Time Series Modeling of Neuroscience Data by Ozaki, Tohru

Time Series Modeling of Neuroscience Data (Hardcover) (ISBN-13: 9781420094602)

Vendor: Tohru Ozaki
Product type: Books
Format: Hardcover
$200.00
$200.00
$200.00
Subtotal: $200.00
Time Series Modeling of Neuroscience Data by Ozaki, Tohru

Time Series Modeling of Neuroscience Data

$200.00
Time Series Modeling of Neuroscience Data by Ozaki, Tohru

Time Series Modeling of Neuroscience Data

$200.00
Format: Hardcover
Description
Description
x

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.

Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include:

  • A statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more
  • Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike
  • A state space modeling method for dynamicization of solutions for the Inverse Problems
  • A heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis
  • An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series
  • An innovation-based method for spatial time series modeling for fMRI data analysis

    • The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role.

    Author: Tohru Ozaki
    ISBN-10: 1420094602
    ISBN-13: 9781420094602
    Publisher: CRC Press
    Language: English
    Published: 01/19/2012
    Pages: 574
    Format: Hardcover
    Weight: 2.00lbs
    Size: 9.30h x 6.40w x 1.30d

    Review Citation(s):
    Reference and Research Bk News 04/01/2012 pg. 229

    By Tohru Ozaki
    By Tohru Ozaki
    x

    Tohru Ozaki is a mathematician and statistician. He received his BSc in mathematics from the University of Tokyo in 1969. He then joined the Institute of Statistical Mathematics (ISM), Tokyo, in 1970 and study and worked with Hirotugu Akaike. He received his DSc from Tokyo Institute of Technology in 1981 under the supervision of Akaike. From 1987 to 2008, he was a professor at ISM and, after Akaike's retirement, served as the director of the prediction and control group. His major research areas include time series analysis, nonlinear stochastic dynamic modeling, predictive control, signal processing and their applications in neurosciences, control engineering, and financial engineering.

    While he was at ISM, Ozaki was engaged in various projects in applied time series analysis in science and engineering: EEG dynamic inverse problems, spatial time series modeling of fMRI data, causality analysis in behavioral science, modeling nonlinear dynamics in ship engineering, predictive control design in fossil power plant control, seasonal adjustment in official statistics, heteroscedastic modeling and risk-sensitive control in financial engineering, nonlinear dynamic modeling in macroeconomics, spectral analysis of seismology data, point process modeling of earthquake occurrence data, river-flow prediction in stochastic hydrology, etc.

    Ozaki retired from ISM in 2008. Since then he has been a visiting professor at Tohoku University, Sendai, Japan, and at Queensland University of Technology, Brisbane, Australia. He has been involved in supporting several research projects (in dynamic modeling of neuroscience data, fossil power plant control design, and risk-sensitive control in financial engineering) in universities and industry. He has also led, through his international research network, a time series research group called Akaike Innovation School from his office in Mount Fuji and organizes seminars every summer.

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