Advanced Data Analysis in Neuroscience (Record no. 7834)

MARC details
000 -LEADER
fixed length control field 05265nam a22003017a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240305192520.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220729b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 3319599763
International Standard Book Number 9783319599762
040 ## - CATALOGING SOURCE
Transcribing agency dlc
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5 DUR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Daniel Durstewitz
9 (RLIN) 40688
222 ## - KEY TITLE
Key title algorithm analysis assume assumptions attractor autocorrelations behavioral bifurcation Bishop bootstrap causal Chap clusters Comput converge correlations cortex covariance matrix data points defined density estimation derivatives deterministic dimensionality Duda and Hart Durstewitz dynamical systems eigenvalues equations error example firing rates fixed point fMRI function Gaussian gradient descent Granger causality graph Hastie Hence independent Independent Component Analysis inference input instance kernel Krzanowski 2000 latent likelihood likelihood function limit cycle linear model linear regression log-likelihood Lütkepohl 2006 maximization multivariate neural networks neurons neuroscience noise nonlinear dynamical normal distribution Note observations obtained optimization oscillations output parameter estimation phase Poisson potential prediction predictors prefrontal prefrontal cortex probability random variables sample Sect space models spike trains stable Strogatz temporal Tibshirani trajectories underlying values variance vector Þ¼
245 ## - TITLE STATEMENT
Title Advanced Data Analysis in Neuroscience
Remainder of title Integrating Statistical and Computational Models
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Department of Theoretical Neuroscience Central Institute of Mental Health Medical Faculty Mannheim of Heidelberg University Mannheim, Germany
Name of publisher, distributor, etc. Springer
Date of publication, distribution, etc. 2017
300 ## - PHYSICAL DESCRIPTION
Extent 292 pages
490 ## - SERIES STATEMENT
Series statement Bernstein Series in Computational Neuroscience
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Contents:<br/><br/>Statistical Inference<br/><br/>Regression Problems<br/><br/>Classification Problems<br/><br/>Model Complexity and Selection<br/><br/>Clustering and Density Estimation<br/><br/>Dimensionality Reduction<br/><br/>Linear Time Series Analysis<br/><br/>Nonlinear Concepts in Time Series Analysis<br/><br/>Time Series from a Nonlinear Dynamical Systems Perspective<br/><br/>References<br/><br/>Index
520 ## - SUMMARY, ETC.
Summary, etc. This 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 explanatory 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.<br/><br/><br/>"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 neuroscientists as 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."<br/><br/>Henry D. I. Abarbanel<br/><br/>Physics and Scripps Institution of Oceanography, University of California, San Diego<br/><br/><br/>“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. “<br/><br/>Bruno B. Averbeck
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
General subdivision Mathematics › Probability & Statistics › General
9 (RLIN) 27286
General subdivision Mathematics / Applied Mathematics / Probability & Statistics / General
9 (RLIN) 40689
General subdivision Medical / Biostatistics Medical / General
9 (RLIN) 40690
General subdivision Medical / Neuroscience
9 (RLIN) 26972
General subdivision Science / Life Sciences / General
9 (RLIN) 27324
General subdivision Science / Life Sciences / Neuroscience
9 (RLIN) 27050
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme ddc
Koha item type E-BOOKS
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Full call number Barcode Date last seen Price effective from Koha item type
            MWALIMU NYERERE LEARNING RESOURCES CENTRE-CUHAS BUGANDO MWALIMU NYERERE LEARNING RESOURCES CENTRE-CUHAS BUGANDO   07/29/2022   519.5 DUR EBS12200 07/29/2022 07/29/2022 E-BOOKS
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