Analysis Of Neural Data PDF Download
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| Author | : Robert E. Kass |
| Publisher | : Springer |
| Total Pages | : 663 |
| Release | : 2014-07-08 |
| Genre | : Medical |
| ISBN | : 1461496020 |
Download Analysis of Neural Data Book in PDF, ePub and Kindle
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
| Author | : Mike X Cohen |
| Publisher | : MIT Press |
| Total Pages | : 615 |
| Release | : 2014-01-17 |
| Genre | : Psychology |
| ISBN | : 0262019876 |
Download Analyzing Neural Time Series Data Book in PDF, ePub and Kindle
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.
| Author | : Mark A. Kramer |
| Publisher | : MIT Press |
| Total Pages | : 385 |
| Release | : 2016-11-04 |
| Genre | : Science |
| ISBN | : 0262529378 |
Download Case Studies in Neural Data Analysis Book in PDF, ePub and Kindle
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.
| Author | : Erik Lee Nylen |
| Publisher | : Academic Press |
| Total Pages | : 370 |
| Release | : 2017-02-24 |
| Genre | : Science |
| ISBN | : 012804098X |
Download Neural Data Science Book in PDF, ePub and Kindle
A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. Includes discussions of both MATLAB and Python in parallel Introduces the canonical data analysis cascade, standardizing the data analysis flow Presents tactics that strategically, tactically, and algorithmically help improve the organization of code
| Author | : Concha Bielza |
| Publisher | : Cambridge University Press |
| Total Pages | : 709 |
| Release | : 2020-11-26 |
| Genre | : Computers |
| ISBN | : 110849370X |
Download Data-Driven Computational Neuroscience Book in PDF, ePub and Kindle
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
| Author | : Wulfram Gerstner |
| Publisher | : Cambridge University Press |
| Total Pages | : 591 |
| Release | : 2014-07-24 |
| Genre | : Computers |
| ISBN | : 1107060834 |
Download Neuronal Dynamics Book in PDF, ePub and Kindle
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
| Author | : Alex Fornito |
| Publisher | : Academic Press |
| Total Pages | : 494 |
| Release | : 2016-03-04 |
| Genre | : Medical |
| ISBN | : 0124081185 |
Download Fundamentals of Brain Network Analysis Book in PDF, ePub and Kindle
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
| Author | : Matt Carter |
| Publisher | : Academic Press |
| Total Pages | : 416 |
| Release | : 2022-03-26 |
| Genre | : Medical |
| ISBN | : 0323915612 |
Download Guide to Research Techniques in Neuroscience Book in PDF, ePub and Kindle
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
| Author | : Eilif Muller |
| Publisher | : Frontiers Media SA |
| Total Pages | : 275 |
| Release | : 2015-07-23 |
| Genre | : Electronic book |
| ISBN | : 2889196089 |
Download Python in Neuroscience Book in PDF, ePub and Kindle
Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.
| Author | : Daniel S. Yeung |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 89 |
| Release | : 2009-11-09 |
| Genre | : Computers |
| ISBN | : 3642025323 |
Download Sensitivity Analysis for Neural Networks Book in PDF, ePub and Kindle
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
