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

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

Label
Computational neuroscience of vision
Title
Computational neuroscience of vision
Statement of responsibility
Edmund T. Rolls and Gustavo Deco
Creator
Contributor
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
Publication
Bibliography note
Includes bibliographical references (p. [520]-564) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
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
9780198524892
Lccn
2002277312
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)48065474
  • (OCoLC)ocm48065474
Label
Computational neuroscience of vision, Edmund T. Rolls and Gustavo Deco
Publication
Bibliography note
Includes bibliographical references (p. [520]-564) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
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
9780198524892
Lccn
2002277312
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)48065474
  • (OCoLC)ocm48065474

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