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.