LEADER 00681nam0-22002531i-450- 001 990000492980403321 005 20160108103940.0 035 $a000049298 035 $aFED01000049298 035 $a(Aleph)000049298FED01 035 $a000049298 100 $a20020821d--------km-y0itay50------ba 101 0 $aeng 105 $ay-------001yy 200 1 $aAdvances in Computers vol. 23$fYovits Marshall 210 $aN.Y.$cA.P.$d1984 700 1$aYovits,$bMarshall C. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000492980403321 952 $a10 P.T. 1/23$b130 DIS$fDINEL 959 $aDINEL 996 $aAdvances in Computers vol. 23$9332979 997 $aUNINA LEADER 05833nam 2200853 a 450 001 9910953886203321 005 20250611181739.0 010 $a0-19-988436-6 010 $a0-19-803963-8 010 $a9786611374624 010 $a1-281-37462-8 010 $a1-4356-3359-8 035 $a(CKB)2560000000296707 035 $a(EBL)415601 035 $a(OCoLC)476243589 035 $a(SSID)ssj0000088548 035 $a(PQKBManifestationID)11130632 035 $a(PQKBTitleCode)TC0000088548 035 $a(PQKBWorkID)10082426 035 $a(PQKB)11049341 035 $a(StDuBDS)EDZ0000073240 035 $a(MiAaPQ)EBC415601 035 $a(Au-PeEL)EBL415601 035 $a(CaPaEBR)ebr10215750 035 $a(CaONFJC)MIL137462 035 $a(EXLCZ)992560000000296707 100 $a20070507d2008 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aObserved brain dynamics /$fPartha P. Mitra, Hemant Bokil 205 $a1st ed. 210 $aOxford ;$aNew York $cOxford University Press$dc2008 215 $a1 online resource (xxii, 381 pages) $cillustrations 300 $aDescription based upon print version of record. 311 08$a0-19-517808-4 311 08$a0-19-986482-9 320 $aIncludes bibliographical references (p. 349-361) and index. 327 $aContents; 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 327 $aElementary Functions5.2 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 327 $a7.7 Point Process Spectral Estimation7.8 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; 10.2 Analysis of Electroencephalographic Signals: Early Work 327 $a10.3 Physics of Encephalographic Signals10.4 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 327 $a14.3 Discrete-Valued Discrete-Time Stochastic Processes14.4 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 330 $aThe 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 606 $aNeural conduction$xMathematical models 606 $aBrain$xMathematical models 606 $aBrain$xPhysiology 606 $aNeural networks (Neurobiology) 606 $aElectroencephalography 606 $aNeural Conduction$xphysiology$3(DNLM)D009431Q000502 606 $aModels, Theoretical$3(DNLM)D008962 606 $aBrain$xphysiology$3(DNLM)D001921Q000502 606 $aElectroencephalography$3(DNLM)D004569 615 0$aNeural conduction$xMathematical models. 615 0$aBrain$xMathematical models. 615 0$aBrain$xPhysiology. 615 0$aNeural networks (Neurobiology) 615 0$aElectroencephalography. 615 12$aNeural Conduction$xphysiology 615 12$aModels, Theoretical 615 22$aBrain$xphysiology 615 2$aElectroencephalography 676 $a612.8/2 700 $aMitra$b Partha$01821780 702 $aBokil$b Hemant 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910953886203321 996 $aObserved brain dynamics$94387647 997 $aUNINA