Preface
Organization
Contents
Machine Learning and Deep Learning Approaches in Human Brain Mapping
A High-Resolution Model of the Human Entorhinal Cortex in the ‘BigBrain’ – Use Case for Machine Learning and 3D Analyses
1 Introduction
2 Material and Methods
2.1 Histological Processing and 3D-Reconstruction of ‘BigBrain’
2.2 Border Definition and Annotation of the EC in A3D
2.3 Segmentation of Pre-α Islands in Ilastik
2.4 Analysis of Pre-α Islands in ImageJ
2.5 Visualization of the EC and the Included Pre-α Islands in the Context of the Entire ‘BigBrain’ Data Set
3 Results
3.1 Overview of the Layers of the EC
3.2 Cytoarchitecture of Layer 2 (Pre-α Islands) and Modifications Along the Rostrocaudal Extent
3.3 Surface and Morphological Features of Pre-α Islands in EC
3.4 Number and Distribution of Pre-α Islands
4 Discussion
References
Deep Learning-Supported Cytoarchitectonic Mapping of the Human Lateral Geniculate Body in the BigBrain
1 Introduction
2 Materials and Methods
2.1 Histology
2.2 Manual Analysis and Reference Mapping of Histological Sections
2.3 Training of the Deep-Learning Algorithm to Predict Missing Delineations
3 Results
3.1 Cytoarchitectonic Mapping Based on Expert Annotations and Deep Learning
3.2 High-Resolution 3D Reconstruction
3.3 Volumes of Layers
4 Discussion and Conclusion
References
Brain Modelling and Simulation
Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout
1 Cerebellar Transcranial Magnetic Stimulation
2 Experimental Protocols
3 Computational Modelling
4 Comparative Analysis
5 Discussion and Conclusions
References
Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex
1 Introduction
2 Overview of the Multi-Area Model
3 The Multi-Area Model Workflow
4 Example Usage
5 Strong Scaling
6 Conclusions
References
Exascale Compute and Data Infrastructures for Neuroscience and Applications
Modular Supercomputing for Neuroscience
1 Introduction
2 The Modular Supercomputing Architecture (MSA)
3 Current Hardware Platforms
3.1 JURECA Cluster-Booster
3.2 DEEP-EST Prototype
4 Software Environment
4.1 Scheduling
4.2 Programming Environment
5 Neuroscience Workflow on MSA
5.1 NEST
5.2 Arbor
6 Summary
References
Fenix: Distributed e-Infrastructure Services for EBRAINS
1 Introduction
2 Fenix Concept
3 Fenix Compute and Data Services
4 Selected EBRAINS Services
5 Resource Allocation
6 Summary and Outlook
References
Independent Component Analysis for Noise and Artifact Removal in Three-Dimensional Polarized Light Imaging
1 Introduction
2 Methods
2.1 Preparation of Brain Sections
2.2 Three-Dimensional Polarized Light Imaging (3D-PLI)
2.3 Segmentation of White and Gray Matter
2.4 Independent Component Analysis (ICA)
2.5 Automatic Noise Removal with ICA
3 Results
4 Discussion
References
Exascale Artificial and Natural Neural Architectures
Brain-Inspired Algorithms for Processing of Visual Data
1 Introduction
2 Brain-Inspired Processing of Visual Data
2.1 Edge and Line Detection
2.2 Object(-part) Detection
2.3 Inhibition for Image Processing
3 Convolutional Networks for Visual Data Processing
3.1 Inhibition in Convolutional Networks
4 Conclusions
References
An Hybrid Attention-Based System for the Prediction of Facial Attributes
1 Introduction
2 Prediction of Facial Attributes
2.1 The Hierarchical HMAX Network
2.2 Local Texture Description Based on LBP
2.3 Binary Classification with Support Vector Machines
3 Experimental Results
4 Conclusion
References
The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios
1 Introduction
2 Statistical Physics of Learning: Learning Curves
2.1 Learning a Linearly Separable Rule: Student and Teacher
2.2 The Density of Input Data
2.3 Generalization Error and the Perceptron Order Parameter
2.4 Training as a Stochastic Process and Thermal Equilibrium
2.5 Disorder Average and High-Temperature Limit
2.6 Two Concrete Examples
3 Summary and Conclusion
References
Emotion Mining: from Unimodal to Multimodal Approaches
1 Introduction
2 Emotion Theories
2.1 Discrete Theories of Emotions
2.2 Dimensional Emotional Models
3 Basic Unimodal Emotion Recognition Approaches
3.1 Emotion Recognition from Textual Sources
3.2 Affective Computing Methodologies
3.3 Emotion Recognition from Facial Expression
3.4 Emotion Recognition from Speech
4 Deep Learning Algorithms for Emotion Detection
5 Challenges and Tools for Multimodal Emotion Recognition
5.1 Existing Multimodal Dataset for Emotion Recognition
6 Conclusions
References
Author Index