Brain computations : what and how

목차 (Table of Contents)

• CONTENTS
• 1 Introduction = 1
• 1.1 What and how the brain computes : introduction = 1
• 1.2 What and how the brain computes : plan of the book = 3
• 1.3 Neurons = 5
• 1.4 Neurons in a network = 6
• 1.5 Synaptic modification = 8
• 1.6 Long-term potentiation and long-term depression = 10
• 1.7 Information encoding by neurons, and distributed representations = 14
• 1.8 Neuronal network approaches versus connectionism = 18
• 1.9 Introduction to three neuronal network architectures = 18
• 1.10 Systems-level analysis of brain function = 21
• 1.11 Brodmann areas = 23
• 1.12 The fine structure of the cerebral neocortex = 26
• 2 The ventral visual system = 40
• 2.1 Introduction and overview = 40
• 2.2 What : V1 - primary visual cortex = 47
• 2.3 What : V2 and V4 - intermediate processing areas in the ventral visual system = 48
• 2.4 What : Invariant representations of faces and objects in the inferior temporal visual cortex = 49
• 2.5 How the computations are performed : approaches to invariant object recognition = 71
• 2.6 Hypotheses about how the computations are performed in a feature hierarchy approach = 82
• 2.7 VisNet : a model of how the computations are performed in the ventral visual system = 86
• 2.8 Further approaches to invariant object recognition = 161
• 2.9 Visuo-spatial scratchpad memory, and change blindness = 167
• 2.10 Processes involved in object identification = 169
• 2.11 Top-down attentional modulation is implemented by biased competition = 170
• 2.12 Highlights on how the computations are performed in the ventral visual system = 173
• 3 The dorsal visual system = 176
• 3.1 Introduction, and overview of the dorsal cortical visual stream = 176
• 3.2 Global motion in the dorsal visual system = 177
• 3.3 Invariant object-based motion in the dorsal visual system = 179
• 3.4 What is computed in the dorsal visual system : visual coordinate transforms = 181
• 3.5 How visual coordinate transforms are computed in the dorsal visual system = 185
• 4 The taste and flavour system = 192
• 4.1 Introduction and overview = 192
• 4.2 Taste and related pathways : what is computed = 194
• 4.3 Taste and related pathways : how the computations are performed = 212
• 5 The olfactory system = 217
• 5.1 Introduction = 217
• 5.2 What is computed in the olfactory system = 219
• 5.3 How computations are performed in the olfactory system = 226
• 6 The somatosensory system = 232
• 6.1 What is computed in the somatosensory system = 232
• 6.2 How computations are performed in the somatosensory system = 242
• 7 The auditory system = 244
• 7.1 Introduction, and overview of computations in the auditory system = 244
• 7.2 Auditory Localization = 245
• 7.3 Ventral and dorsal cortical auditory pathways = 248
• 7.4 The ventral cortical auditory stream = 249
• 7.5 The dorsal cortical auditory stream = 251
• 7.6 How the computations are performed in the auditory system = 251
• 8 The temporal cortex = 253
• 8.1 Introduction and overview = 253
• 8.2 Middle temporal gyrus and face expression and gesture = 253
• 8.3 Semantic representations in the temporal lobe neocortex = 255
• 8.4 The mechanisms for semantic learning in the human anterior temporal lobe = 259
• 9 The hippocampus, memory, and spatial function = 260
• 9.1 Introduction and overview = 260
• 9.2 What is computed in the hippocampus = 263
• 9.3 How computations are performed in the hippocampal system = 293
• 9.4 Tests of the theory of hippocampal cortex operation = 344
• 9.5 Comparison with other theories of hippocampal function = 358
• 10 The parietal cortex, spatial functions, and navigation = 363
• 10.1 Introduction and overview = 363
• 10.2 Precuneus and medial area 7 = 365
• 10.3 Navigation : What computations are performed in the parietal and related cortex = 366
• 10.4 How navigation is performed = 367
• 11 The orbitofrontal cortex, amygdala, reward value, and emotion = 379
• 11.1 Introduction and overview = 379
• 11.2 The topology and connections of the orbitofrontal cortex = 383
• 11.3 What is computed in the orbitofrontal cortex = 387
• 11.4 How the computations are performed in the orbitofrontal cortex = 428
• 11.5 Highlights : the special computational roles of the orbitofrontal cortex = 444
• 12 The cingulate cortex = 447
• 12.1 Introduction to and overview of the cingulate cortex = 447
• 12.2 Anterior Cingulate Cortex = 450
• 12.3 Mid-cingulate cortex, the cingulate motor area, and action-outcome learning = 457
• 12.4 The posterior cingulate cortex = 458
• 12.5 How the computations are performed by the cingulate cortex = 459
• 12.6 Synthesis and conclusions = 461
• 13 The motor cortical areas = 464
• 13.1 Introduction and overview = 464
• 13.2 What is computed in different cortical motor-related areas = 464
• 13.3 The mirror neuron system = 466
• 13.4 How the computations are performed in motor cortical and related areas = 467
• 14 The basal ganglia = 468
• 14.1 Introduction and overview = 468
• 14.2 Systems-level architecture of the basal ganglia = 469
• 14.3 What computations are performed by the basal ganglia? = 471
• 14.4 How do the basal ganglia perform their computations? = 485
• 14.5 Comparison of computations for selection in the basal ganglia and cerebral cortex = 494
• 15 Cerebellar cortex = 497
• 15.1 Introduction = 497
• 15.2 Architecture of the cerebellum = 498
• 15.3 Modifiable synapses of parallel fibres onto Purkinje cell dendrites = 502
• 15.4 The cerebellar cortex as a perceptron = 502
• 15.5 Highlights : differences between cerebral and cerebellar cortex microcircuitry = 503
• 16 The prefrontal cortex = 505
• 16.1 Introduction and overview = 505
• 16.2 Divisions of the lateral prefrontal cortex = 508
• 16.3 The lateral prefrontal cortex and top-down attention = 512
• 16.4 How the computations are performed in the prefrontal cortex = 515
• 17 Language and syntax in the brain = 527
• 17.1 Introduction and overview = 527
• 17.2 What is computed in different brain systems to implement language = 529
• 17.3 Hypotheses about how semantic representations are computed = 534
• 17.4 A neurodynamical hypothesis about how syntax is computed = 535
• 18 Cortical attractor dynamics and connectivity, stochasticity, psychiatric disorders, and aging = 554
• 18.1 Introduction and overview = 554
• 18.2 The noisy cortex = 555
• 18.3 Attractor dynamics and schizophrenia = 573
• 18.4 Attractor dynamics and obsessive-compulsive disorder = 582
• 18.5 Depression and attractor dynamics = 586
• 18.6 Attractor stochastic dynamics, aging, and memory = 600
• 18.7 High blood pressure, reduced hippocampal functional connectivity, and impaired memory = 607
• 18.8 Brain development, and structural differences in the brain = 608
• 19 Computations by different types of brain, and by artificial neural systems = 609
• 19.1 Introduction and overview = 609
• 19.2 Computations that combine different computational systems in the brain to produce behaviour = 610
• 19.3 Brain computation compared to computation on a digital computer = 610
• 19.4 Brain computation compared with artificial deep learning networks = 616
• 19.5 Reinforcement Learning = 618
• 19.6 Levels of explanation, and the mind-brain problem = 620
• 19.7 Levels of explanation, and levels of investigation = 622
• 19.8 Brain-Inspired Intelligence = 623
• 19.9 Brain-Inspired Medicine = 624
• 19.10 Primates including humans have different brain organisation than rodents = 628
• A. Introduction to linear algebra for neural networks = 634
• A.1 Vectors = 634
• A.2 Application to understanding simple neural networks = 640
• B. Neuronal network models = 646
• B.1 Introduction = 646
• B.2 Pattern association memory = 646
• B.3 Autoassociation or attractor memory = 663
• B.4 Competitive networks, including self-organizing maps = 686
• B.5 Continuous attractor networks = 707
• B.6 Network dynamics : the integrate-and-fire approach = 718
• B.7 Network dynamics : introduction to the mean-field approach = 732
• B.8 Mean-field based neurodynamics = 733
• B.9 Interacting attractor networks = 742
• B.10 Sequence memory implemented by adaptation in an attractor network = 745
• B.11 Error correction networks = 745
• B.12 Error backpropagation multilayer networks = 753
• B.13 Convolution networks = 757
• B.14 Contrastive Hebbian learning : the Boltzmann machine = 758
• B.15 Deep Belief Networks = 760
• B.16 Reinforcement learning = 760
• B.17 Learning in the neocortex = 767
• B.18 Forgetting in cortical associative neural networks, and memory reconsolidation = 769
• B.19 Highlights = 773
• C. Information theory, and neuronal encoding = 774
• C.1 Information theory = 775
• C.2 The information carried by neuronal responses = 783
• C.3 Information theory results = 797
• C.4 Information theory terms - a short glossary = 838
• C.5 Highlights = 839
• D. Simulation software for neuronal networks, and information analysis of neuronal encoding = 840
• D.1 Introduction = 840
• D.2 Autoassociation or attractor networks = 841
• D.3 Pattern association networks = 843
• D.4 Competitive networks and Self-Organizing Maps = 846
• D.5 Further developments = 848
• D.6 Matlab code for a tutorial version of VisNet = 848
• D.7 Matlab code for information analysis of neuronal encoding = 849
• D.8 Matlab code to illustrate the use of spatial view cells in navigation = 849
• D.9 Highlights = 849
• References = 850
• Index = 924