Brain computations and connectivity:
"Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to d...
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| Sprache: | English |
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Oxford, United Kingdom
Oxford University Press
[2023]
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics."--Publisher's website |
| Beschreibung: | Formerly CIP. |
| Beschreibung: | xxiii, 1149 Seiten Illustrationen, Diagramme 26 cm |
| ISBN: | 9780198887911 0198887914 |
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| 520 | |a "Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics."--Publisher's website | ||
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Contents 1 2 Introduction 1 1.1 What and how the brain computes: introduction 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 1.6.1 Long-Term Potentiation 1.6.2 Long-Term Depression 1.6.3 Spike Timing-Dependent Plasticity 10 10 13 14 1.7 Information encoding by neurons, and distributed representations 1.7.1 Definitions 1.7.2 Advantages of sparse distributed encoding 15 16 17 1.8 Neuronal network approaches versus connectionism 18 1.9 Introduction to three neuronal network architectures 1 19 1.10 Systems-level analysis of brain function 22 1.11 22 Brodmann areas 1.12 Human Connectome Project Multi-Modal Parcellation atlas of the human cortex 27 1.13 Connectivity of the human brain 1.13.1 Connections analyzed with diffusion tractography 1.13.2 Functional connectivity 1.13.3 Effective connectivity 36 37 37 37 1.14 The fine structure of the cerebral neocortex 1.14.1 The fine structure and connectivity of the neocortex 1.14.2 Excitatory cells and connections 1.14.3 Inhibitory cells and connections 1.14.4 Quantitative aspects of cortical architecture 1.14.5 Functional pathways through the cortical layers 1.14.6 The scale of lateral excitatory and inhibitory effects, and modules 39 39 40 41 44 46 51 The ventral visual system 53 53 53 53 57 Introduction and overview 2.1.1 Introduction 2.1.2 Overview of what is computed in the ventral visual system 2.1.3 Overview of how computations are performed in the ventral visual system 2.1.4 What is computed in the
ventral visual system is unimodal, and is related to other 'what' systems after the inferior temporal visual cortex 58 2.2 What: V1 - primary visual cortex 60 2.3 What: V2 and V4 - intermediate processing areas in the ventral visual system 61 2.1 2.4 What: Invariant representations of faces and objects in the inferior temporal visual cortex 2.4.1 2.4.2 2.4.3 Reward value is not represented in the primate ventral visual system Translation invariant representations Reduced translation invariance in natural scenes 62 62 63 64
xii I Contents 2.4.4 2.4.5 2.4.6 2.4.7 2.4.8 2.4.9 2.4.10 Size and spatial frequency invariance Combinations of features in the correct spatial configuration A view-invariant representation Learning in the inferior temporal cortex A sparse distributed representation is what is computed in the ventral visual system 74 Face expression, gesture, and view Specialized regions in the temporal cortical visual areas 80 80 2.5 The connectivity of the ventral visual pathways in humans 2.5.1 A Ventrolateral Visual Cortical Stream to the inferior temporal visual cortex for object and face representations 86 2.5.2 A Visual Cortical Stream to the cortex in the inferior bank of the superior temporal sulcus involved in semantic representations 89 2.5.3 A Visual Cortical Stream to the cortex in the superior bank of the superior temporal sulcus involved in multimodal semantic representations includ ing visual motion, auditory, somatosensory and social information 85 2.6 How the computations are performed: approaches to invariant object recognition 2.6.1 Feature spaces 2.6.2 Structural descriptions and syntactic pattern recognition 2.6.3 Template matching and the alignment approach 2.6.4 Invertible networks that can reconstruct their inputs 2.6.5 Deep learning 2.6.6 Feature hierarchies 92 93 94 96 97 98 98 2.7 89 Hypotheses about how the computations are performed in a feature hierarchy ap proach 3 67 67 69 71 104 2.8 VisNet: 2.8.1 2.8.2 2.8.3 2.8.4 2.8.5 2.8.6 2.8.7 2.8.8 2.8.9 2.8.10 2.8.11 2.8.12 2.8.13 2.8.14 2.8.15 2.8.16 a model of how the computations are performed in the ventral
visual system The architecture of VisNet Initial experiments with VisNet The optimal parameters for the temporal trace used in the learning rule Different forms of the trace learning rule, and error correction The issue of feature binding, and a solution Operation in a cluttered environment Learning 3D transforms Capacity of the architecture, and an attractor implementation Vision in natural scenes - effects of background versus attention The representation of multiple objects in a scene Learning invariant representations using spatial continuity Lighting invariance Deformation-invariant object recognition Learning invariant representations of scenes and places Finding and recognising objects in natural scenes Non-accidental properties, and transform invariant object recognition 107 108 117 124 126 134 147 154 159 162 170 172 173 175 176 178 181 2.9 Further approaches to invariant object recognition 2.9.1 Other types of slow learning 2.9.2 HMAX 2.9.3 Minimal recognizable configurations 2.9.4 Hierarchical convolutional deep neural networks 2.9.5 Sigma-Pi synapses 2.9.6 A principal dimensions approach to coding in the inferior temporal visual cortex 190 183 183 183 188 189 190 2.10 Visuo-spatial scratchpad memory, and change blindness 192 2.11 Processes involved in object identification 194 2.12 Top-down attentional modulation is implemented by biased competition 195 2.13 Highlights on how the computations are performed in the ventral visual system 198 The dorsal visual system 3.1 Introduction, and overview of the dorsal cortical visual stream 201 201
Contents I xiii 3.2 Global motion in the dorsal visual system 202 3.3 Invariant object-based motion in the dorsal visual system 204 3.4 What is computed in the dorsal visual system: visual coordinate transforms 3.4.1 The transform from retinal to head-based coordinates 3.4.2 The transform from head-based to allocentric bearing coordinates 3.4.3 A transform from allocentric bearing coordinates to allocentric spatial view coordinates 209 206 207 208 3.5 How visual coordinate transforms are computed in the dorsal visual system 3.5.1 Gain modulation 3.5.2 Mechanisms of gain modulation using a trace learning rule 3.5.3 Gain modulation by eye position to produce a head-centered representation in Layer 1 of VisNetCT 213 3.5.4 Gain modulation by head direction to produce an allocentric bearing to a landmark in Layer 2 of VisNetCT 214 3.5.5 Gain modulation by place to produce an allocentric spatial view represent ation in Layer 3 of VisNetCT 215 3.5.6 The utility of the coordinate transforms in the dorsal visual system 211 211 211 The human Dorsal Visual Cortical Stream 3.6.1 Dorsal stream visual division regions 3.6.2 MT+ complex regions (FST, LO1, LO2, LO3, MST, MT, PH, V3CD and V4t) 3.6.3 Intraparietal sulcus posterior parietal cortex, regions (AIP, LIPd, LIPv, MIR VIP; with IPO, IP1 and IP2) 219 3.6.4 Area 7 regions 217 217 218 3.6 4 The taste and flavor system 220 221 4.1 Introduction and overview 4.1.1 Introduction 4.1.2 Overview of what is computed in the taste and flavor system 4.1.3 Overview of how computations are performed in the taste and flavor system 221 221 221 223
4.2 Taste and related pathways: what is computed 4.2.1 Hierarchically organised anatomical pathways 4.2.2 Taste neuronal tuning become more selective through the taste hierarchy 4.2.3 The primary, insular, taste cortex represents what taste is present and its intensity 228 4.2.4 The secondary, orbitofrontal, taste cortex, and its representation of the reward value and pleasantness of taste 229 4.2.5 Sensory-specific satiety is computed in the orbitofrontal cortex 4.2.6 Oral texture is represented in the primary and secondary taste cortex: vis cosity and fat texture 234 4.2.7 Vision and olfaction converge using associative learning with taste to repre sent flavor in the secondary but not primary taste cortex 236 4.2.8 Top-down attention and cognition can modulate taste and flavor represen tations in the taste cortical areas 238 4.2.9 The tertiary taste cortex in the anterior cingulate cortex provides the rewards for action-reward outcome learning 4.2.10 Taste, oral texture and flavor provide the rewards for eating, and the gut provides satiety signals 241 223 223 226 4.3 5 216 Taste and related pathways: how the computations are performed 4.3.1 Increased selectivity of taste and flavor neurons through the hierarchy by competitive learning and convergence 245 4.3.2 Pattern association learning of associations of visual and olfactory stimuli with taste 245 4.3.3 Rule-based reversal of visual to taste associations in the orbitofrontal cortex 4.3.4 Sensory-specific satiety is implemented by adaptation of synapses onto orbitofrontal cortex neurons 246 4.3.5 Top-down cognitive
and attentional modulation is implemented by biased activation 247 The olfactory system 231 240 245 246 251
xiv I Contents 5.1 Introduction 5.1.1 Overview of what is computed in the olfactory system 5.1.2 Overview of how the computations are performed in the olfactory system 5.2 What is computed in the olfactory system 5.2.1 1000 gene-encoded olfactory receptor types, and 1000 corresponding glomerulus types in the olfactory bulb 5.2.2 The primary olfactory, pyriform, cortex: olfactory feature combinations are what is represented 255 5.2.3 Orbitofrontal cortex: olfactory neuronal response selectivity 5.2.4 Orbitofrontal cortex: olfactory to taste convergence 5.2.5 Orbitofrontal cortex: olfactory to taste association learning and reversal 5.2.6 Orbitofrontal cortex: olfactory reward value is represented 5.2.7 Cognitive influences on olfactory representations in the orbitofrontal cortex 5.3 6 The somatosensory system 6.1 6.2 7 253 253 256 257 257 259 260 262 262 263 267 268 What is computed in the somatosensory system 6.1.1 The receptors and periphery 6.1.2 The anterior somatosensory cortex, areas 1,2, 3a, and 3b, in the anterior parietal cortex 268 6.1.3 The ventral somatosensory stream: areas S2 and PV, in the lateral parietal cortex 269 6.1.4 The dorsal somatosensory stream to area 5 and then 7b, in the posterior parietal cortex 270 6.1.5 Somatosensory representations in the insula 6.1.6 Somatosensory and temperature inputs to the orbitofrontal cortex, affective value, pleasant touch, and pain 272 6.1.7 Decision-making in the somatosensory system 6.1.8 Somatosensory cortical regions and connectivity in humans 268 268 How computations are performed in the somatosensory system
6.2.1 Hierarchical computation in the somatosensory system 6.2.2 Computations for pleasant touch and pain 6.2.3 The mechanisms for somatosensory decision-making 284 284 284 285 The auditory system 272 276 278 286 7.1 Introduction, and overview of computations in the auditory system 7.2 Auditory Localization 287 7.3 Ventral and dorsal cortical auditory pathways 290 7.4 The ventral cortical auditory stream 291 7.5 The dorsal cortical auditory stream 293 7.6 Auditory cortical regions and connectivity in humans 7.6.1 Early Auditory cortical regions 7.6.2 Ventral auditory cortical streams 7.6.3 Dorsal auditory cortical streams 7.6.4 Other auditory system cortical connectivities 293 294 294 295 297 7.7 8 How computations are performed in the olfactory system 5.3.1 Olfactory receptors, and the olfactory bulb 5.3.2 Olfactory (pyriform) cortex 5.3.3 Orbitofrontal cortex 251 251 252 How the computations are performed in the auditory system 286 298 The temporal cortex 299 8.1 Introduction and overview 299 8.2 Middle temporal gyrus and face expression and gesture 299 8.3 Semantic representations in the temporal lobe neocortex 8.3.1 Neurophysiology of the medial temporal lobe, including concept cells 301 301
Contents I XV 8.3.2 8.3.3 8.3.4 8.4 8.5 9 Neuropsychology Functional neuroimaging Brain stimulation Connectivity and functions of the human temporal lobe regions related to semantics 8.4.1 Group 1 semantic regions that include regions in the ventral bank of the Superior Temporal Sulcus 8.4.2 Group 3 semantic regions that include regions in the dorsal bank of the Superior Temporal Sulcus 305 The mechanisms for semantic learning in the human anterior temporal lobe 311 The! hippocampus, memory, and spatial function 9.1 9.2 9.3 9.4 9.5 303 303 304 306 309 313 Introduction and overview 9.1.1 Overview of what is computed by the hippocampal system 9.1.2 Overview of how the computations are performed by the hippocampal sys tem 313 313 What is computed in the hippocampus 9.2.1 Systems-level anatomy 9.2.2 Evidence from the effects of damage to the hippocampus 9.2.3 Episodic memories need to be recalled from the hippocampus, and can be used to help build neocortical semantic memories 9.2.4 Systems-level neurophysiology of the primate including human hippo campus 9.2.5 Spatial view cells in primates including humans, and foveal vision 9.2.6 Head direction cells in the presubiculum 9.2.7 Perirhinal cortex, recognition memory, and long-term familiarity memory 9.2.8 Connectivity of the human hippocampal system 316 316 319 How computations are performed in the hippocampal system 9.3.1 Historical development of the theory of the hippocampus 9.3.2 Hippocampal circuitry 9.3.3 Medial entorhinal cortex, spatial processing streams, and grid cells 9.3.4 Lateral entorhinal cortex, object
processing streams, and the generation of time cells in the hippocampus 9.3.5 CA3 as an autoassociation memory Dentate granule cells 9.3.6 9.3.7 CA1 cells Backprojections to the neocortex, memory recall, and consolidation 9.3.8 9.3.9 Backprojections to the neocortex - quantitative aspects 9.3.10 Simulations of hippocampal operation 9.3.11 The learning of spatial view and place cell representations 9.3.12 Linking the inferior temporal visual cortex to spatial view and place cells 9.3.13 A scientific theory of the art of memory: scientia artis memoriae 9.3.14 How navigation is performed Navigational computations using neuron types found in primates including 9.3.15 humans 364 364 366 369 Tests of the theory of hippocampal cortex operation 9.4.1 Dentate gyrus (DG) subregion of the hippocampus 9.4.2 CA3 subregion of the hippocampus 9.4.3 CA1 subregion of the hippocampus 315 321 324 342 344 346 353 372 378 397 400 405 409 413 414 416 418 419 423 432 432 435 442 Comparison of spatial processing and computations in primates including humans vs rodents 9.5.1 Similarities and differences between the spatial representations in primates and rodents 9.5.2 Hippocampal computational similarities and differences between primates and rodents 445 445 447 9.6 Synthesis: the hippocampus: memory, navigation, or both? 450 9.7 Comparison with other theories of hippocampal function 454
xvi I Contents 10 The parietal cortex, spatial functions, and navigation 459 10.1 Introduction and overview 10.1.1 Overview of what is computed in the parietal cortex 10.1.2 Overview of how the computations are performed in the parietal cortex 459 459 460 10.2 Inferior parietal cortex somatosensory stream, PF regions 460 10.3 Intraparietal sulcus posterior parietal cortex, regions AIP, LIPd, LIPv, MIP, VIP, IPO, IP1,andlP2 464 10.4 Posterior superior parietal cortex, regions 7AL, 7Am, 7PC, 7PL, and 7Pm 466 10.5 Inferior parietal cortex, visual regions PFm, PGi, PGs and PGp 10.5.1 Region PGi 10.5.2 Region PGs 10.5.3 Region PFm 10.5.4 Region PGp 467 469 470 470 471 10.6 Navigation: What computations are performed in the parietal and related cortex 473 10.7 How the computations are performed in the parietal cortex 474 11 The orbitofrental cortex, amygdala, reward value, emotion, and decision-making 475 11.1 Introduction and overview 11.1.1 Introduction 11.1.2 Overview of what is computed in the orbitofrontal cortex 11.1.3 Overview of how the computations are performed by the orbitofrontal cortex 475 475 475 478 11.2 The topology and connections of the orbitofrontal cortex 11.2.1 Inputs to the orbitofrontal cortex 11.2.2 Outputs of the orbitofrontal cortex 479 480 482 11.3 What is computed in the orbitofrontal cortex 11.3.1 The orbitofrontal cortex represents reward value 11.3.2 Neuroeconomic value is represented in the orbitofrontal cortex 11.3.3 A representation of face and voice expression and other socially relevant stimuli in the orbitofrontal cortex 492 11.3.4 Negative
reward prediction error neurons in the orbitofrontal cortex 11.3.5 The human medial orbitofrontal cortex represents rewards, and the lateral orbitofrontal cortex non-reward and punishers 500 11.3.6 Decision-making in the orbitofrontal / ventromedial prefrontal cortex 11.3.7 The ventromedial prefrontal cortex and memory 11.3.8 The orbitofrontal cortex and emotion 11.3.9 Emotional orbitofrontal vs rational routes to action 11.3.10 The connectivity of the human orbitofrontal cortex, and its relation to function 11.3.11 Mental problems associated with the orbitofrontal cortex 483 483 490 11.4 11.5 495 502 504 506 508 521 528 What is computed in the amygdala for emotion 11.4.1 Overview of the functions of the amygdala in emotion 11.4.2 The amygdala and the associative processes involved in emotion-related learning 530 11.4.3 Connections of the amygdala 11.4.4 Effects of amygdala lesions 11.4.5 Amygdala lesions in primates 11.4.6 Amygdala lesions in rats 11.4.7 Neuronal activity in the primate amygdala toreinforcing stimuli 11.4.8 Neuronal responses in the amygdala to faces 11.4.9 Evidence from humans 11.4.10 Connectivity of the human amygdala 529 529 How the computations are performed in the orbitofrontal cortex 11.5.1 Decision-making in attractor networks in the brain 11.5.2 Analyses of reward-related decision-making mechanisms in the orbitofrontal cortex 549 11.5.3 A model for reversal learning in the orbitofrontal cortex 544 544 531 532 532 534 535 536 538 539 554
Contents I xvii 11.5.4 11.6 A theory and model of non-reward neural mechanisms in the orbitofrontal cortex 559 Highlights: the special computational roles of the orbitofrontal cortex 12 The cingulate cortex 560 564 12.1 Introduction to and overview of the cingulate cortex 12.1.1 Introduction 12.1.2 Overview of what is computed in the cingulate cortex 12.1.3 Overview of how the computations are performed by the cingulate cortex 564 564 565 567 12.2 Anterior cingulate cortex 12.2.1 Anterior cingulate cortex anatomy and connections in primates 12.2.2 Anterior cingulate cortex: A framework 12.2.3 Anterior cingulate cortex and action-outcome representations 12.2.4 Anterior cingulate cortex lesion effects 12.2.5 Anterior cingulate cortex and ventromedial prefrontal cortex connectivity and functions in humans 12.2.6 Pregenual anterior cingulate representations of reward value, and supracal losal anterior cingulate representations of punishers and non-reward 12.2.7 The human supracallosal anterior cingulate cortex, dACC, and action outcome learning 578 12.2.8 Reward value outputs from the orbitofrontal and pregenual anterior cortex, and vmPFC, to the hippocampal memory system 579 12.2.9 The pregenual anterior cingulate cortex has connectivity with the septal cholinergic system that is involved in memory consolidation 579 12.2.10 Reward value outputs from the orbitofrontal and pregenual anterior cortex, and vmPFC, to the hippocampal system to provide the goals for naviga tion 580 12.2.11 Subgenual cingulate cortex 568 568 569 571 572 572 575 581 Posterior cingulate cortex 12.3.1
Introduction andoverview 12.3.2 Postero-ventral posterior cingulate and medial parietal regions 31 pd, 31 pv, d23ab, v23ab and 7m, and their relation to episodic memory 582 12.3.3 Antero-dorsal Posterior Cingulate Division regions 23d, 31a, PCV; and RSC, POS2, and POS1 ; and their relation to navigation and executive function 12.3.4 Dorsal Visual Transitional area and ProStriate region: the retrosplenial scene area 587 581 581 12.4 Mid-cingulate cortex, the cingulate motor area, and action-outcome learning 588 12.5 How the computations are performed by the cingulate cortex 12.5.1 The anterior cingulate cortex and emotion 12.5.2 Action-outcome learning in the supracallosal anterior cingulate cortex (dACC) 590 12.5.3 Connectivity of the posterior cingulate cortex with the hippocampal memory system 592 589 589 12.6 Synthesis and conclusions 593 12.3 13 The prefrontal cortex 584 596 13.1 Introduction and overview 596 13.2 Divisions of the lateral prefrontal cortex 13.2.1 The dorsolateral prefrontal cortex 13.2.2 The caudal prefrontal cortex 13.2.3 The ventrolateral prefrontal cortex 600 600 603 603 13.3 The connectivity and computational organisation of the human prefrontal cortex 13.3.1 Inferior frontal gyrus 13.3.2 Dorsolateral prefrontal cortex division 603 604 606 13.4 The lateral prefrontal cortex and top-down attention 609 13.5 The frontal pole cortex 612
xviii I Contents 13.6 How the computations are performed in the prefrontal cortex 13.6.1 Cortical short-term memory systems and attractor networks 13.6.2 Prefrontal cortex short-term memory networks, and their relation to percep tual networks 13.6.3 Mapping from one representation to another in short-term memory 13.6.4 The mechanisms of top-down attention 13.6.5 Computational necessity for a separate, prefrontal cortex, short-term mem ory system 13.6.6 Synaptic modification is needed to set up but not to reuse short-term memory systems 13.6.7 Sequence memory 13.6.8 Working memory, and planning 14 Language and syntax in the brain 613 613 615 620 621 622 623 623 623 624 14.1 Introduction and overview 14.1.1 Introduction 14.1.2 Overview 624 624 624 * 14.2 What is computed in different brain systems to implement language 14.2.1 The Wernicke-Lichtheim-Geschwind hypothesis 14.2.2 The dual-stream hypothesis of speech comprehension 14.2.3 Reading requires different brain systems to hearing speech 14.2.4 Semantic representations 14.2.5 Syntactic processing 14.2.6 The parietal cortex: supramarginal and angular gyri 626 626 627 627 629 630 631 14.3 Cortical regions for language and their connectivity in humans 14.3.1 A semantic system that includes the inferior bank of the superior temporal sulcus including object representations 14.3.2 A semantic system that includes the superior bank of the superior temp oral sulcus including visual motion, auditory, somatosensory and social information 14.3.3 Multimodal semantic representations 14.3.4 Broca’s area and related regions (TGv 44 45
47ISFL 55b) 631 632 634 635 637 14.4 Hypotheses about how semantic representations are computed 640 14.5 A neurodynamical hypothesis about how syntax is computed 14.5.1 Binding by synchrony? 14.5.2 Syntax using a place code 14.5.3 Temporal trajectories through a state space of attractors 14.5.4 Hypotheses about the implementation of language in the cerebral cortex 14.5.5 Tests of the hypotheses - a model 14.5.6 Tests of the hypotheses - findings with the model 14.5.7 Evaluation of the hypotheses 14.5.8 Further approaches 641 641 642 642 643 646 651 654 658 15 The motor cortical areas 15.1 Introduction and overview 660 660 15.2 What is computed in different cortical motor-related areas 15.2.1 Ventral parietal and ventral premotor cortex F4 15.2.2 Superior parietal areas with activity related to reaching 15.2.3 Inferior parietal areas with activity related to grasping, and ventral premotor cortex F5 661 661 661 15.3 The mirror neuron system 662 15.4 How the computations are performed in motor cortical and related areas 664 16 The basal ganglia 662 665 16.1 Introduction and overview 665 16.2 Systems-level architecture of the basal ganglia 666
Contents I xix 16.3 What computations are performed by the basal ganglia? 16.3.1 Effects of striatal lesions 16.3.2 Neuronal activity in different parts of the striatum 669 669 670 16.4 How do the basal ganglia perform their computations? 16.4.1 Interaction between neurons and selection of output 16.4.2 Convergence within the basal ganglia, useful for stimulus-response habit learning 686 16.4.3 Dopamine as a reward prediction error signal for reinforcement learning in the striatum 688 683 683 16.5 Comparison of computations for selection in the basal ganglia and cerebral cortex 692 17 Cerebellar cortex 17.1 Introduction 17.2 Architecture of the cerebellum 17.2.1 The connections of the parallel fibres onto the Purkinje cells 17.2.2 The climbing fibre input to the Purkinje cell 17.2.3 The mossy fibre togranule cell connectivity 695 695 697 697 698 698 17.3 Modifiable synapses of parallel fibres onto Purkinje cell dendrites 700 17.4 The cerebellar cortex as a perceptron 701 17.5 Cognitive functions of the cerebellum 17.5.1 Anatomical connections from most neocortical regions 17.5.2 Functional connectivity of different cortical systems with different parts of the cerebellum 703 17.5.3 Activation of different cerebellar cortical regions in different tasks 17.5.4 Damage to different parts of the cerebellum can produce different cognitive, emotional, and motor impairments 703 17.5.5 Neocortical-cerebellar cortical computations for cognition 702 702 Highlights: differences between cerebral and cerebellar cortex microcircuitry 707 17.6 18 Cortical attractor dynamics and
connectivity, stochasticity, psychi atric disorders, and aging 18.1 Introduction and overview 18.1.1 Introduction 18.1.2 Overview 18.2 The noisy cortex 18.2.1 Reasons why the brain is inherently noisy and stochastic 18.2.2 Attractor networks, energylandscapes, and stochastic neurodynamics 18.2.3 A multistable system withnoise 18.2.4 Stochastic dynamics and the stability of short-term memory 18.2.5 Stochastic dynamics in decision-making, and the evolutionary utility of prob abilistic choice 725 18.2.6 Selection between conscious vs unconscious decision-making, and free will 18.2.7 Stochastic dynamics and creative thought 18.2.8 Stochastic dynamics and unpredictable behavior 703 703 709 709 709 709 711 711 714 719 721 726 728 729 18.3 Attractor dynamics and schizophrenia 18.3.1 Introduction 18.3.2 A dynamical systems hypothesis of the symptoms of schizophrenia 18.3.3 Reduced functional connectivity of some brain regions in schizophrenia 18.3.4 Beyond the disconnectivity hypothesis of schizophrenia: reduced forward but not backward connectivity 734 729 729 730 733 18.4 Attractor dynamics and obsessive-compulsive disorder 18.4.1 Introduction 18.4.2 A hypothesis about obsessive-compulsive disorder 18.4.3 Glutamate and increased depth of the basins of attraction 738 738 739 741 18.5 Depression and attractor dynamics 742
XX I Contents 18.6 18.7 18.5.1 18.5.2 18.5.3 18.5.4 18.5.5 18.5.6 18.5.7 Introduction A non-reward attractor theory of depression The orbitofrontal cortex, and the theory of depression Altered connectivity of the orbitofrontal cortex in depression Activations of the orbitofrontal cortex related to depression Implications, and possible treatments, and subtypes of depression Mania and bipolar disorder 742 743 745 746 751 753 756 Attractor 18.6.1 18.6.2 18.6.3 18.6.4 stochastic dynamics, aging, and memory NMDA receptor hypofunction Dopamine and norepinephrine Impaired synaptic modification Cholinergic function and memory 758 758 760 760 761 High blood pressure, reduced hippocampal functional connectivity, and impaired memory 18.8 766 Brain development, and structural differences in the brain 766 19 Computations by different types of brain, and by artificial neural systems 19.1 19.2 Introduction and overview 768 768 Computations that combine different computational systems in the brain to produce 769 behavior 19.3 Brain computation compared to computation on a digital computer 769 19.4 Brain computation compared with artificial deep learning networks 775 19.5 Reinforcement Learning 778 Levels of explanation, and the mind-brain problem 19.6.1 A levels of explanation theory of causality, and the relation between the mind and the brain 780 19.6.2 Downward or Upward Causality? 19.6.3 Consciousness - a Higher Order Syntactic Thought theory 19.6.4 Levels of explanation, and levels of investigation 780 19.7 Biologically plausible computation in the brain: a grand unifying theory? 787
19.8 Brain-Inspired Intelligence 791 19.9 Brain-Inspired Medicine 19.9.1 Computational psychiatry and neurology 19.9.2 Reward systems in the brain, and their application to understanding food intake control and obesity 793 19.9.3 Multiple Routes to Action 792 792 19.6 19.10 Primates 19.10.1 19.10.2 19.10.3 19.10.4 19.10.5 19.10.6 19.10.7 19.10.8 19.10.9 19.10.10 including humans have different brain organisation than rodents The visual system The taste system The olfactory system The somatosensory system The auditory system The hippocampal system, memory,and navigation The orbitofrontal cortex and amygdala The cingulate cortex The motor system Language 782 784 785 796 796 796 797 798 798 799 799 800 801 802 802 A Introduction to linear algebra for neural networks 803 Vectors A. 1.1 Theinnerordot product of two vectors A. 1.2 The length of a vector A.1.3 Normalizing the length of a vector 803 803 805 805 A.1
Contents I xxi The angle between two vectors: the normalized dot product The outer product of two vectors Linear and non-linear systems Linear combinations, linear independence, andlinear separability 805 806 807 808 Application to understanding simple neural networks A.2.1 Capability and limitations of single-layer networks A.2.2 Non-linear networks: neurons with non-linear activation functions A.2.3 Non-linear networks: neurons with non-linear activations 809 810 812 813 A.1.4 A.1.5 A. 1.6 A.1.7 A.2 В Neuronal network models 815 B.1 Introduction 815 B.2 Pattern association memory B.2.1 Architecture and operation B.2.2 A simple model B.2.3 The vector interpretation B.2.4 Properties B.2.5 Prototype extraction, extraction of central tendency, and noisereduction B.2.6 Speed B.2.7 Local learning rule B.2.8 Implications of different types of coding for storage in pattern associators 815 816 818 821 822 825 825 826 831 B.3 Autoassociation or attractor memory B.3.1 Architecture and operation B.3.2 Introduction to the analysis of the operation of autoassociationnetworks B.3.3 Properties B.3.4 Diluted connectivity and the storage capacity of attractor networks B.3.5 Use of autoassociation networks in the brain 832 832 834 836 843 854 B.4 Competitive networks, including self-organizing maps B.4.1 Function B.4.2 Architecture and algorithm B.4.3 Properties B.4.4 Utility of competitive networks in information processing by the brain B.4.5 Guidance of competitive learning B.4.6 Topographic map formation B.4.7 Invariance learning by competitive networks B.4.8 Radial Basis Function
networks B.4.9 Further details of the algorithms used in competitive networks 855 855 856 857 862 864 866 870 871 873 B.5 Continuous attractor networks B.5.1 Introduction B.5.2 The generic model of a continuous attractor network B.5.3 Learning the synaptic strengths in a continuous attractor network B.5.4 The capacity of a continuous attractor network: multiple charts B.5.5 Continuous attractor models: path integration B.5.6 Stabilization of the activity packet within a continuous attractornetwork B.5.7 Continuous attractor networks in two or more dimensions B.5.8 Mixed continuous and discrete attractor networks 876 876 878 879 881 882 884 886 887 B.6 Network dynamics: the integrate-and-fire approach B.6.1 From discrete to continuous time B.6.2 Continuous dynamics with discontinuities B.6.3 An integrate-and-fire implementation B.6.4 The speed of processing of attractor networks B.6.5 The speed of processing of a four-layer hierarchical network B.6.6 Spike response model 888 888 889 893 894 897 900 B.7 Network dynamics: introduction to the mean-field approach 901 B.8 Mean-field based neurodynamics B.8.1 Population activity B.8.2 The mean-field approach used in a model of decision-making B.8.3 The model parameters used in the mean-field analyses of decision-making B.8.4 A basic computational module based on biased competition 902 903 905 907 908
xxii I Contents B.8.5 909 B.9 Interacting attractor networks 911 B.10 Sequence memory implemented by adaptation in anattractor network 915 B.11 Error correction networks B.11.1 Architecture and general description B.11.2 Generic algorithm for a one-layer error correction network B.11.3 Capability and limitations of single-layer error-correcting networks B.11.4 Properties 915 916 916 917 920 B.12 Error backpropagation multilayer networks B.12.1 Introduction B.12.2 Architecture and algorithm B.12.3 Properties of multilayer networks trained by error backpropagation 922 922 923 927 B.13 Deep learning using stochastic gradient descent 928 B.14 Deep convolutional networks 928 B.15 Contrastive Hebbian learning: the Boltzmann machine 930 B.16 Deep Belief Networks 931 B.17 Reinforcement learning B.17.1 Associative reward-penalty algorithm of Barto and Sutton B.17.2 Reward prediction error or delta rule learning, and classicalconditioning B.17.3 Temporal Difference (TD) learning 932 933 934 935 B.18 Learning in the neocortex 938 B.19 Forgetting in cortical associative neural networks,and memory reconsolidation 940 B.20 Genes and self-organization build neural networks in the cortex B.20.1 Introduction B.20.2 Hypotheses about the genes that build cortical neural networks B.20.3 Genetic selection of neuronal network parameters B.20.4 Simulation of the evolution of neural networks using a genetic algorithm B.20.5 Evaluation of the gene-based evolution of single-layer networks B.20.6 The gene-based evolution of multi-layer cortical systems B.20.7 Summary 945 945 946 950 952 961 964 965
B.21 C Multimodular neurodynamical architectures Highlights Neuronal encoding, and information theory 965 967 C.1 Information theory C.1.1 The information conveyed by definite statements C.1.2 Information conveyed by probabilistic statements C.1.3 Information sources, information channels, and information measures C.1.4 The information carried by a neuronal response and its averages C.1.5 The information conveyed by continuous variables 968 968 969 970 971 974 C.2 The information carried by neuronal responses C.2.1 The limited sampling problem C.2.2 Correction procedures for limited sampling C.2.3 The information from multiple cells: decoding procedures C.2.4 Information in the correlations between cells: a decoding approach C.2.5 Information in the correlations between cells: second derivative approach 976 976 977 978 982 987 C.3 Neuronal encoding results C.3.1 The sparseness of the distributed encoding used by the brain C.3.2 The information from single neurons C.3.3 The information from single neurons: temporal codes versus rate codes C.3.4 The information from single neurons: the speed of information transfer C.3.5 The information from multiple cells: independence versus redundancy C.3.6 Should one neuron be as discriminative as the whole organism? C.3.7 The information from multiple cells: the effects of cross-correlations C.3.8 Conclusions on cortical neuronal encoding 990 991 1002 1005 1008 1019 1023 1025 1029
Contents I xxiii C.4 Information theory terms - a short glossary 1033 C.5 Highlights 1034 D Simulation software for neuronal networks, and information analysis of neuronal encoding 1035 D.1 Introduction 1035 D.2 Autoassociation or attractor networks D.2.1 Running the simulation D.2.2 Exercises 1036 1036 1038 D.3 Pattern association networks D.3.1 Running the simulation D.3.2 Exercises 1038 1038 1040 D.4 Competitive networks and Self-Organizing Maps D.4.1 Running the simulation D.4.2 Exercises 1041 1041 1042 D.5 Further developments 1043 D.6 Matlab code for a tutorial version of VIsNet 1043 D.7 Matlab code for information analysis of neuronal encoding 1044 D.8 Matlab code to illustrate the use of spatial view cells in navigation 1044 D.9 The Automated Anatomical Labelling Atlas 3, AAL3 1044 D.10 The extended Human Connectome Project extended atlas, HCPex 1044 D.11 Highlights 1044 Bibliography 1045 Index 1139 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T23:38:00Z |
| indexdate | 2024-07-20T07:54:28Z |
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| isbn | 9780198887911 0198887914 |
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| spelling | Rolls, Edmund T. 1945- Verfasser (DE-588)1150877243 aut Brain computations and connectivity Edmund T. Rolls (Oxford Centre for Computational Neuroscience, Oxford, England) Oxford, United Kingdom Oxford University Press [2023] ©2023 xxiii, 1149 Seiten Illustrationen, Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier Formerly CIP. "Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics."--Publisher's website Brain Brain / Localization of functions Brain chemistry Neural transmission Neural circuitry Neurons Neurology Brain Mapping Brain Chemistry Synaptic Transmission Nerve Net Cerveau Localisation cérébrale Cerveau / Chimie Transmission nerveuse Réseaux nerveux Neurones Neurologie brains aat Brain fast Brain / Localization of functions fast Neuropsychologie (DE-588)4135740-1 gnd rswk-swf Hirnfunktion (DE-588)4159930-5 gnd rswk-swf Neuropsychologie (DE-588)4135740-1 s Hirnfunktion (DE-588)4159930-5 s DE-604 Erscheint auch als Online-Ausgabe 978-0-19-199491-3 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034971686&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rolls, Edmund T. 1945- Brain computations and connectivity Brain Brain / Localization of functions Brain chemistry Neural transmission Neural circuitry Neurons Neurology Brain Mapping Brain Chemistry Synaptic Transmission Nerve Net Cerveau Localisation cérébrale Cerveau / Chimie Transmission nerveuse Réseaux nerveux Neurones Neurologie brains aat Brain fast Brain / Localization of functions fast Neuropsychologie (DE-588)4135740-1 gnd Hirnfunktion (DE-588)4159930-5 gnd |
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| title | Brain computations and connectivity |
| title_auth | Brain computations and connectivity |
| title_exact_search | Brain computations and connectivity |
| title_exact_search_txtP | Brain computations and connectivity |
| title_full | Brain computations and connectivity Edmund T. Rolls (Oxford Centre for Computational Neuroscience, Oxford, England) |
| title_fullStr | Brain computations and connectivity Edmund T. Rolls (Oxford Centre for Computational Neuroscience, Oxford, England) |
| title_full_unstemmed | Brain computations and connectivity Edmund T. Rolls (Oxford Centre for Computational Neuroscience, Oxford, England) |
| title_short | Brain computations and connectivity |
| title_sort | brain computations and connectivity |
| topic | Brain Brain / Localization of functions Brain chemistry Neural transmission Neural circuitry Neurons Neurology Brain Mapping Brain Chemistry Synaptic Transmission Nerve Net Cerveau Localisation cérébrale Cerveau / Chimie Transmission nerveuse Réseaux nerveux Neurones Neurologie brains aat Brain fast Brain / Localization of functions fast Neuropsychologie (DE-588)4135740-1 gnd Hirnfunktion (DE-588)4159930-5 gnd |
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