000			03707nam a22003495i 4500
001			102168
003			DE-He213
005			20230102113028.0
007			cr nn 008mamaa
008			171228s2018 gw | s |||| 0|eng d
020			_a9783319719764
024	7		_a10.1007/978-3-319-71976-4 _2doi
040			_aES-MaUEC
050		4	_aR856 2018 EB
245	1	0	_aDynamic Neuroscience _bStatistics, Modeling, and Control _cedited by Zhe Chen, Sridevi V. Sarma.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXI, 328 páginas 80 ilustraciones, 71 ilustraciones a color)
347			_atext file _bPDF
505	0		_aIntroduction -- Part I Statistics & Signal Processing -- Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models -- Latent Variable Modeling of Neural Population Dynamics -- What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex -- Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems -- Artifact Rejection for Concurrent TMS-EEG Data -- Part II Modeling & Control Theory -- Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models -- Brain-Machine Interfaces -- Control-theoretic Approaches for Modeling, Analyzing and Manipulating Neuronal (In)activity -- From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach -- Neural Engine Hypothesis -- Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical Models -- Epilogue.
520	3		_aThis book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers. Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis; Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience; Covers a wide range of representative case studies in neuroscience applications.
650		7	_aIngeniería biomédica _9143820 _2embne
650		7	_aBioinformática _2embne _9160489
650		7	_aNeurociencias _2embne _9158907
700	1		_aChen, Zhe _0http://id.loc.gov/authorities/names/no2006044519 _1http://viaf.org/viaf/29939963/
700	1		_aSarma, Sridevi V. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iEdición impresa: _z9783319719757
776	0	8	_iEdición impresa: _z9783319719771
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-71976-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b01/2019 _dz _ek _feng _ggw _h0
999			_c102168 _d102168 _x1