Computational Neuroscience

1. Front Matter

   Pages i-xvii

   PDF

2. Data Mining

   1. Front Matter

      Pages 1-1

      PDF

   2. Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains

      • António R.C. Paiva, Il Park, José C. Príncipe

      Pages 3-29

   3. Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods

      • Michael L. Anderson, Joan Brumbaugh, Aysu Şuben

      Pages 31-42

   4. Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles

      • Vangelis Sakkalis, Michalis Zervakis

      Pages 43-56

   5. 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

      Pages 57-74

   6. Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data

      • Mahdi Ramezani, Emad Fatemizadeh

      Pages 75-83

   7. Recent Advances of Data Biclustering with Application in Computational Neuroscience

      • Neng Fan, Nikita Boyko, Panos M. Pardalos

      Pages 85-112

   8. A Genetic Classifier Account for the Regulation of Expression

      • Tsvi Achler, Eyal Amir

      Pages 113-123

3. Modeling

   1. Front Matter

      Pages 125-125

      PDF

   2. Neuroelectromagnetic Source Imaging of Brain Dynamics

      • Rey R. Ramírez, David Wipf, Sylvain Baillet

      Pages 127-155

   3. Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms

      • Changxu Wu, Marc Berman, Yili Liu

      Pages 157-179

   4. Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions

      • Vassilis Cutsuridis

      Pages 181-191

   5. Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson’s Disease

      • Vassilis Cutsuridis

      Pages 193-212

   6. 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

      Pages 213-225

   7. Advances Toward Closed-Loop Deep Brain Stimulation

      • Stathis S. Leondopulos, Evangelia Micheli-Tzanakou

      Pages 227-253

   8. Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization

      • Max H. Garzon, Andrew J. Neel

      Pages 255-267

4. Brain Dynamics/Synchronization

   1. Front Matter

      Pages 269-269

      PDF

   2. A Robust Estimation of Information Flow in Coupled Nonlinear Systems

      • Shivkumar Sabesan, Konstantinos Tsakalis, Andreas Spanias, Leon Iasemidis

      Pages 271-283

   3. An Optimization Approach for Finding a Spectrum of Lyapunov Exponents

      • Panos M. Pardalos, Vitaliy A. Yatsenko, Alexandre Messo, Altannar Chinchuluun, Petros Xanthopoulos

      Pages 285-303

???????????????????????????????????????? ??????????????????. ??????????(460?. ?-360?. ?. ) Thereareinfacttwothings,scienceandopinion;theformerbegetsknowledge,the latterignorance. Hippocrates(460BC-360BC) 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 quan- tative investigation of neuroscienti?c data. To bridge the vital gap between science and medicine, this book brings together diverse research areas ranging from me- cal 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 neuroscienti?c 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. , ph- maceutical, 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.

• Master Patient Index
• biomedical engineering
• computational neuroscience
• data mining
• human brain function
• human neurophysiological systems
• modern neuroscience problems
• neuronal excitability
• synaptic transmission

From the book reviews:

“This text has a broad overlapping content concerning the biophysics, basic and clinical neurophysiology and neurosurgical approaches to computation. … I recommend this fascinating book for neurologists, neurosurgeons, physiologists, and intermediate students seeking to augment their knowledge in this burgeoning field of computation.” (Joseph J. Grenier, Amazon.com, August, 2014)

• Department of Industrial and Systems, Engineering, Rutgers State University of New Jersey, Piscataway, USA

  Wanpracha Chaovalitwongse

• Dept. Industrial & Systems, Engineering, University of Florida, Gainesville, USA

  Panos M. Pardalos

• , Dept. of Industrial & Systems Eng., University of Florida, Gainesville, USA

  Petros Xanthopoulos

Policies and ethics