توضیحاتی در مورد کتاب Computational Neuroscience: Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers (Communications in Computer and Information Science, 1519)
نام کتاب : Computational Neuroscience: Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers (Communications in Computer and Information Science, 1519)
ویرایش : 1st ed. 2022
عنوان ترجمه شده به فارسی : علوم اعصاب محاسباتی: سومین کارگاه آموزشی آمریکای لاتین، LAWCN 2021، سائو لوئیس، برزیل، 8 تا 10 دسامبر 2021، مقالات منتخب اصلاح شده (ارتباطات در علوم کامپیوتر و اطلاعات، 1519)
سری :
نویسندگان : Paulo Rogério de Almeida Ribeiro (editor), Vinícius Rosa Cota (editor), Dante Augusto Couto Barone (editor), Alexandre César Muniz de Oliveira (editor)
ناشر : Springer
سال نشر : 2022
تعداد صفحات : 276
[268]
ISBN (شابک) : 303108442X , 9783031084423
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 41 Mb
بعد از تکمیل فرایند پرداخت لینک دانلود کتاب ارائه خواهد شد. درصورت ثبت نام و ورود به حساب کاربری خود قادر خواهید بود لیست کتاب های خریداری شده را مشاهده فرمایید.
توضیحاتی در مورد کتاب :
این کتاب مجموعه مقالات داوری سومین کارگاه آموزشی آمریکای لاتین، LAWCN 2021، در سائو لوئیس دو مارانهائو، برزیل، طی 8 تا 10 دسامبر 2021 است.13 مقاله کامل. و 3 مقاله کوتاه موجود در این کتاب با دقت بررسی و از بین 27 مقاله ارسالی انتخاب شدند. آنها در بخش های موضوعی به شرح زیر سازماندهی شدند: کاربردهای بین رشته ای هوش مصنوعی (AI) و یادگیری ماشین (ML). هوش مصنوعی و ML در رباتیک اعمال می شوند. هوش مصنوعی و ML در علوم زیست پزشکی اعمال می شود. مسائل بهداشتی و علوم اعصاب محاسباتی؛ پیاده سازی نرم افزار و سخت افزار در علوم اعصاب؛ و مهندسی عصبی – علم و فناوری.
فهرست مطالب :
Preface
Organization
Contents
Interdisciplinary Applications of Artificial Intelligence (AI) and Machine Learning (ML)
Semantic Segmentation of the Cultivated Area of Plantations with U-Net
1 Introduction
2 Related Works
3 Methodology
3.1 Building the Dataset
3.2 Preprocessing
3.3 Segmentation with U-Net Architecture
3.4 Hyperparameter Optimization
3.5 Performance Evaluation
4 Experiments and Results
4.1 Data Description
4.2 Hyperparameter Test
4.3 Segmentation of Plantation Areas
5 Conclusion
References
Use and Interpretation of Item Response Theory Applied to Machine Learning
1 Introduction
2 Item Response Theory
3 Methodology
3.1 Data
3.2 Experiment Design
4 Results and Discussion
4.1 Experiment 1
4.2 Experiment 2
5 Conclusion
References
AI and ML Applied to Robotics
Towards Loop Closure Detection for SLAM Applications Using Bag of Visual Features: Experiments and Simulation
1 Introduction
2 Related Works
3 Proposed Approach
3.1 Bag of Visual Features
3.2 Multilayer Perceptron
4 Experimental Setup and Results
4.1 Experimental Setup
4.2 Results
4.3 Other Results
5 Discussion and Future Works
References
Loss Function Regularization on the Iterated Racing Procedure for Automatic Tuning of RatSLAM Parameters
1 Introduction
2 The RatSLAM Algorithm
2.1 RatSLAM Structure
2.2 RatSLAM Parameters
3 Improved Tuning Process
4 Experimental Setup
4.1 Environment Setup
4.2 irace Setup
5 Results
6 Discussion
7 Conclusion
References
Controlling the UR3 Robotic Arm Using a Leap Motion: A Comparative Study
1 Introduction
2 Related Works
3 Materials and Methods
3.1 Overview
3.2 Leap Motion
3.3 Experimental Scenarios
3.4 Experimental Procedure
3.5 Coded Control
3.6 Open Control
4 Results
5 Conclusions
References
AI and ML Applied to Biomedical Sciences
Web Service Based Epileptic Seizure Detection by Applying Machine Learning Techniques
1 Introduction
2 Methodology
2.1 Database
2.2 Preprocessing
2.3 Feature Extraction
2.4 Machine Learning
2.5 Web Service
3 Results
3.1 Model Optimization
3.2 Model Selection
3.3 Web Service
4 Conclusion
References
Health Issues and Computational Neuroscience
Machine Learning Search of Novel Selective NaV1.2 and NaV1.6 Inhibitors as Potential Treatment Against Dravet Syndrome
1 Introduction
2 Methods
2.1 Dataset Collection, Curation, and Labeling
2.2 Dataset Partitioning into Training and Test Sets
2.3 Molecular Descriptor Calculation and Modeling Procedure
2.4 Ensemble Learning
2.5 Retrospective Screening
2.6 Use of Positive Predictive Value Surfaces to Choose a Score Threshold for the Prospective Screen
2.7 Prospective Virtual Screen
3 Results and Discussion
4 Conclusions
References
Implementation of Intra and Extracellular Nonperiodic Scale-Free Stimulation in silicofor the NEURON Simulator
1 Introduction
2 Methodology
2.1 Intracellular Stimulation Using Point-Processes
2.2 Extracellular Stimulation Using Field Propagation
2.3 Temporal-Patterned Stimuli
3 Results
3.1 Single Neuron Response to Intracellular Stimulation
3.2 Network Response to Extracellular Stimulation
4 Discussion and Conclusions
References
In silicoInvestigation of the Effects of Distinct Temporal Patterns of Electrical Stimulation to the Amygdala Using a Network of Izhikevich Neurons
1 Introduction
1.1 General
2 Methodology
2.1 Network of Izhikevich Neurons
2.2 Induction of Ictogenesis
2.3 Temporal-Patterned Stimuli
2.4 Experimental Protocol and Assessment of Effects on Synchronization
2.5 Interspike Intervals (ISI) and Coefficient of Variation (CV)
3 Results
4 Discussion and Conclusions
References
Software and Hardware Implementations in Neuroscience
Brain Connectivity Measures in EEG-Based Biometry for Epilepsy Patients: A Pilot Study
1 Introduction
2 Subjects, Materials and Methods
2.1 Data Acquisition and Pre-processing
2.2 Connectivity Measures
2.3 Classification
3 Results and Discussion
4 Conclusion
References
A Multiplatform Output Stage for the Development of Current-Fixed Electrical Stimulators Applied to Neural Electrophysiology
1 Introduction
2 Materials and Methods
2.1 Electronic Circuit Design
2.2 Circuit Tests
2.3 Circuit Assembly
3 Results
3.1 Positive and Negative Current Test
3.2 Polarity Test
3.3 Dead Time Test
4 Discussions and Conclusions
References
Neuroengineering – Science and Technology
Physiological Self-regulation Using Biofeedback Training: From Concept to Clinical Applicability
1 Introduction
2 Principles of System Operation
2.1 Inputs
2.2 Features from Input Signals
3 Real-Time Signal Processing
4 Output Signals: Feedback Stimulation
5 Biofeedback Training Protocols
6 Underlying Mechanisms
7 Clinical Applicability of Biofeedback
8 Final Considerations
References
Movement-Related Electroencephalography in Stroke Patients Across a Brain-Computer Interface-Based Intervention
1 Introduction
2 Materials and Methods
2.1 Patients
2.2 Intervention
2.3 BCI System
2.4 EEG Signal Acquisition
2.5 EEG Signal Processing
2.6 Statistical Analysis
3 Results
3.1 Patients’ Clinical Assessment
3.2 ERD/ERS Brain Topographic Maps
3.3 ERD/ERS Comparisons
3.4 Comparison Between Clinical Recovery and ERD/ERS
4 Discussion
References
Resting-State Exaggerated Alpha Rhythm from Subthalamic Nucleus Discriminates Freezers from Non-freezers Phenotypes in Parkinson’s Disease: Possible Association to Attentional Circuits
1 Introduction
2 Materials and Methods
2.1 Patients
2.2 Surgery
2.3 Signal Processing
2.4 Statistical Analysis
3 Results
4 Discussion
References
Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks
1 Introduction
2 Materials and Methods
2.1 Data Acquisition
2.2 Data Processing
2.3 Feature Extraction, Selection, and Classification
2.4 Experiments
3 Results and Discussion
3.1 Future Work
4 Summary and Conclusion
References
Kinematic Responses as a Control Strategy to Visual Occlusion
1 Introduction
2 Methods
2.1 Participants
2.2 Instruments and Task
2.3 Study Design
2.4 Data Analysis
2.5 Statistical Analysis
3 Results
4 Discussion
5 Conclusion
References
Author Index
توضیحاتی در مورد کتاب به زبان اصلی :
This book constitutes the refereed proceedings of the Third Latin American Workshop, LAWCN 2021, held in Sao Luis do Maranhao, Brazil, during December 8–10, 2021.The 13 full papers and 3 short papers included in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections as follows: Interdisciplinary applications of Artificial Intelligence (AI) and Machine Learning (ML); AI and ML applied to robotics; AI and ML applied to biomedical sciences; Health issues and computational neuroscience; Software and hardware implementations in neuroscience; and Neuroengineering – science and technology.