Observed brain dynamics
The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imagi...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Oxford
Oxford University Press
2008
Oxford University Press, Incorporated |
| Edition | 1 |
| Subjects | |
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| Abstract | The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. The first part of the book contains a set of chapters which provide non-technical conceptual background to the subject. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above, and the fourth part contains special topics. |
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| AbstractList | The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. The first part of the book contains a set of chapters which provide non-technical conceptual background to the subject. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above, and the fourth part contains special topics. The biomedical sciences have undergone revolutionary change, due to the ability to digitize and store large data sets. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, this text addresses the need for a textbook in this interdisciplinary area. The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets.In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area.The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It may also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing.The first part of the book contains a set of essays meant to provide conceptual background which are not technical and shall be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics. |
| Author | Mitra, Partha P. Bokil, Hemant |
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| Keywords | brain imaging neural activity nervous system EEG optical imaging electrode arrays MEG |
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| Notes | Includes bibliographical references (p. 349-361) and index |
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| Snippet | The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data... The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets.In neuroscience, the data... The biomedical sciences have undergone revolutionary change, due to the ability to digitize and store large data sets. Written by investigators who have played... |
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| SubjectTerms | Brain Brain -- Mathematical models Brain -- Physiology Electroencephalography Mathematical models Mathematics Molecular and Cellular Systems Neural Conduction -- physiology Neural networks (Neurobiology) Neuroscientific Techniques Physiology |
| TableOfContents | 10.2 Analysis of Electroencephalographic Signals: Early Work -- 10.3 Physics of Encephalographic Signals -- 10.4 Measurement Techniques -- 10.5 Analysis -- 11 PET and fMRI -- 11.1 Introduction -- 11.2 Biophysics of PET and fMRI -- 11.3 Experimental Overview -- 11.4 Analysis -- 12 Optical Imaging -- 12.1 Introduction -- 12.2 Biophysical Considerations -- 12.3 Analysis -- PART IV: Special Topics -- 13 Local Regression and Likelihood -- 13.1 Local Regression -- 13.2 Local Likelihood -- 13.3 Density Estimation -- 13.4 Model Assessment and Selection -- 14 Entropy and Mutual Information -- 14.1 Entropy and Mutual Information for Discrete Random Variables -- 14.2 Continuous Random Variables -- 14.3 Discrete-Valued Discrete-Time Stochastic Processes -- 14.4 Continuous-Valued Discrete-Time Stochastic Processes -- 14.5 Point Processes -- 14.6 Estimation Methods -- Appendix A: The Bandwagon -- Appendix B: Two Famous Papers -- Photograph Credits -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y Intro -- Contents -- PART I: Conceptual Background -- 1 Why Study Brain Dynamics? -- 1.1 Why Dynamics? An Active Perspective -- 1.2 Quantifying Dynamics: Shared Theoretical Instruments -- 1.3 ''Newtonian and Bergsonian Time'' -- 2 Theoretical Accounts of the Nervous System -- 2.1 Three Axes in the Space of Theories -- 3 Engineering Theories and Nervous System Function -- 3.1 What Do Brains Do? -- 3.2 Engineering Theories -- 4 Methodological Considerations -- 4.1 Conceptual Clarity and Valid Reasoning -- 4.2 Nature of Scientific Method -- PART II: Tutorials -- 5 Mathematical Preliminaries -- 5.1 Scalars: Real and Complex Variables -- Elementary Functions -- 5.2 Vectors and Matrices: Linear Algebra -- 5.3 Fourier Analysis -- 5.4 Time Frequency Analysis -- 5.5 Probability Theory -- 5.6 Stochastic Processes -- 6 Statistical Protocols -- 6.1 Data Analysis Goals -- 6.2 An Example of a Protocol: Method of Least Squares -- 6.3 Classical and Modern Approaches -- 6.4 Classical Approaches: Estimation and Inference -- 7 Time Series Analysis -- 7.1 Method of Moments -- 7.2 Evoked Potentials and Peristimulus Time Histogram -- 7.3 Univariate Spectral Analysis -- 7.4 Bivariate Spectral Analysis -- 7.5 Multivariate Spectral Analysis -- 7.6 Prediction -- 7.7 Point Process Spectral Estimation -- 7.8 Higher Order Correlations -- PART III: Applications -- 8 Electrophysiology: Microelectrode Recordings -- 8.1 Introduction -- 8.2 Experimental Approaches -- 8.3 Biophysics of Neurons -- 8.4 Measurement Techniques -- 8.5 Analysis Protocol -- 8.6 Parametric Methods -- 8.7 Predicting Behavior From Neural Activity -- 9 Spike Sorting -- 9.1 Introduction -- 9.2 General Framework -- 9.3 Data Acquisition -- 9.4 Spike Detection -- 9.5 Clustering -- 9.6 Quality Metrics -- 10 Electro- and Magnetoencephalography -- 10.1 Introduction |
| Title | Observed brain dynamics |
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