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Time series modeling of neuroscience...

Ozaki, Tohru, (1944-)

Time series modeling of neuroscience data /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Time series modeling of neuroscience data // Tohru Ozaki.
Author:	Ozaki, Tohru,
Published:	Boca Raton, FL :CRC Press, : 2012.,
Description:	xxv, 548 p. :ill. ; : 25 cm.;
Subject:	Nervous system - Diseases -
ISBN:	9781420094602 :

Time series modeling of neuroscience data /
Ozaki, Tohru,1944-

Time series modeling of neuroscience data /Tohru Ozaki. - Boca Raton, FL :CRC Press,2012. - xxv, 548 p. :ill. ;25 cm. - Chapman & Hall/CRC interdisciplinary statistics series. - Interdisciplinary statistics..

Includes bibliographical references (p. 519-532) and index.

"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: 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 state space modeling method for dynamicization of solutions for the Inverse Problems 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"--Provided by publisher.

ISBN: 9781420094602 :NT3423Subjects--Topical Terms:

973943
Nervous system
--Diseases

LC Class. No.: RC386.6.B7 / O93 2012

Dewey Class. No.: 616.8/0475

National Library of Medicine Call No.: WL 141

Time series modeling of neuroscience data /
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020 $a 9781420094602 : $c NT3423
020 $a 1420094602
035 $a (OCoLC)234431016
035 $a 00039975
040 $a DNLM/DLC $b eng $c DLC $d YDX $d BTCTA $d BAKER $d YDXCP $d NLM $d UKMGB $d NFU
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042 $a pcc
050 # 4 $a RC386.6.B7 $b O93 2012
060 1 0 $a WL 141
082 0 0 $a 616.8/0475 $2 23
100 1 $a Ozaki, Tohru, $d 1944- $3 973942
245 1 0 $a Time series modeling of neuroscience data / $c Tohru Ozaki.
260 # $a Boca Raton, FL : $c 2012. $b CRC Press,
300 $a xxv, 548 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC interdisciplinary statistics series
504 $a Includes bibliographical references (p. 519-532) and index.
520 # $a "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: 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 state space modeling method for dynamicization of solutions for the Inverse Problems 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"--Provided by publisher.
650 # 0 $a Nervous system $x Diseases $x Diagnosis $x Statistical methods. $3 973943
650 # 0 $a Brain mapping $x Statistical methods. $3 789151
650 # 0 $a Neurosciences $x Statistics. $3 973944
650 1 2 $a Diagnostic Techniques, Neurological $x statistics & numerical data. $3 973945
650 2 2 $a Brain Mapping $x statistics & numerical data. $3 973946
650 2 2 $a Data Interpretation, Statistical. $3 563890
650 2 2 $a Models, Neurological. $3 581379
650 2 2 $a Time Factors. $3 973947
830 0 $a Interdisciplinary statistics. $3 848082