Brain-Inspired Computing : : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers.
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Superior document: | Lecture Notes in Computer Science Series ; v.12339 |
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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2021. ©2021. |
Year of Publication: | 2021 |
Edition: | 1st ed. |
Language: | English |
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Physical Description: | 1 online resource (163 pages) |
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Amunts, Katrin. Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. 1st ed. Cham : Springer International Publishing AG, 2021. ©2021. 1 online resource (163 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Lecture Notes in Computer Science Series ; v.12339 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Grandinetti, Lucio. Lippert, Thomas. Petkov, Nicolai. Print version: Amunts, Katrin Brain-Inspired Computing Cham : Springer International Publishing AG,c2021 9783030824266 ProQuest (Firm) Lecture Notes in Computer Science Series https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6681091 Click to View |
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Amunts, Katrin. |
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Amunts, Katrin. Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. Lecture Notes in Computer Science Series ; 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. |
author_facet |
Amunts, Katrin. Grandinetti, Lucio. Lippert, Thomas. Petkov, Nicolai. |
author_variant |
k a ka |
author2 |
Grandinetti, Lucio. Lippert, Thomas. Petkov, Nicolai. |
author2_variant |
l g lg t l tl n p np |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_sort |
Amunts, Katrin. |
title |
Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_sub |
4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_full |
Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_fullStr |
Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_full_unstemmed |
Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_auth |
Brain-Inspired Computing : 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15-19, 2019, Revised Selected Papers. |
title_new |
Brain-Inspired Computing : |
title_sort |
brain-inspired computing : 4th international workshop, braincomp 2019, cetraro, italy, july 15-19, 2019, revised selected papers. |
series |
Lecture Notes in Computer Science Series ; |
series2 |
Lecture Notes in Computer Science Series ; |
publisher |
Springer International Publishing AG, |
publishDate |
2021 |
physical |
1 online resource (163 pages) |
edition |
1st ed. |
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. |
isbn |
9783030824273 9783030824266 |
callnumber-first |
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QA76 |
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Electronic books. |
genre_facet |
Electronic books. |
url |
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illustrated |
Not Illustrated |
oclc_num |
1262140800 |
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