Statistical Techniques For Neuroscientists
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Statistical Techniques For Neuroscientists
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Author : Young K. Truong
language : en
Publisher: CRC Press
Release Date : 2016-10-04
Statistical Techniques For Neuroscientists written by Young K. Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-04 with Mathematics categories.
Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.
Statistical Techniques For Neuroscientists
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Author : Young K. Truong
language : en
Publisher: CRC Press
Release Date : 2016-10-04
Statistical Techniques For Neuroscientists written by Young K. Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-04 with Mathematics categories.
Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.
Advanced Data Analysis In Neuroscience
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Author : Daniel Durstewitz
language : en
Publisher: Springer
Release Date : 2017-09-15
Advanced Data Analysis In Neuroscience written by Daniel Durstewitz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-15 with Medical categories.
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
Fundamental Statistical Principles For The Neurobiologist
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Author : Stephen W. Scheff
language : en
Publisher: Academic Press
Release Date : 2016-02-11
Fundamental Statistical Principles For The Neurobiologist written by Stephen W. Scheff and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-11 with Science categories.
Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statistics book that covers fundamental principles written by a neuroscientist who understands the plight of the neuroscience graduate student and the senior investigator. It summarizes the fundamental concepts associated with statistical analysis that are useful for the neuroscientist, and provides understanding of a particular test in language that is more understandable to this specific audience, with the overall purpose of explaining which statistical technique should be used in which situation. Different types of data are discussed such as how to formulate a research hypothesis, the primary types of statistical errors and statistical power, followed by how to actually graph data and what kinds of mistakes to avoid. Chapters discuss variance, standard deviation, standard error, mean, confidence intervals, correlation, regression, parametric vs. nonparametric statistical tests, ANOVA, and post hoc analyses. Finally, there is a discussion on how to deal with data points that appear to be "outliers" and what to do when there is missing data, an issue that has not sufficiently been covered in literature. - An introductory guide to statistics aimed specifically at the neuroscience audience - Contains numerous examples with actual data that is used in the analysis - Gives the investigators a starting pointing for evaluating data in easy-to-understand language - Explains in detail many different statistical tests commonly used by neuroscientists
Data Driven Computational Neuroscience
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Author : Concha Bielza
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-26
Data Driven Computational Neuroscience written by Concha Bielza and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Computers categories.
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Analysis Of Neural Data
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Author : Robert E. Kass
language : en
Publisher:
Release Date : 2014-03-31
Analysis Of Neural Data written by Robert E. Kass and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-31 with categories.
Case Studies In Neural Data Analysis
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Author : Mark A. Kramer
language : en
Publisher: MIT Press
Release Date : 2016-11-04
Case Studies In Neural Data Analysis written by Mark A. Kramer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-04 with Science categories.
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website.
Molecular Genetic And Statistical Techniques For Behavioral And Neural Research
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Author : Robert T. Gerlai
language : en
Publisher: Academic Press
Release Date : 2018-04-24
Molecular Genetic And Statistical Techniques For Behavioral And Neural Research written by Robert T. Gerlai and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-24 with Science categories.
Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research presents the most exciting molecular and recombinant DNA techniques used in the analysis of brain function and behavior, a critical piece of the puzzle for clinicians, scientists, course instructors and advanced undergraduate and graduate students. Chapters examine neuroinformatics, genetic and neurobehavioral databases and data mining, also providing an analysis of natural genetic variation and principles and applications of forward (mutagenesis) and reverse genetics (gene targeting). In addition, the book discusses gene expression and its role in brain function and behavior, along with ethical issues in the use of animals in genetics testing. Written and edited by leading international experts, this book provides a clear presentation of the frontiers of basic research as well as translationally relevant techniques that are used by neurobehavioral geneticists. - Focuses on new techniques, including electrocorticography, functional mapping, stereo EEG, motor evoked potentials, optical coherence tomography, magnetoencephalography, laser evoked potentials, transmagnetic stimulation, and motor evoked potentials - Presents the most exciting molecular and recombinant DNA techniques used in the analysis of brain function and behavior - Written and edited by leading international experts
Guide To Research Techniques In Neuroscience
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Author : Matt Carter
language : en
Publisher: Academic Press
Release Date : 2022-03-26
Guide To Research Techniques In Neuroscience written by Matt Carter and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-26 with Medical categories.
Modern neuroscience research is inherently multidisciplinary, with a wide variety of cutting edge new techniques to explore multiple levels of investigation. This Third Edition of Guide to Research Techniques in Neuroscience provides a comprehensive overview of classical and cutting edge methods including their utility, limitations, and how data are presented in the literature. This book can be used as an introduction to neuroscience techniques for anyone new to the field or as a reference for any neuroscientist while reading papers or attending talks. - Nearly 200 updated full-color illustrations to clearly convey the theory and practice of neuroscience methods - Expands on techniques from previous editions and covers many new techniques including in vivo calcium imaging, fiber photometry, RNA-Seq, brain spheroids, CRISPR-Cas9 genome editing, and more - Clear, straightforward explanations of each technique for anyone new to the field - A broad scope of methods, from noninvasive brain imaging in human subjects, to electrophysiology in animal models, to recombinant DNA technology in test tubes, to transfection of neurons in cell culture - Detailed recommendations on where to find protocols and other resources for specific techniques - "Walk-through" boxes that guide readers through experiments step-by-step
Statistical Parametric Mapping The Analysis Of Functional Brain Images
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Author : William D. Penny
language : en
Publisher: Elsevier
Release Date : 2011-04-28
Statistical Parametric Mapping The Analysis Of Functional Brain Images written by William D. Penny and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-28 with Psychology categories.
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible