Advanced Data Analysis in Neuroscience (Record no. 7834)
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000 -LEADER | |
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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 |
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 |
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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 |