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E-raamat: Observed Brain Dynamics

(Member of the Technical Staff, Biological Computation & Theoretical Physics Group, Cold Spring Harbor Laboratory, USA), (, Cold Spring Harbor Laboratory, USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2007
  • Kirjastus: Oxford University Press Inc
  • Keel: eng
  • ISBN-13: 9780198039631
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2007
  • Kirjastus: Oxford University Press Inc
  • Keel: eng
  • ISBN-13: 9780198039631

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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, as well as 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 could 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 should 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.

Arvustused

"Well written and full of good illustrations and updated references. It should be readily accessible to behavioral neurologists, bioengineers, and biomathematicians."--Doody's "Well written and full of good illustrations and updated references. It should be readily accessible to behavioral neurologists, bioengineers, and biomathematicians."--Doody's

PART I Conceptual Background
1(48)
Why Study Brain Dynamics?
3(11)
Why Dynamics? An Active Perspective
3(3)
Quantifying Dynamics: Shared Theoretical Instruments
6(1)
``Newtonian and Bergsonian Time''
7(7)
Reversible and Irreversible Dynamics: Entropy
9(3)
Deterministic Versus Random Motion
12(1)
Biological Arrows of Time?
12(2)
Theoretical Accounts of the Nervous System
14(13)
Three Axes in the Space of Theories
15(12)
Level of Organization
17(5)
Direction of Causal Explanations
22(2)
Instrumental Approach
24(1)
Conclusion
25(2)
Engineering Theories and Nervous System Function
27(13)
What Do Brains Do?
27(2)
Engineering Theories
29(11)
Control Theory
31(1)
Communication Theory
32(4)
Computation
36(4)
Methodological Considerations
40(9)
Conceptual Clarity and Valid Reasoning
41(1)
Syntax: Well-Formed Statements
41(1)
Logic: Consequence
42(1)
Nature of Scientific Method
42(7)
Empirical and Controlled Experimental Methods
43(1)
Deductive and Inductive Methods
44(2)
Causation and Correlation
46(3)
PART II Tutorials
49(168)
Mathematical Preliminaries
51(97)
Scalars: Real and Complex Variables: Elementary Functions
52(4)
Exponential Functions
54(2)
Miscellaneous Remarks
56(1)
Vectors and Matrices: Linear Algebra
56(16)
Vectors as Points in a High-Dimensional Space
57(1)
Angles, Distances, and Volumes
58(3)
Linear Independence and Basis Sets
61(1)
Subspaces and Projections
62(1)
Matrices: Linear Transformations of Vectors
63(1)
Some Classes of Matrices
64(2)
Functions of Matrices: Determinants, Traces, and Exponentials
66(1)
Classical Matrix Factorization Techniques
67(3)
Pseudospectra
70(2)
Fourier Analysis
72(17)
Function Spaces and Basis Expansions
74(3)
Fourier Series
77(4)
Convergence of Fourier Expansions on the Interval
81(2)
Fourier Transforms
83(1)
Bandlimited Functions, the Sampling Theorem, and Aliasing
84(2)
Discrete Fourier Transforms and Fast Fourier Transforms
86(3)
Time Frequency Analysis
89(9)
Broadband Bias and Narrowband Bias
90(4)
The Spectral Concentration Problem
94(4)
Probability Theory
98(15)
Sample Space, Events, and Probability Axioms
100(2)
Random Variables and Characteristic Function
102(3)
Some Common Probability Measures
105(6)
Law of Large Numbers
111(1)
Central Limit Theorems
112(1)
Stochastic Processes
113(35)
Defining Stochastic Processes
114(2)
Time Translational Invariance
116(1)
Ergodicity
117(1)
Time Translation Invariance and Spectral Analysis
118(1)
Gaussian Processes
118(5)
Non-Gaussian Processes
123(1)
Point Processes
124(24)
Statistical Protocols
148(36)
Data Analysis Goals
149(1)
An Example of a Protocol: Method of Least Squares
150(1)
Classical and Modern Approaches
151(2)
Data Visualization
152(1)
Classical Approaches: Estimation and Inference
153(31)
Point Estimation
154(7)
Method of Least Squares: The Linear Model
161(6)
Generalized Linear Models
167(4)
Interval Estimation
171(1)
Hypothesis Testing
172(6)
Nonparametric Tests
178(3)
Bayesian Estimation and Inference
181(3)
Time Series Analysis
184(33)
Method of Moments
185(2)
Evoked Potentials and Peristimulus Time Histogram
187(2)
Univariate Spectral Analysis
189(18)
Periodogram Estimate: Problems of Bias and Variance
190(1)
Nonparametric Quadratic Estimates
191(6)
Autoregressive Parametric Estimates
197(3)
Harmonic Analysis and Mixed Spectral Estimation
200(2)
Dynamic Spectra
202(5)
Bivariate Spectral Analysis
207(2)
Cross-Coherence
208(1)
Multivariate Spectral Analysis
209(2)
Singular Value Decomposition of Cross-Spectral Matrix
209(2)
Prediction
211(2)
Linear Prediction Using Autoregressive Models
212(1)
Point Process Spectral Estimation
213(2)
Degrees of Freedom
214(1)
Hybrid Multivariate Processes
214(1)
Higher Order Correlations
215(2)
Correlations Between Spectral Power at Different Frequencies
216(1)
PART III Applications
217(104)
Electrophysiology: Microelectrode Recordings
219(38)
Introduction
219(1)
Experimental Approaches
220(1)
Biophysics of Neurons
221(3)
Transmembrane Resting Potential
221(1)
Action Potentials and Synaptic Potentials
221(2)
Extracellular Potentials
223(1)
Measurement Techniques
224(1)
Intracellular Measurements
224(1)
Extracellular Measurements
224(1)
Noise Sources
225(1)
Analysis Protocol
225(23)
Data Conditioning
225(3)
Analysis of Spike Trains
228(11)
Local Field Potentials
239(3)
Measures of Association
242(4)
Periodic Stimulation
246(2)
Parametric Methods
248(3)
Goodness of Fit
250(1)
Example
250(1)
Predicting Behavior From Neural Activity
251(6)
Selecting Feature Vectors
253(1)
Discrete Categories
254(1)
Continuous Movements
255(2)
Spike Sorting
257(14)
Introduction
257(1)
General Framework
258(1)
Manual Sorting
258(1)
Data Acquisition
259(3)
Multiple Electrodes
259(1)
Sampling
259(1)
Data Windows
260(2)
Spike Detection
262(4)
Alignment
263(2)
Outlier Removal
265(1)
Data Visualization
265(1)
Clustering
266(2)
Quality Metrics
268(3)
Manual Review
268(3)
Electro- and Magnetoencephalography
271(23)
Introduction
271(1)
Analysis of Electroencephalographic Signals: Early Work
271(2)
Physics of Encephalographic Signals
273(1)
Measurement Techniques
273(2)
Noise
275(1)
Analysis
275(19)
Denoising and Dimensionality Reduction
275(8)
Confirmatory Analysis
283(11)
PET and fMRI
294(19)
Introduction
294(1)
Biophysics of PET and fMRI
295(2)
PET
295(1)
fMRI
295(1)
Noise Sources
296(1)
Experimental Overview
297(2)
Experimental Protocols
298(1)
Analysis
299(14)
Data Conditioning
299(6)
Harmonic Analysis
305(1)
Statistical Parametric Mapping
306(4)
Multiple Hypothesis Tests
310(1)
Anatomical Considerations
311(2)
Optical Imaging
313(8)
Introduction
313(1)
Biophysical Considerations
313(1)
Noise Sources
314(1)
Analysis
314(7)
Difference and Ratio Maps
315(1)
Multivariate Methods
315(6)
PART IV Special Topics
321(22)
Local Regression and Likelihood
323(10)
Local Regression
323(3)
Local Likelihood
326(2)
Local Logistic Regression
327(1)
Local Poisson Regression
327(1)
Density Estimation
328(1)
Model Assessment and Selection
328(5)
Degrees of Freedom
328(1)
Selection of the Bandwidth and Polynomial Degree
329(2)
Residuals
331(1)
Confidence Intervals
332(1)
Entropy and Mutual Information
333(10)
Entropy and Mutual Information for Discrete Random Variables
334(2)
Continuous Random Variables
336(1)
Discrete-Valued Discrete-Time Stochastic Processes
337(1)
Continuous-Valued Discrete-Time Stochastic Processes
338(1)
Point Processes
339(1)
Estimation Methods
340(3)
Appendix A: The Bandwagon 343(2)
C. E. Shannon
Appendix B: Two Famous Papers 345(2)
Peter Elias
Photograph Credits 347(2)
Bibliography 349(14)
Index 363