Computational Neuroscience

This book represents 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. This work has been designed 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. 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. We hope that this work will also be of value to investigators and practitioners in academic institutions who become involved in computational modeling as an aid in translating information in neuroscientific data to their colleagues in medical - main. This volume will be very appealing to graduate (and advanced undergraduate) students, researchers, and practitioners across a wide range of industries (e. g. , pharmaceutical, chemical, biological sciences), who require a detailed overview of the practical aspects of computational modeling in real-life neuroscience problems. For this reason, our audience is assumed to be very diverse and heterogenous, including: vii viii Preface • researchers from engineering, computer science, statistics, and mathematics - mains as well as medical and biological scientists; •physicians working in scienti?c research to understand how basic science can be linked with biological systems.

Data Mining.- Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains.- Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods.- Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles.- Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis.- Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data.- Recent Advances of Data Biclustering with Application in Computational Neuroscience.- A Genetic Classifier Account for the Regulation of Expression.- Modeling.- Neuroelectromagnetic Source Imaging of Brain Dynamics.- Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms.- Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions.- Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson’s Disease.- Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain.- Advances Toward Closed-Loop Deep Brain Stimulation.- Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization.- Brain Dynamics/Synchronization.- A Robust Estimation of Information Flow in Coupled Nonlinear Systems.- An Optimization Approach for Finding a Spectrum of Lyapunov Exponents.- Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs.- Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR.- Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht–LundborgDisease: A Pilot Study.- Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit.