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The Resource Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco

Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco

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The item Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.

Creator

Rolls, Edmund T

Contributor

Deco, Gustavo

Language: eng

Work

Publication

Oxford England | New York, Oxford University Press, 2002

Extent: xviii, 569 pages

Contents

4
55
2.6
Backprojections to the lateral geniculate nucleus
55
3
Extrastriate visual areas
57
3.2
Visual pathways in extrastriate cortical areas
57
1.5
3.3
Colour processing
61
3.3.1
Trichromacy theory
61
3.3.2
Colour opponency, and colour contrast: Opponent cells
61
3.4
Long-Term Potentiation and Long-Term Depression
Motion and depth processing
65
3.4.1
The motion pathway
65
3.4.2
Depth perception
67
4
The parietal cortex
7
70
4.2
Spatial processing in the parietal cortex
70
4.2.1
Area LIP
71
4.2.2
Area VIP
73
1.6
4.2.3
Area MST
74
4.2.4
Area 7a
74
4.3
The neuropsychology of the parietal lobe
75
4.3.1
Distributed representations
Unilateral neglect
75
4.3.2
Balint's syndrome
77
4.3.3
Gerstmann's syndrome
79
5
Inferior temporal cortical visual areas
11
81
5.2
Neuronal responses in different areas
81
5.3
The selectivity of one population of neurons for faces
83
5.4
Combinations of face features
84
1.6.2
5.5
Distributed encoding of object and face identity
84
5.5.1
Distributed representations evident in the firing rate distributions
85
5.5.2
The representation of information in the responses of single neurons to a set of stimuli
90
5.5.3
Advantages of different types of coding
The representation of information in the responses of a population of inferior temporal visual cortex neurons
94
5.5.4
Advantages for brain processing of the distributed representation of objects and faces
98
5.5.5
Should one neuron be as discriminative as the whole organism, in object encoding systems?
103
5.5.6
Temporal encoding in the spike train of a single neuron
12
105
5.5.7
Temporal synchronization of the responses of different cortical neurons
108
5.5.8
Conclusions on cortical encoding
111
5.6
Invariance in the neuronal representation of stimuli
112
1.2
1.7
5.6.1
Size and spatial frequency invariance
112
5.6.2
Translation (shift) invariance
113
5.6.3
Reduced translation invariance in natural scenes
113
5.6.4
Neuronal network approaches versus connectionism
A view-independent representation of objects and faces
115
5.7
Face identification and face expression systems
118
5.8
Learning in the inferior temporal cortex
120
5.9
Cortical processing speed
13
122
6
Visual attentional mechanisms
126
6.2
The classical view
126
6.2.1
The spotlight metaphor and feature integration theory
126
1.8
6.2.2
Computational models of visual attention
129
6.3
Biased competition -- single cell studies
132
6.3.1
Neurophysiology of attention
133
6.3.2
Introduction to three neuronal network architectures
The role of competition
135
6.3.3
Evidence of attentional bias
136
6.3.4
Non-spatial attention
136
6.3.5
High-resolution buffer hypothesis
14
139
6.4
Biased competition -- fMRI
140
6.4.1
Neuroimaging of attention
140
6.4.2
Attentional effects in the absence of visual stimulation
141
1.9
6.5
The computational role of top-down feedback connections
142
7
Neural network models
145
7.2
Pattern association memory
145
7.2.1
Systems-level analysis of brain function
Architecture and operation
146
7.2.2
The vector interpretation
149
7.2.3
Properties
150
7.2.4
Prototype extraction, extraction of central tendency, and noise reduction
16
151
7.2.5
Speed
151
7.2.6
Local learning rule
152
7.2.7
Implications of different types of coding for storage in pattern associators
158
1.10
7.3
Autoassociation memory
159
7.3.1
Architecture and operation
160
7.3.2
Introduction to the analysis of the operation of autoassociation networks
161
7.3.3
Neurons
The fine structure of the cerebral neocortex
Properties
163
7.3.4
Use of autoassociation networks in the brain
170
7.4
Competitive networks, including self-organizing maps
171
7.4.1
Function
21
171
7.4.2
Architecture and algorithm
171
7.4.3
Properties
173
7.4.4
Utility of competitive networks in information processing by the brain
178
1.10.1
7.4.5
Guidance of competitive learning
180
7.4.6
Topographic map formation
182
7.4.7
Radial Basis Function networks
187
7.4.8
The fine structure and connectivity of the neocortex
Further details of the algorithms used in competitive networks
188
7.5
Continuous attractor networks
192
7.5.2
The generic model of a continuous attractor network
195
7.5.3
Learning the synaptic strengths between the neurons that implement a continuous attractor network
21
196
7.5.4
The capacity of a continuous attractor network
198
7.5.5
Continuous attractor models: moving the activity packet of neuronal activity
198
7.5.6
Stabilization of the activity packet within the continuous attractor network when the agent is stationary
202
1.10.2
7.5.7
Continuous attractor networks in two or more dimensions
203
7.5.8
Mixed continuous and discrete attractor networks
203
7.6
Network dynamics: the integrate-and-fire approach
204
7.6.1
Excitatory cells and connections
From discrete to continuous time
204
7.6.2
Continuous dynamics with discontinuities
205
7.6.3
Conductance dynamics for the input current
207
7.6.4
The speed of processing of one-layer attractor networks with integrate-and-fire neurons
21
209
7.6.5
The speed of processing of a four-layer hierarchical network with integrate-and-fire attractor dynamics in each layer
212
7.6.6
Spike response model
215
7.7
Network dynamics: introduction to the mean field approach
216
1.10.3
7.8
Mean-field based neurodynamics
218
7.8.1
Population activity
218
7.8.2
A basic computational module based on biased competition
220
7.8.3
Inhibitory cells and connections
Multimodular neurodynamical architectures
221
7.9
Interacting attractor networks
224
7.10
Error correction networks
228
7.10.1
Architecture and general description
2
23
229
7.10.2
Generic algorithm (for a one-layer network taught by error correction)
229
7.10.3
Capability and limitations of single-layer error-correcting networks
230
7.10.4
Properties
234
1.10.4
7.11
Error backpropagation multilayer networks
236
7.11.2
Architecture and algorithm
237
7.11.3
Properties of multilayer networks trained by error backpropagation
238
7.12
Quantitative aspects of cortical architecture
Biologically plausible networks
239
7.13
Reinforcement learning
240
7.14
Contrastive Hebbian learning: the Boltzmann machine
241
8
Models of invariant object recognition
25
243
8.2
Approaches to invariant object recognition
244
8.2.1
Feature spaces
244
8.2.2
Structural descriptions and syntactic pattern recognition
245
1.10.5
8.2.3
Template matching and the alignment approach
247
8.2.4
Invertible networks that can reconstruct their inputs
248
8.2.5
Feature hierarchies
249
8.3
Functional pathways through the cortical layers
Hypotheses about object recognition mechanisms
253
8.4
Computational issues in feature hierarchies
257
8.4.1
The architecture of VisNet
258
8.4.2
Initial experiments with VisNet
27
266
8.4.3
The optimal parameters for the temporal trace used in the learning rule
274
8.4.4
Different forms of the trace learning rule, and their relation to error correction and temporal difference learning
275
8.4.5
The issue of feature binding, and a solution
284
1.10.6
8.4.6
Operation in a cluttered environment
295
8.4.7
Learning 3D transforms
301
8.4.8
Capacity of the architecture, and incorporation of a trace rule into a recurrent architecture with object attractors
307
8.4.9
The scale of lateral excitatory and inhibitory effects, and the concept of modules
Vision in natural scenes -- effects of background versus attention
313
8.5
Synchronization and syntactic binding
319
8.6
Further approaches to invariant object recognition
320
8.7
Processes involved in object identification
29
321
9
The cortical neurodynamics of visual attention -- a model
323
9.2
Physiological constraints
324
9.2.1
The dorsal and ventral paths of the visual cortex
324
1.3
1.11
9.2.2
The biased competition hypothesis
326
9.2.3
Neuronal receptive fields
327
9.3
Architecture of the model
328
9.3.1
Backprojections in the cortex
Overall architecture of the model
328
9.3.2
Formal description of the model
331
9.3.3
Performance measures
336
9.4
Simulations of basic experimental findings
30
336
9.4.1
Simulations of single-cell experiments
337
9.4.2
Simulations of fMRI experiments
339
9.5
Object recognition and spatial search
341
1.11.1
9.5.1
Dynamics of spatial attention and object recognition
343
9.5.2
Dynamics of object attention and visual search
345
Architecture
30
1.11.2
Learning
31
1.11.3
Neurons in a network
Recall
33
1.11.4
Semantic priming
34
1.11.5
Attention
34
1.11.6
Autoassociative storage, and constraint satisfaction
2
34
2
The primary visual cortex
36
2.2
Retina and lateral geniculate nuclei
37
2.3
Striate cortex: Area V1
43
1.4
2.3.1
Classification of V1 neurons
43
2.3.2
Organization of the striate cortex
45
2.3.3
Visual streams within the striate cortex
48
2.4
Synaptic modification
Computational processes that give rise to V1 simple cells
49
2.4.1
Linsker's method: Information maximization
50
2.4.2
Olshausen and Field's method: Sparseness maximization
53
2.5
The computational role of V1 for form processing

Isbn: 9780198524885

Instance

Label: Computational neuroscience of vision

Title: Computational neuroscience of vision

Statement of responsibility: Edmund T. Rolls and Gustavo Deco

Creator

Rolls, Edmund T

Contributor

Deco, Gustavo

Subject

Language: eng

Cataloging source: NLM

http://library.link/vocab/creatorName: Rolls, Edmund T

Illustrations: illustrations

Index: index present

LC call number: QP475

LC item number: .R498 2002

Literary form: non fiction

Nature of contents: bibliography

NLM call number

2002 A-066
WW 105

NLM item number: R755c 2002

http://library.link/vocab/relatedWorkOrContributorName: Deco, Gustavo

http://library.link/vocab/subjectName

Vision
Computational neuroscience
Neuropsychology
Neurophysiology
Computational Biology
Models, Neurological
Visual Perception
Computer Simulation
Neurosciences
Computational neuroscience
Neurophysiology
Neuropsychology
Vision
Visuele waarneming
Neurowetenschappen
Neurociências
Percepção visual
Visão
Vision
Neuroscience informatique
Neuropsychologie
Neurophysiologie
Perception visuelle
Réseau neuronal (Biologie)
Cortex visuel
Sehen
Neurophysiologie
Informationsverarbeitung

Label: Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco

Instantiates

Computational neuroscience of vision

Publication

Oxford England | New York, Oxford University Press, 2002

Bibliography note: Includes bibliographical references (p. [520]-564) and index

Carrier category: volume

Carrier category code

Carrier MARC source: rdacarrier

Content category: text

Content type code

Content type MARC source: rdacontent

Contents

4
55
2.6
Backprojections to the lateral geniculate nucleus
55
3
Extrastriate visual areas
57
3.2
Visual pathways in extrastriate cortical areas
57
1.5
3.3
Colour processing
61
3.3.1
Trichromacy theory
61
3.3.2
Colour opponency, and colour contrast: Opponent cells
61
3.4
Long-Term Potentiation and Long-Term Depression
Motion and depth processing
65
3.4.1
The motion pathway
65
3.4.2
Depth perception
67
4
The parietal cortex
7
70
4.2
Spatial processing in the parietal cortex
70
4.2.1
Area LIP
71
4.2.2
Area VIP
73
1.6
4.2.3
Area MST
74
4.2.4
Area 7a
74
4.3
The neuropsychology of the parietal lobe
75
4.3.1
Distributed representations
Unilateral neglect
75
4.3.2
Balint's syndrome
77
4.3.3
Gerstmann's syndrome
79
5
Inferior temporal cortical visual areas
11
81
5.2
Neuronal responses in different areas
81
5.3
The selectivity of one population of neurons for faces
83
5.4
Combinations of face features
84
1.6.2
5.5
Distributed encoding of object and face identity
84
5.5.1
Distributed representations evident in the firing rate distributions
85
5.5.2
The representation of information in the responses of single neurons to a set of stimuli
90
5.5.3
Advantages of different types of coding
The representation of information in the responses of a population of inferior temporal visual cortex neurons
94
5.5.4
Advantages for brain processing of the distributed representation of objects and faces
98
5.5.5
Should one neuron be as discriminative as the whole organism, in object encoding systems?
103
5.5.6
Temporal encoding in the spike train of a single neuron
12
105
5.5.7
Temporal synchronization of the responses of different cortical neurons
108
5.5.8
Conclusions on cortical encoding
111
5.6
Invariance in the neuronal representation of stimuli
112
1.2
1.7
5.6.1
Size and spatial frequency invariance
112
5.6.2
Translation (shift) invariance
113
5.6.3
Reduced translation invariance in natural scenes
113
5.6.4
Neuronal network approaches versus connectionism
A view-independent representation of objects and faces
115
5.7
Face identification and face expression systems
118
5.8
Learning in the inferior temporal cortex
120
5.9
Cortical processing speed
13
122
6
Visual attentional mechanisms
126
6.2
The classical view
126
6.2.1
The spotlight metaphor and feature integration theory
126
1.8
6.2.2
Computational models of visual attention
129
6.3
Biased competition -- single cell studies
132
6.3.1
Neurophysiology of attention
133
6.3.2
Introduction to three neuronal network architectures
The role of competition
135
6.3.3
Evidence of attentional bias
136
6.3.4
Non-spatial attention
136
6.3.5
High-resolution buffer hypothesis
14
139
6.4
Biased competition -- fMRI
140
6.4.1
Neuroimaging of attention
140
6.4.2
Attentional effects in the absence of visual stimulation
141
1.9
6.5
The computational role of top-down feedback connections
142
7
Neural network models
145
7.2
Pattern association memory
145
7.2.1
Systems-level analysis of brain function
Architecture and operation
146
7.2.2
The vector interpretation
149
7.2.3
Properties
150
7.2.4
Prototype extraction, extraction of central tendency, and noise reduction
16
151
7.2.5
Speed
151
7.2.6
Local learning rule
152
7.2.7
Implications of different types of coding for storage in pattern associators
158
1.10
7.3
Autoassociation memory
159
7.3.1
Architecture and operation
160
7.3.2
Introduction to the analysis of the operation of autoassociation networks
161
7.3.3
Neurons
The fine structure of the cerebral neocortex
Properties
163
7.3.4
Use of autoassociation networks in the brain
170
7.4
Competitive networks, including self-organizing maps
171
7.4.1
Function
21
171
7.4.2
Architecture and algorithm
171
7.4.3
Properties
173
7.4.4
Utility of competitive networks in information processing by the brain
178
1.10.1
7.4.5
Guidance of competitive learning
180
7.4.6
Topographic map formation
182
7.4.7
Radial Basis Function networks
187
7.4.8
The fine structure and connectivity of the neocortex
Further details of the algorithms used in competitive networks
188
7.5
Continuous attractor networks
192
7.5.2
The generic model of a continuous attractor network
195
7.5.3
Learning the synaptic strengths between the neurons that implement a continuous attractor network
21
196
7.5.4
The capacity of a continuous attractor network
198
7.5.5
Continuous attractor models: moving the activity packet of neuronal activity
198
7.5.6
Stabilization of the activity packet within the continuous attractor network when the agent is stationary
202
1.10.2
7.5.7
Continuous attractor networks in two or more dimensions
203
7.5.8
Mixed continuous and discrete attractor networks
203
7.6
Network dynamics: the integrate-and-fire approach
204
7.6.1
Excitatory cells and connections
From discrete to continuous time
204
7.6.2
Continuous dynamics with discontinuities
205
7.6.3
Conductance dynamics for the input current
207
7.6.4
The speed of processing of one-layer attractor networks with integrate-and-fire neurons
21
209
7.6.5
The speed of processing of a four-layer hierarchical network with integrate-and-fire attractor dynamics in each layer
212
7.6.6
Spike response model
215
7.7
Network dynamics: introduction to the mean field approach
216
1.10.3
7.8
Mean-field based neurodynamics
218
7.8.1
Population activity
218
7.8.2
A basic computational module based on biased competition
220
7.8.3
Inhibitory cells and connections
Multimodular neurodynamical architectures
221
7.9
Interacting attractor networks
224
7.10
Error correction networks
228
7.10.1
Architecture and general description
2
23
229
7.10.2
Generic algorithm (for a one-layer network taught by error correction)
229
7.10.3
Capability and limitations of single-layer error-correcting networks
230
7.10.4
Properties
234
1.10.4
7.11
Error backpropagation multilayer networks
236
7.11.2
Architecture and algorithm
237
7.11.3
Properties of multilayer networks trained by error backpropagation
238
7.12
Quantitative aspects of cortical architecture
Biologically plausible networks
239
7.13
Reinforcement learning
240
7.14
Contrastive Hebbian learning: the Boltzmann machine
241
8
Models of invariant object recognition
25
243
8.2
Approaches to invariant object recognition
244
8.2.1
Feature spaces
244
8.2.2
Structural descriptions and syntactic pattern recognition
245
1.10.5
8.2.3
Template matching and the alignment approach
247
8.2.4
Invertible networks that can reconstruct their inputs
248
8.2.5
Feature hierarchies
249
8.3
Functional pathways through the cortical layers
Hypotheses about object recognition mechanisms
253
8.4
Computational issues in feature hierarchies
257
8.4.1
The architecture of VisNet
258
8.4.2
Initial experiments with VisNet
27
266
8.4.3
The optimal parameters for the temporal trace used in the learning rule
274
8.4.4
Different forms of the trace learning rule, and their relation to error correction and temporal difference learning
275
8.4.5
The issue of feature binding, and a solution
284
1.10.6
8.4.6
Operation in a cluttered environment
295
8.4.7
Learning 3D transforms
301
8.4.8
Capacity of the architecture, and incorporation of a trace rule into a recurrent architecture with object attractors
307
8.4.9
The scale of lateral excitatory and inhibitory effects, and the concept of modules
Vision in natural scenes -- effects of background versus attention
313
8.5
Synchronization and syntactic binding
319
8.6
Further approaches to invariant object recognition
320
8.7
Processes involved in object identification
29
321
9
The cortical neurodynamics of visual attention -- a model
323
9.2
Physiological constraints
324
9.2.1
The dorsal and ventral paths of the visual cortex
324
1.3
1.11
9.2.2
The biased competition hypothesis
326
9.2.3
Neuronal receptive fields
327
9.3
Architecture of the model
328
9.3.1
Backprojections in the cortex
Overall architecture of the model
328
9.3.2
Formal description of the model
331
9.3.3
Performance measures
336
9.4
Simulations of basic experimental findings
30
336
9.4.1
Simulations of single-cell experiments
337
9.4.2
Simulations of fMRI experiments
339
9.5
Object recognition and spatial search
341
1.11.1
9.5.1
Dynamics of spatial attention and object recognition
343
9.5.2
Dynamics of object attention and visual search
345
Architecture
30
1.11.2
Learning
31
1.11.3
Neurons in a network
Recall
33
1.11.4
Semantic priming
34
1.11.5
Attention
34
1.11.6
Autoassociative storage, and constraint satisfaction
2
34
2
The primary visual cortex
36
2.2
Retina and lateral geniculate nuclei
37
2.3
Striate cortex: Area V1
43
1.4
2.3.1
Classification of V1 neurons
43
2.3.2
Organization of the striate cortex
45
2.3.3
Visual streams within the striate cortex
48
2.4
Synaptic modification
Computational processes that give rise to V1 simple cells
49
2.4.1
Linsker's method: Information maximization
50
2.4.2
Olshausen and Field's method: Sparseness maximization
53
2.5
The computational role of V1 for form processing

Dimensions: 25 cm

Extent: xviii, 569 pages

Isbn: 9780198524885

Lccn: 2002277312

Media category: unmediated

Media MARC source: rdamedia

Media type code

Other physical details: illustrations

System control number

(OCoLC)48065474
(OCoLC)ocm48065474

Label: Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco

Publication

Oxford England | New York, Oxford University Press, 2002

Bibliography note: Includes bibliographical references (p. [520]-564) and index

Carrier category: volume

Carrier category code

Carrier MARC source: rdacarrier

Content category: text

Content type code

Content type MARC source: rdacontent

Contents

4
55
2.6
Backprojections to the lateral geniculate nucleus
55
3
Extrastriate visual areas
57
3.2
Visual pathways in extrastriate cortical areas
57
1.5
3.3
Colour processing
61
3.3.1
Trichromacy theory
61
3.3.2
Colour opponency, and colour contrast: Opponent cells
61
3.4
Long-Term Potentiation and Long-Term Depression
Motion and depth processing
65
3.4.1
The motion pathway
65
3.4.2
Depth perception
67
4
The parietal cortex
7
70
4.2
Spatial processing in the parietal cortex
70
4.2.1
Area LIP
71
4.2.2
Area VIP
73
1.6
4.2.3
Area MST
74
4.2.4
Area 7a
74
4.3
The neuropsychology of the parietal lobe
75
4.3.1
Distributed representations
Unilateral neglect
75
4.3.2
Balint's syndrome
77
4.3.3
Gerstmann's syndrome
79
5
Inferior temporal cortical visual areas
11
81
5.2
Neuronal responses in different areas
81
5.3
The selectivity of one population of neurons for faces
83
5.4
Combinations of face features
84
1.6.2
5.5
Distributed encoding of object and face identity
84
5.5.1
Distributed representations evident in the firing rate distributions
85
5.5.2
The representation of information in the responses of single neurons to a set of stimuli
90
5.5.3
Advantages of different types of coding
The representation of information in the responses of a population of inferior temporal visual cortex neurons
94
5.5.4
Advantages for brain processing of the distributed representation of objects and faces
98
5.5.5
Should one neuron be as discriminative as the whole organism, in object encoding systems?
103
5.5.6
Temporal encoding in the spike train of a single neuron
12
105
5.5.7
Temporal synchronization of the responses of different cortical neurons
108
5.5.8
Conclusions on cortical encoding
111
5.6
Invariance in the neuronal representation of stimuli
112
1.2
1.7
5.6.1
Size and spatial frequency invariance
112
5.6.2
Translation (shift) invariance
113
5.6.3
Reduced translation invariance in natural scenes
113
5.6.4
Neuronal network approaches versus connectionism
A view-independent representation of objects and faces
115
5.7
Face identification and face expression systems
118
5.8
Learning in the inferior temporal cortex
120
5.9
Cortical processing speed
13
122
6
Visual attentional mechanisms
126
6.2
The classical view
126
6.2.1
The spotlight metaphor and feature integration theory
126
1.8
6.2.2
Computational models of visual attention
129
6.3
Biased competition -- single cell studies
132
6.3.1
Neurophysiology of attention
133
6.3.2
Introduction to three neuronal network architectures
The role of competition
135
6.3.3
Evidence of attentional bias
136
6.3.4
Non-spatial attention
136
6.3.5
High-resolution buffer hypothesis
14
139
6.4
Biased competition -- fMRI
140
6.4.1
Neuroimaging of attention
140
6.4.2
Attentional effects in the absence of visual stimulation
141
1.9
6.5
The computational role of top-down feedback connections
142
7
Neural network models
145
7.2
Pattern association memory
145
7.2.1
Systems-level analysis of brain function
Architecture and operation
146
7.2.2
The vector interpretation
149
7.2.3
Properties
150
7.2.4
Prototype extraction, extraction of central tendency, and noise reduction
16
151
7.2.5
Speed
151
7.2.6
Local learning rule
152
7.2.7
Implications of different types of coding for storage in pattern associators
158
1.10
7.3
Autoassociation memory
159
7.3.1
Architecture and operation
160
7.3.2
Introduction to the analysis of the operation of autoassociation networks
161
7.3.3
Neurons
The fine structure of the cerebral neocortex
Properties
163
7.3.4
Use of autoassociation networks in the brain
170
7.4
Competitive networks, including self-organizing maps
171
7.4.1
Function
21
171
7.4.2
Architecture and algorithm
171
7.4.3
Properties
173
7.4.4
Utility of competitive networks in information processing by the brain
178
1.10.1
7.4.5
Guidance of competitive learning
180
7.4.6
Topographic map formation
182
7.4.7
Radial Basis Function networks
187
7.4.8
The fine structure and connectivity of the neocortex
Further details of the algorithms used in competitive networks
188
7.5
Continuous attractor networks
192
7.5.2
The generic model of a continuous attractor network
195
7.5.3
Learning the synaptic strengths between the neurons that implement a continuous attractor network
21
196
7.5.4
The capacity of a continuous attractor network
198
7.5.5
Continuous attractor models: moving the activity packet of neuronal activity
198
7.5.6
Stabilization of the activity packet within the continuous attractor network when the agent is stationary
202
1.10.2
7.5.7
Continuous attractor networks in two or more dimensions
203
7.5.8
Mixed continuous and discrete attractor networks
203
7.6
Network dynamics: the integrate-and-fire approach
204
7.6.1
Excitatory cells and connections
From discrete to continuous time
204
7.6.2
Continuous dynamics with discontinuities
205
7.6.3
Conductance dynamics for the input current
207
7.6.4
The speed of processing of one-layer attractor networks with integrate-and-fire neurons
21
209
7.6.5
The speed of processing of a four-layer hierarchical network with integrate-and-fire attractor dynamics in each layer
212
7.6.6
Spike response model
215
7.7
Network dynamics: introduction to the mean field approach
216
1.10.3
7.8
Mean-field based neurodynamics
218
7.8.1
Population activity
218
7.8.2
A basic computational module based on biased competition
220
7.8.3
Inhibitory cells and connections
Multimodular neurodynamical architectures
221
7.9
Interacting attractor networks
224
7.10
Error correction networks
228
7.10.1
Architecture and general description
2
23
229
7.10.2
Generic algorithm (for a one-layer network taught by error correction)
229
7.10.3
Capability and limitations of single-layer error-correcting networks
230
7.10.4
Properties
234
1.10.4
7.11
Error backpropagation multilayer networks
236
7.11.2
Architecture and algorithm
237
7.11.3
Properties of multilayer networks trained by error backpropagation
238
7.12
Quantitative aspects of cortical architecture
Biologically plausible networks
239
7.13
Reinforcement learning
240
7.14
Contrastive Hebbian learning: the Boltzmann machine
241
8
Models of invariant object recognition
25
243
8.2
Approaches to invariant object recognition
244
8.2.1
Feature spaces
244
8.2.2
Structural descriptions and syntactic pattern recognition
245
1.10.5
8.2.3
Template matching and the alignment approach
247
8.2.4
Invertible networks that can reconstruct their inputs
248
8.2.5
Feature hierarchies
249
8.3
Functional pathways through the cortical layers
Hypotheses about object recognition mechanisms
253
8.4
Computational issues in feature hierarchies
257
8.4.1
The architecture of VisNet
258
8.4.2
Initial experiments with VisNet
27
266
8.4.3
The optimal parameters for the temporal trace used in the learning rule
274
8.4.4
Different forms of the trace learning rule, and their relation to error correction and temporal difference learning
275
8.4.5
The issue of feature binding, and a solution
284
1.10.6
8.4.6
Operation in a cluttered environment
295
8.4.7
Learning 3D transforms
301
8.4.8
Capacity of the architecture, and incorporation of a trace rule into a recurrent architecture with object attractors
307
8.4.9
The scale of lateral excitatory and inhibitory effects, and the concept of modules
Vision in natural scenes -- effects of background versus attention
313
8.5
Synchronization and syntactic binding
319
8.6
Further approaches to invariant object recognition
320
8.7
Processes involved in object identification
29
321
9
The cortical neurodynamics of visual attention -- a model
323
9.2
Physiological constraints
324
9.2.1
The dorsal and ventral paths of the visual cortex
324
1.3
1.11
9.2.2
The biased competition hypothesis
326
9.2.3
Neuronal receptive fields
327
9.3
Architecture of the model
328
9.3.1
Backprojections in the cortex
Overall architecture of the model
328
9.3.2
Formal description of the model
331
9.3.3
Performance measures
336
9.4
Simulations of basic experimental findings
30
336
9.4.1
Simulations of single-cell experiments
337
9.4.2
Simulations of fMRI experiments
339
9.5
Object recognition and spatial search
341
1.11.1
9.5.1
Dynamics of spatial attention and object recognition
343
9.5.2
Dynamics of object attention and visual search
345
Architecture
30
1.11.2
Learning
31
1.11.3
Neurons in a network
Recall
33
1.11.4
Semantic priming
34
1.11.5
Attention
34
1.11.6
Autoassociative storage, and constraint satisfaction
2
34
2
The primary visual cortex
36
2.2
Retina and lateral geniculate nuclei
37
2.3
Striate cortex: Area V1
43
1.4
2.3.1
Classification of V1 neurons
43
2.3.2
Organization of the striate cortex
45
2.3.3
Visual streams within the striate cortex
48
2.4
Synaptic modification
Computational processes that give rise to V1 simple cells
49
2.4.1
Linsker's method: Information maximization
50
2.4.2
Olshausen and Field's method: Sparseness maximization
53
2.5
The computational role of V1 for form processing

Dimensions: 25 cm

Extent: xviii, 569 pages

Isbn: 9780198524885

Lccn: 2002277312

Media category: unmediated

Media MARC source: rdamedia

Media type code

Other physical details: illustrations

System control number

(OCoLC)48065474
(OCoLC)ocm48065474

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