Advanced Data Analysis in Neuroscience

Advanced Data Analysis in Neuroscience

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. "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." 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

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ISBN 3319599763
Pages 292 pages
Advanced Data Analysis in Neuroscience
Language: en
Pages: 292
Authors: Daniel Durstewitz
Categories: Medical
Type: BOOK - Published: 2017-09-15 - Publisher: Springer

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understa
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Language: en
Pages: 378
Authors: Christian Borgelt
Categories: Technology & Engineering
Type: BOOK - Published: 2012-08-29 - Publisher: Springer

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing fo
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Language: en
Pages: 662
Authors: Hernando Ombao
Categories: Mathematics
Type: BOOK - Published: 2016-11-18 - Publisher: CRC Press

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical appro
Advanced Data Analysis in Neuroscience
Language: en
Pages: 292
Authors: Daniel Durstewitz
Categories: Medical
Type: BOOK - Published: 2017-10-02 - Publisher: Springer

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understa
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Pages: 350
Authors: Henry D. I. Abarbanel
Categories: Computers
Type: BOOK - Published: 2022-01-31 - Publisher: Cambridge University Press

The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.
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Language: en
Pages:
Authors: Zhe Chen
Categories: Technology & Engineering
Type: BOOK - Published: 2015-10-15 - Publisher: Cambridge University Press

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-t
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Language: en
Pages:
Authors: João O. Malva
Categories: Science
Type: BOOK - Published: 2022-01-11 - Publisher: Frontiers Media SA

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Language: en
Pages: 362
Authors: Mayuri Mehta
Categories: Computers
Type: BOOK - Published: 2021-12-09 - Publisher: CRC Press

Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used
Advances in Cognitive Neurodynamics
Language: en
Pages: 1070
Authors: Rubin Wang
Categories: Medical
Type: BOOK - Published: 2008-09-15 - Publisher: Springer Science & Business Media

Fifty years ago, enthused by successes in creating digital computers and the DNA model of heredity, scientists were con?dent that solutions to the problems of u
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Language: en
Pages: 216
Authors: Philippe J. Giabbanelli
Categories: Technology & Engineering
Type: BOOK - Published: 2018-04-20 - Publisher: Springer

This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are exp