000 04561nam a22006015i 4500
001 978-3-319-71976-4
003 DE-He213
005 20220801215123.0
007 cr nn 008mamaa
008 171228s2018 sz | s |||| 0|eng d
020 _a9783319719764
_9978-3-319-71976-4
024 7 _a10.1007/978-3-319-71976-4
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC059000
_2bisacsh
072 7 _aMQW
_2thema
082 0 4 _a610.28
_223
245 1 0 _aDynamic Neuroscience
_h[electronic resource] :
_bStatistics, Modeling, and Control /
_cedited by Zhe Chen, Sridevi V. Sarma.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXXI, 327 p. 80 illus., 62 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 _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 0 _aBiomedical engineering.
_93292
650 0 _aSignal processing.
_94052
650 0 _aBioinformatics.
_99561
650 0 _aNeurosciences.
_924499
650 0 _aStatistics .
_931616
650 0 _aNeural networks (Computer science) .
_942460
650 1 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputational and Systems Biology.
_931619
650 2 4 _aNeuroscience.
_934310
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aChen, Zhe.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_942461
700 1 _aSarma, Sridevi V.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_942462
710 2 _aSpringerLink (Online service)
_942463
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319719757
776 0 8 _iPrinted edition:
_z9783319719771
776 0 8 _iPrinted edition:
_z9783030101398
856 4 0 _uhttps://doi.org/10.1007/978-3-319-71976-4
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77131
_d77131