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Computational neuroscience of vision

Author: Edmund T Rolls; Gustavo Deco
Publisher: Oxford [England] ; New York : Oxford University Press, 2002.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:

This text focuses on the visual information processing and computational operations in the visual system that lead to representations of objects in the brain.

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Edmund T Rolls; Gustavo Deco
ISBN: 9780198524892 0198524897 9780198524885 0198524889
OCLC Number: 48065474
Description: xviii, 569 pages : illustrations ; 25 cm
Contents: 1.2 Neurons 2 --
1.3 Neurons in a network 2 --
1.4 Synaptic modification 4 --
1.5 Long-Term Potentiation and Long-Term Depression 7 --
1.6 Distributed representations 11 --
1.6.2 Advantages of different types of coding 12 --
1.7 Neuronal network approaches versus connectionism 13 --
1.8 Introduction to three neuronal network architectures 14 --
1.9 Systems-level analysis of brain function 16 --
1.10 The fine structure of the cerebral neocortex 21 --
1.10.1 The fine structure and connectivity of the neocortex 21 --
1.10.2 Excitatory cells and connections 21 --
1.10.3 Inhibitory cells and connections 23 --
1.10.4 Quantitative aspects of cortical architecture 25 --
1.10.5 Functional pathways through the cortical layers 27 --
1.10.6 The scale of lateral excitatory and inhibitory effects, and the concept of modules 29 --
1.11 Backprojections in the cortex 30 --
1.11.1 Architecture 30 --
1.11.2 Learning 31 --
1.11.3 Recall 33 --
1.11.4 Semantic priming 34 --
1.11.5 Attention 34 --
1.11.6 Autoassociative storage, and constraint satisfaction 34 --
2 The primary visual cortex 36 --
2.2 Retina and lateral geniculate nuclei 37 --
2.3 Striate cortex: Area V1 43 --
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 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 55 --
2.6 Backprojections to the lateral geniculate nucleus 55 --
3 Extrastriate visual areas 57 --
3.2 Visual pathways in extrastriate cortical areas 57 --
3.3 Colour processing 61 --
3.3.1 Trichromacy theory 61 --
3.3.2 Colour opponency, and colour contrast: Opponent cells 61 --
3.4 Motion and depth processing 65 --
3.4.1 The motion pathway 65 --
3.4.2 Depth perception 67 --
4 The parietal cortex 70 --
4.2 Spatial processing in the parietal cortex 70 --
4.2.1 Area LIP 71 --
4.2.2 Area VIP 73 --
4.2.3 Area MST 74 --
4.2.4 Area 7a 74 --
4.3 The neuropsychology of the parietal lobe 75 --
4.3.1 Unilateral neglect 75 --
4.3.2 Balint's syndrome 77 --
4.3.3 Gerstmann's syndrome 79 --
5 Inferior temporal cortical visual areas 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 --
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 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 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 --
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 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 122 --
6 Visual attentional mechanisms 126 --
6.2 The classical view 126 --
6.2.1 The spotlight metaphor and feature integration theory 126 --
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 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 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 --
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 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 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 --
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 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 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 --
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 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 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 --
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 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 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 --
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 Multimodular neurodynamical architectures 221 --
7.9 Interacting attractor networks 224 --
7.10 Error correction networks 228 --
7.10.1 Architecture and general description 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 --
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 Biologically plausible networks 239 --
7.13 Reinforcement learning 240 --
7.14 Contrastive Hebbian learning: the Boltzmann machine 241 --
8 Models of invariant object recognition 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 --
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 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 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 --
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 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 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 --
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 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 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 --
9.5.1 Dynamics of spatial attention and object recognition 343 --
9.5.2 Dynamics of object attention and visual search 345.
Responsibility: Edmund T. Rolls and Gustavo Deco.
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... useful to anyone who is interested in the underlying principles of visual processing, whether their interest in vision is from the computational, neurobiological, or psychological standpoint." * Read more...

 
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