Brain computations and connectivity

Brain computations and connectivity

'Brain Computations and Connectivity' is about how the brain works and elucidates what is computed in different brain systems and describes current biologically plausible computational approaches and models of how each of these brain systems computes.

Detalles Bibliográficos
Otros Autores: Rolls, Edmund T., author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Oxford : Oxford University Press 2023.
Edición:Second edition
Colección:Oxford scholarship online.
Materias:
Brain.
Brain > Localization of functions.
Brain
Brain Mapping
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009762661306719
Tabla de Contenidos:
  • Cover
  • Brain Computations and Connectivity
  • Copyright
  • Preface
  • Contents
  • 1: Introduction
  • 1.1 What and how the brain computes: introduction
  • 1.2 What and how the brain computes: plan of the book
  • 1.3 Neurons in the brain, and their representation in neuronal networks
  • 1.4 A formalism for approaching the operation of single neurons in a network
  • 1.5 Synaptic modification
  • 1.6 Long-term potentiation and long-term depression as models of synaptic modification
  • 1.6.1 Long-Term Potentiation
  • 1.6.2 Long-Term Depression
  • 1.6.3 Spike Timing-Dependent Plasticity
  • 1.7 Information encoding by neurons, and distributed representations
  • 1.7.1 Definitions
  • 1.7.2 Advantages of sparse distributed encoding
  • 1.8 Neuronal network approaches versus connectionism
  • 1.9 Introduction to three neuronal network architectures
  • 1.10 Systems-level analysis of brain function
  • 1.11 Brodmann areas
  • 1.12 Human Connectome Project Multi-Modal Parcellation atlas of the human cortex
  • 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
  • 1.14 Introduction to 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 the concept of modules
  • 2: The ventral visual system
  • 2.1 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
  • 2.2 What: V1 - primary visual cortex
  • 2.3 What: V2 and V4 - intermediate processing areas in the ventral visual system
  • 2.4 What: Invariant representations of faces and objects in the inferior temporal visual cortex
  • 2.4.1 Reward value is not represented in the primate ventral visual system
  • 2.4.2 Translation invariant representations
  • 2.4.3 Reduced translation invariance in natural scenes, and the selection of a rewarded object
  • 2.4.4 Size and spatial frequency invariance
  • 2.4.5 Combinations of features in the correct spatial configuration
  • 2.4.6 A view-invariant representation
  • 2.4.7 Learning of new representations in the temporal cortical visual areas
  • 2.4.8 A sparse distributed representation is what is computed in the ventral visual system
  • 2.4.9 Face expression, gesture, and view represented in a population of neurons in the cortex in the superior temporal sulcus
  • 2.4.10 Specialized regions in the temporal cortical visual areas
  • 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
  • 2.5.2 A Visual Cortical Stream to the cortex in the inferior bank of the superior temporal sulcus involved in semantic representations
  • 2.5.3 A Visual Cortical Stream to the cortex in the superior bank of the superior temporal sulcus involved in multimodal semantic representations including visual motion, auditory, somatosensory and social information
  • 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 and 2D viewbasedobject recognition
  • 2.6.6.1 The feature hierarchy approach to object recognition
  • 2.6.6.2 The Cognitron and Neocognitron
  • 2.7 Hypotheses about how the computations are performed in a feature hierarchy approach to for invariant object recognition
  • 2.8 VisNet: a model of how the computations are performed in the ventral visual system
  • 2.8.1 The architecture of VisNet
  • 2.8.1.1 The memory trace learning rule
  • 2.8.1.2 The network implemented in VisNet
  • 2.8.1.3 Competition and lateral inhibition
  • 2.8.1.4 The input to VisNet
  • 2.8.1.5 Measures for network performance
  • 2.8.2 Initial experiments with VisNet
  • 2.8.2.1 'T','L' and '+' as stimuli: learning translation invariance
  • 2.8.2.2 'T','L', and '+' as stimuli: Optimal network parameters
  • 2.8.2.3 Faces as stimuli: translation invariance
  • 2.8.2.4 Faces as stimuli: view invariance
  • 2.8.3 The optimal parameters for the temporal trace used in the learning rule
  • 2.8.4 Different forms of the trace learning rule, and their relation to error correction and temporal difference learning
  • 2.8.4.1 The modified Hebbian trace rule and its relation to error correction
  • 2.8.4.2 Five forms of error correction learning rule
  • 2.8.4.3 Relationship to temporal difference learning
  • 2.8.4.4 Evaluation of the different training rules
  • 2.8.5 The issue of feature binding, and a solution
  • 2.8.5.1 Syntactic binding of separate neuronal ensembles by synchronization
  • 2.8.5.2 SigmaPineurons
  • 2.8.5.3 Binding of features and their relative spatial position by feature combination neurons
  • 2.8.5.4 Discrimination between stimuli with super- and sub-set feature combinations.
  • 2.8.5.5 Feature binding and re-use of feature combinations at different levels of a hierarchical network
  • 2.8.5.6 Feature binding in a hierarchical network with invariant representations of local feature combinations
  • 2.8.5.7 Stimulus generalization to new locations
  • 2.8.5.8 Discussion of feature binding in hierarchical layered networks
  • 2.8.6 Operation in a cluttered environment
  • 2.8.6.1 VisNet simulations with stimuli in cluttered backgrounds
  • 2.8.6.2 Learning invariant representations of an object with multiple objects in the scene and with cluttered backgrounds
  • 2.8.6.3 VisNet simulations with partially occluded stimuli
  • 2.8.7 Learning 3D transforms
  • 2.8.8 Capacity of the architecture, and incorporation of a trace rule into a recurrent architecture with object attractors
  • 2.8.9 Vision in natural scenes - effects of background versus attention
  • 2.8.9.1 Neurophysiology of object selection in the inferior temporal visua lcortex
  • 2.8.9.2 Attention in natural scenes - a computational account
  • 2.8.10 The representation of multiple objects in a scene
  • 2.8.11 Learning invariant representations using spatial continuity: Continuous Spatial Transformation learning
  • 2.8.12 Lighting invariance
  • 2.8.13 Deformation-invariant object recognition
  • 2.8.14 Learning invariant representations of scenes and places
  • 2.8.15 Finding and recognising objects in natural scenes: complementary computations in the dorsal and ventral visual systems
  • 2.8.16 Non-accidental properties, and transform invariant object recognition
  • 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
  • 2.10 Visuo-spatial scratchpad memory, and change blindness
  • 2.11 Different processes involved in different types of object identification
  • 2.12 Top-down attentional modulation is implemented by biased competition
  • 2.13 Highlights on how the computations are performed in the ventral visual system
  • 3: The dorsal visual system
  • 3.1 Introduction, and overview of the dorsal cortical visual stream
  • 3.2 Global motion in the dorsal visual system
  • 3.3 Invariant object-based motion in the dorsal visual system
  • 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
  • 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
  • 3.5.4 Gain modulation by head direction to produce an allocentric bearing to a landmark in Layer 2 of VisNetCT
  • 3.5.5 Gain modulation by place to produce an allocentric spatial view representation in Layer 3 of VisNetCT
  • 3.5.6 The utility of the coordinate transforms in the dorsal visual system
  • 3.6 The human Dorsal Visual Cortical Stream for visual motion leading to the intraparietal visual areas, and then to parietal area 7 regions for actions in space
  • 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, MIP, VIP.

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