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

Bibliographic Details
Main Author: Amunts, Katrin.
Other Authors: Grandinetti, Lucio., Lippert, Thomas., Petkov, Nicolai.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Series:Lecture Notes in Computer Science Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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