brain / physiology Computer Simulation Decision Making / physiology Aandacht gtt Besluitvorming gtt Geheugen gtt Neuropsychologie gtt Attention physiology Brain physiology Decision Making physiology Memory physiology Physiologische Psychologie (DE-588)4076126-5 gnd Aufmerksamkeit (DE-588)4068943-8 gnd Gedächtnis (DE-588)4019614-8 gnd Entscheidungsfindung (DE-588)4113446-1 gnd |
| subject_GND | (DE-588)4076126-5 (DE-588)4068943-8 (DE-588)4019614-8 (DE-588)4113446-1 |
| title | Memory, attention, and decision-making a unifying computational neuroscience approach |
| title_auth | Memory, attention, and decision-making a unifying computational neuroscience approach |
| title_exact_search | Memory, attention, and decision-making a unifying computational neuroscience approach |
| title_exact_search_txtP | Memory, attention, and decision-making a unifying computational neuroscience approach |
| title_full | Memory, attention, and decision-making a unifying computational neuroscience approach Edmund T. Rolls |
| title_fullStr | Memory, attention, and decision-making a unifying computational neuroscience approach Edmund T. Rolls |
| title_full_unstemmed | Memory, attention, and decision-making a unifying computational neuroscience approach Edmund T. Rolls |
| title_short | Memory, attention, and decision-making |
| title_sort | memory attention and decision making a unifying computational neuroscience approach |
| title_sub | a unifying computational neuroscience approach |
| topic | Memory Attention Decision making Memory / physiology Attention / physiology Brain / physiology Computer Simulation Decision Making / physiology Aandacht gtt Besluitvorming gtt Geheugen gtt Neuropsychologie gtt Attention physiology Brain physiology Decision Making physiology Memory physiology Physiologische Psychologie (DE-588)4076126-5 gnd Aufmerksamkeit (DE-588)4068943-8 gnd Gedächtnis (DE-588)4019614-8 gnd Entscheidungsfindung (DE-588)4113446-1 gnd |
| topic_facet | Memory Attention Decision making Memory / physiology Attention / physiology Brain / physiology Computer Simulation Decision Making / physiology Aandacht Besluitvorming Geheugen Neuropsychologie Attention physiology Brain physiology Decision Making physiology Memory physiology Physiologische Psychologie Aufmerksamkeit Gedächtnis Entscheidungsfindung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016164761&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rollsedmundt memoryattentionanddecisionmakingaunifyingcomputationalneuroscienceapproach |