Computational Neuroscience
Computational Neuroscience is a collection of recent advances in computational studies in neuroscience research that practically applies to a collaborative and integrative environment in engineering and medical domains. The aim of the author is to address the explosion of interest by academic researchers and practitioners in highly-effective coordination between computational models and tools and quantitative investigation of neuroscientific data. This is to bridge the vital gap between science and medicine. This book brings together diverse research areas ranging from medical signal processing, image analysis, and data mining to neural network modeling, regulation of gene expression, and brain dynamics. The book is good for researchers from engineering, computer science, statistics, and mathematics domains as well as medical and biological scientists; and physicians working in scientific research to understand how basic science can be linked with biological systems.
The first six chapters focused on data mining and medical data processing.
First chapter, presenting a complete methodological framework based on optimization for reproducing. Second chapter, proposed graph-theoretic models to investigate functional cooperation in the human brain.
Third chapter, proposed a framework for extracting time frequency features from electroencephalographic (EEG) recordings through the use of wavelet analysis.
Fourth chapter, presented an application of independent component analysis (ICA) transformation into Creutzfeldt–Jakob disease.
Fifth chapter discussed a comparison study of classification methods using various data preprocessing procedures applied to functional magnetic resonance imaging (fMRI) data for the detection of brain activation.
Sixth chapter discussed the most well known methods in biclustering applied to a neuroscientific application in evaluating the therapeutic intervention using vagus nerve stimulation treatment for patients with epilepsy.
Seventh chapter proposed a genetic classifier used in the study of gene expression regulation.
The second theme includes five chapters that provide reviews and challenges in brain modeling in respect of human behavior and brain disease.
Eighth chapter provided a review of the inverse source localization problem for neuroelectromagnetic source imaging of brain dynamics.
Ninth chapter proposed an approach based on the queuing theory and reinforcement learning for modeling the brain function and interpreting the human behavior.
Tenth and eleventh chapters, suggests deterministic mathematical model for modeling neural networks of voluntary single-joint movement organization in normal subjects as well as patients with Parkinson’s disease.
Twelfth chapter proposed a parametric model for optical time series data of the respiratory
neural network in the brainstem.
Thirteenth chapter give an overview of the closed-loop deep brain stimulation technology.
Fourteenth chapter present a novel approach to
build fine grain models of the human brain with a large number of neurons inspired by recent advances in computing based on DNA modecules.
The third theme includes six chapters that focus on quantitative analyses of EEG recordings to investigate the brain dynamics and neural synchronization.
Fifteenth chapter, investigate the synchronization in the neural networks based on information flow, measured by the metric of network transfer entropy, among different brain areas.
Sixteenth chapter, describe an optimization-based model for estimating all Lyapunov exponents to characterize the dynamics of EEG recordings.
Seventeenth chapter, report the potential use of nonlinear dynamics for analyzing EEG recordings to evaluate the efficacy of antiepileptic drugs.
Eighteenth chapter, study the synchronization of EEG recordings using the measures of phase synchronization and cointergrated VAR.
Nineteenth chapter, use the concept of mutual information to measure the coupling strength of EEG recordings in order to evaluate the efficacy of antiepileptic drugs in a very rare brain disease.
In the last chapter, propose a seizure monitoring and alert system to be used in an intensive care unit based on statistical analyses of EEG recordings.
Computational Neuroscience is a collection of recent advances in computational studies in neuroscience research that practically applies to a collaborative and integrative environment in engineering and medical domains. The aim of the author is to address the explosion of interest by academic researchers and practitioners in highly-effective coordination between computational models and tools and quantitative investigation of neuroscientific data. This is to bridge the vital gap between science and medicine. This book brings together diverse research areas ranging from medical signal processing, image analysis, and data mining to neural network modeling, regulation of gene expression, and brain dynamics. The book is good for researchers from engineering, computer science, statistics, and mathematics domains as well as medical and biological scientists; and physicians working in scientific research to understand how basic science can be linked with biological systems.
The first six chapters focused on data mining and medical data processing.
First chapter, presenting a complete methodological framework based on optimization for reproducing. Second chapter, proposed graph-theoretic models to investigate functional cooperation in the human brain.
Third chapter, proposed a framework for extracting time frequency features from electroencephalographic (EEG) recordings through the use of wavelet analysis.
Fourth chapter, presented an application of independent component analysis (ICA) transformation into Creutzfeldt–Jakob disease.
Fifth chapter discussed a comparison study of classification methods using various data preprocessing procedures applied to functional magnetic resonance imaging (fMRI) data for the detection of brain activation.
Sixth chapter discussed the most well known methods in biclustering applied to a neuroscientific application in evaluating the therapeutic intervention using vagus nerve stimulation treatment for patients with epilepsy.
Seventh chapter proposed a genetic classifier used in the study of gene expression regulation.
The second theme includes five chapters that provide reviews and challenges in brain modeling in respect of human behavior and brain disease.
Eighth chapter provided a review of the inverse source localization problem for neuroelectromagnetic source imaging of brain dynamics.
Ninth chapter proposed an approach based on the queuing theory and reinforcement learning for modeling the brain function and interpreting the human behavior.
Tenth and eleventh chapters, suggests deterministic mathematical model for modeling neural networks of voluntary single-joint movement organization in normal subjects as well as patients with Parkinson’s disease.
Twelfth chapter proposed a parametric model for optical time series data of the respiratory
neural network in the brainstem.
Thirteenth chapter give an overview of the closed-loop deep brain stimulation technology.
Fourteenth chapter present a novel approach to
build fine grain models of the human brain with a large number of neurons inspired by recent advances in computing based on DNA modecules.
The third theme includes six chapters that focus on quantitative analyses of EEG recordings to investigate the brain dynamics and neural synchronization.
Fifteenth chapter, investigate the synchronization in the neural networks based on information flow, measured by the metric of network transfer entropy, among different brain areas.
Sixteenth chapter, describe an optimization-based model for estimating all Lyapunov exponents to characterize the dynamics of EEG recordings.
Seventeenth chapter, report the potential use of nonlinear dynamics for analyzing EEG recordings to evaluate the efficacy of antiepileptic drugs.
Eighteenth chapter, study the synchronization of EEG recordings using the measures of phase synchronization and cointergrated VAR.
Nineteenth chapter, use the concept of mutual information to measure the coupling strength of EEG recordings in order to evaluate the efficacy of antiepileptic drugs in a very rare brain disease.
In the last chapter, propose a seizure monitoring and alert system to be used in an intensive care unit based on statistical analyses of EEG recordings.
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