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 scientific research to understand how basic science can be linked with biological systems.

1101675680
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 scientific research to understand how basic science can be linked with biological systems.

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Overview

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 scientific research to understand how basic science can be linked with biological systems.


Product Details

ISBN-13: 9780387886299
Publisher: Springer New York
Publication date: 05/12/2010
Series: Springer Optimization and Its Applications , #38
Edition description: 2010
Pages: 396
Product dimensions: 6.30(w) x 9.20(h) x 1.00(d)

Table of Contents

Part I Data Mining

1 Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains António R. C. Paiva Il Park José C. Príncipe 3

2 Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods Michael L. Anderson Joan Brumbaugh Aysu Suben 31

3 Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles Vangelis Sakkalis Michalis Zervakis 43

4 Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt-Jakob Disease for Assisting Early Diagnosis Chih-I Hung Po-Shan Wang Bing-Wen Soong Shin Teng Jen-Chuen Hsieh Yu-Te Wu 57

5 Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data Mahdi Ramezani Emad Fatemizadeh 75

6 Recent Advances of Data Biclustering with Application in Computational Neuroscience Neng Fan Nikita Boyko Panos M. Pardalos 85

7 A Genetic Classifier Account for the Regulation of Expression Tsvi Achler Eyal Amir 113

Part II Modeling

8 Neuroelectromagnetic Source Imaging of Brain Dynamics Rey R. Ramírez David Wipf Sylvain Baillet 127

9 Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms Changxu Wu Marc Berman Yili Liu 157

10 Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions Vassilis Cutsuridis 181

11 Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson's Disease Vassilis Cutsuridis 193

12 Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain Shigeharu Kawai Yositaka Oku Yasumasa Okada Fumikazu Miwakeichi Makio Ishiguro Yoshiyasu Tamura 213

13 Advances Toward Closed-Loop Deep Brain Stimulation Stathis S. Leondopulos Evangelia Micheli-Tzanakou 227

14 Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization Max H. Garzon Andrew J. Neel 255

Part III Brain Dynamics/Synchronization

15 A Robust Estimation of Information Flow in Coupled Nonlinear Systems Shivkumar Sabesan Konstantinos Tsakalis Andreas Spanias Leon Iasemidis 271

16 An Optimization Approach for Finding a Spectrum of Lyapunov Exponents Panos M. Pardalos Vitaliy A. Yatsenko Alexandre Messo Altannar Chinchuluun Petros Xanthopoulos 285

17 Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs Aaron Faith Shivkumar Sabesan Norman Wang David Treiman Joseph Sirven Konstantinos Tsakalis Leon Iasemidis 305

18 Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR Alla R. Kammerdiner Panos M. Pardalos 317

19 Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht-Lundborg Disease: A Pilot Study Chang-Chia Liu Petros Xanthopoulos Vera Tomaino Kazutaka Kobayashi Basim M. Uthman Panos M. Pardalos 341

20 Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit J. Chris Sackellares Deng-Shan Shiau Alla R. Kammerdiner Panos M. Pardalos 357

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