LEADER 05039nam 22006375 450 001 9910254305303321 005 20250329172558.0 010 $a9783319599762 010 $a3319599763 024 7 $a10.1007/978-3-319-59976-2 035 $a(CKB)4100000000586885 035 $a(DE-He213)978-3-319-59976-2 035 $a(MiAaPQ)EBC5579660 035 $a(PPN)204535719 035 $a(EXLCZ)994100000000586885 100 $a20170918d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Data Analysis in Neuroscience $eIntegrating Statistical and Computational Models /$fby Daniel Durstewitz 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXV, 292 p. 76 illus., 66 illus. in color.) 225 1 $aBernstein Series in Computational Neuroscience,$x2520-1603 311 08$a9783319599748 311 08$a3319599747 327 $aStatistical Inference -- Regression Problems -- Classification Problems -- Model Complexity and Selection -- Clustering and Density Estimation -- Dimensionality Reduction -- Linear Time Series Analysis -- Nonlinear Concepts in Time Series Analysis -- Time Series From a Nonlinear Dynamical Systems Perspective. 330 $aThis book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientistsas well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego ?This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. ? Bruno B. Averbeck . 410 0$aBernstein Series in Computational Neuroscience,$x2520-1603 606 $aBiometry 606 $aStatistics 606 $aNeurosciences 606 $aBiomathematics 606 $aBiostatistics 606 $aStatistical Theory and Methods 606 $aNeuroscience 606 $aMathematical and Computational Biology 615 0$aBiometry. 615 0$aStatistics. 615 0$aNeurosciences. 615 0$aBiomathematics. 615 14$aBiostatistics. 615 24$aStatistical Theory and Methods. 615 24$aNeuroscience. 615 24$aMathematical and Computational Biology. 676 $a612.8 700 $aDurstewitz$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767170 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254305303321 996 $aAdvanced Data Analysis in Neuroscience$91561726 997 $aUNINA