Time Series Modeling Of Neuroscience Data
DOWNLOAD
Download Time Series Modeling Of Neuroscience Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Time Series Modeling Of Neuroscience Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Time Series Modeling Of Neuroscience Data
DOWNLOAD
Author : Tohru Ozaki
language : en
Publisher: CRC Press
Release Date : 2012-01-26
Time Series Modeling Of Neuroscience Data written by Tohru Ozaki and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-26 with Mathematics categories.
Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to
Analyzing Neural Time Series Data
DOWNLOAD
Author : Mike X Cohen
language : en
Publisher: MIT Press
Release Date : 2014-01-17
Analyzing Neural Time Series Data written by Mike X Cohen and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-17 with Psychology categories.
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Methods In Brain Connectivity Inference Through Multivariate Time Series Analysis
DOWNLOAD
Author : Koichi Sameshima
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Methods In Brain Connectivity Inference Through Multivariate Time Series Analysis written by Koichi Sameshima and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Interest in brain connectivity inference has become ubiquitous and is now increasingly adopted in experimental investigations of clinical, behavioral, and experimental neurosciences. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis gathers the contributions of leading international authors who discuss different time
Advanced Data Analysis In Neuroscience
DOWNLOAD
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
Handbook Of Time Series Analysis
DOWNLOAD
Author : Björn Schelter
language : en
Publisher: John Wiley & Sons
Release Date : 2006-12-13
Handbook Of Time Series Analysis written by Björn Schelter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-13 with Science categories.
This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.
Time Series Analysis Methods And Applications
DOWNLOAD
Author : Tata Subba Rao
language : en
Publisher: Elsevier
Release Date : 2012-06-26
Time Series Analysis Methods And Applications written by Tata Subba Rao and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-26 with Mathematics categories.
'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.
Machine Learning For Time Series With Python
DOWNLOAD
Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29
Machine Learning For Time Series With Python written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.
Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is for This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.
Handbook Of Statistics
DOWNLOAD
Author :
language : en
Publisher: Elsevier
Release Date : 2012-05-18
Handbook Of Statistics written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-18 with Mathematics categories.
The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas
Statistical Methods For Modeling Human Dynamics
DOWNLOAD
Author : Sy-Miin Chow
language : en
Publisher: Routledge
Release Date : 2011-02-25
Statistical Methods For Modeling Human Dynamics written by Sy-Miin Chow and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-25 with Psychology categories.
This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA. Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of: Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience data Dynamic modeling techniques for intensive repeated measurement data Panel modeling techniques for fewer time points data State-space modeling techniques for psychological data Techniques used to analyze reaction time data. Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.
Advanced State Space Methods For Neural And Clinical Data
DOWNLOAD
Author : Zhe Chen
language : en
Publisher: Cambridge University Press
Release Date : 2015-10-15
Advanced State Space Methods For Neural And Clinical Data written by Zhe Chen 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 2015-10-15 with Computers categories.
An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.