| Description |
1 online resource (xviii, 569 pages, 2 unnumbered pages of plates) : illustrations (some color) |
| 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 |
| Summary |
This new book from Edmund Rolls presents a unique approach to understanding the complex subject of vision. It will be useful to psychologists interested in vision and attentional processes neuroscientists and vision scientists |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Vision.
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Computational neuroscience.
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Neuropsychology.
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Neurophysiology.
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Neurosciences.
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Computational Biology
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Models, Neurological
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Visual Perception -- physiology
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Computer Simulation
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Neurosciences
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Vision, Ocular
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Neurophysiology
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sight (sense)
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simulation.
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Neurosciences
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Computational neuroscience
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Neurophysiology
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Neuropsychology
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Vision
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| Form |
Electronic book
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| Author |
Deco, Gustavo
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| ISBN |
9780191689277 |
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0191689270 |
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