Cover Image

Brain computations and connectivity /

"The subject of this book is 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 c...

Full description

Bibliographic Details
Main Author: Rolls, Edmund T. (Author)
Format: eBook
Language:English
Published: Oxford, United Kingdom ; New York, NY : Oxford University Press, [2023]
Edition:Second edition.
Subjects:
Brain.
Brain > Localization of functions.
Brain
Brain Mapping
Cerveau.
Localisation cérébrale.
brains.
Brain > Localization of functions
Pre-clinical medicine: basic sciences.
Health and Wellbeing.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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).