Brain-Inspired Computing : : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers.

• Superior document: Lecture Notes in Computer Science Series ; v.12339
• Author: Amunts, Katrin.
• TeilnehmendeR: Grandinetti, Lucio.; Lippert, Thomas.; Petkov, Nicolai.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: Lecture Notes in Computer Science Series
• Physical Description: 1 online resource (163 pages)

Table of Contents

• Intro
• 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.