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

Time Series Modeling of Neuroscience Data (Chapman & Hall/CRC Interdisciplinary Statistics)

Science & Math

Ozaki, Tohru

Biological Sciences

CRC-Press

While each of the previously known archives from the Third Dynasty of Ur has provided distinct views of Sumerian society, those from Iri-Sa&#285;rig present an extraordinary range of new sources, depicting a cosmopolitan Sumerian/Akkadian city unlike any other from this period. In this publication, Marcel Sigrist and Tohru Ozaki present more than two thousand newly identified tablets, mostly from Iri-Sa&#285;rig. This unique and extensive corpus elucidates the importance that Iri-Sa&#285;rig represented politically, militarily, and culturally in Sumer. Although these tablets were not able to be cleaned, baked, or photographed, the authors' transliterations are based on the original tablets, often after repeated collations. Moreover, access to so many well-preserved tablets made it possible to improve upon the readings and interpretations offered in previous publications. Volume 1 contains a catalog and classification of the texts by provenance, a list of month names and year formulas, another of inscriptions, a chronological listing of the texts, and extensive indexes of personal names, deities, toponyms, and selected words and phrases. Volume 2 presents the texts in transliteration with substantial commentary. This two-volume publication preserves and makes available to the scholarly community a significant segment of Iraq's cultural legacy that otherwise might have been ignored or even lost. It will augment and enhance our understanding of the unique civilization of Mesopotamia in the late third millennium BCE.

Tablets from the Iri-saĝrig Archive (CUSAS: Cornell University Studies in Assyriology and Sumerology)

Sigrist, Marcel

Religion & Spirituality

Eisenbrauns

Tablets From The Irisaĝrig Archive

University Park, Pennsylvania : Eisenbrauns

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