Computational Neuroscience [electronic resource] / edited by Wanpracha Chaovalitwongse, Panos M. Pardalos, Petros Xanthopoulos.

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 CreutzfeldtJakob 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. Parkinsons 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 UnverrichtLundborg Disease: A Pilot Study -- Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit.

The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain. This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience. The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience.

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