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Features of Case Studies in Neural Data Analysis PDF
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. Case Studies in Neural Data Analysis PDF
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.
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Description of Case Studies in Neural Data Analysis PDF
For students of all the branches of medicine and surgery and health professionals that aspire to be greater and better at their procedures and medications. A renowned book by those who have read it and learnt from it. Many have already ordered it and is on the way to their home. Whether you work in the USA, Canada, UK or anywhere around the world. If you are working as a health professional then this is a must read.. The most reviewed on book Case Studies in Neural Data Analysis PDF is available for grabs now here on our website free. Whatever books, mainly textbooks we have in professional courses specially Medicine and surgery is a compendium in itself so understand one book you need to refer another 2-10 books. Beside this there are various other text material which needs to be mastered!! Only reference books are partially read but all other books have to be read, commanded and in fact read multiple times.
The Authors
Mark A. Kramer is Associate Professor in the Department of Mathematics and Statistics at Boston University.
Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University.
Dimensions and Characteristics of Case Studies in Neural Data Analysis PDF
- Identification Number : 0262529378
Publisher : The MIT Press; 1st edition (November 4, 2016)
Language : English
Paperback : 384 pages
International Standard Book Number-10 : 9780262529372
International Standard Book Number-13 : 978-0262529372
Reading age : 18 years and up
Item Weight : 1.5 pounds
Dimensions : 7.06 x 0.65 x 9 inches
Best Sellers Rank: #1,381,419 in Books
Top reviews
Lauren O.
Informative and easy to follow
December 5, 2017
Kramer and Eden’s textbook was used in Prof. Wilson Truccolo’s statistical neuroscience class at Brown University. I found it incredibly easy to follow – it provided a great introduction to MATLAB, so that anyone who wanted to analyze neural processes had a ready analysis tool, and it made complex topics much easier to understand. The sample data and code provided for free on github also helped me tremendously – I was able to walk through the data analysis with their sample data, and see for myself how the figures and results in the text were generated. Definitely recommend to anyone interested in neural data analysis and modeling!
Neurolab
Clear explanations with matlab code!
March 22, 2019
Well written with clear examples that demonstrate concepts, applications, and provide matlab code. I run a translational neuroscience lab and ask students to consult this book and Nunez first when tackling a new question in EEG or ECoG data analysis.
Alik W
but through pretty clear and well-commented code that could easily turn into …
December 24, 2016
There are two books that I regularly hand to people who are starting in my lab, or send them a chapter PDF and say “here, go read this and then let’s discuss your questions.” One is Mike Cohen’s “Analysis of Neural Time Series Data”, and the other is this book, Kramer & Eden (henceforth, “KE”). The authors are research collaborators and kindly shared a draft with us. Both books give their examples in MATLAB, but through pretty clear and well-commented code that could easily turn into numpy if desired. There are, however, two major differences that make KE a useful addition to your bookshelf even if you own Cohen.
First, there’s the “case studies” approach. KE is specifically organized around things like “here’s how you do an ERP analysis” or “here are a few ways to examine cross-frequency coupling”. It walks the novice analyst through basic data checks such as “do I have my matrices aligned right?” and “have I extracted my sampling frequency correctly?” I slightly prefer their methods of explaining things like the effect of windowing and sampling rate of the quality of a frequency-domain estimate, because it reads the way one might teach it in an undergraduate lecture. There is substantially less focus on theory and more focus on practicality. This is why I still use both books — KE will show you more of what to do, Cohen will then explain in detail why to do it.
Second, Cohen has nothing on spiking data. About half of KE is dedicated to analysis of spiking, specifically in the point-process GLM framework that Eden and his mentors/collaborators have built over much of their careers. The techniques are the same ones covered in Eden, Brown & Kass, and I think some of the examples are even the same. The difference is again that this book hand-holds you, with code, through actually performing such an analysis on spiking data. It includes much less formal proof/theory and much more practical introduction to how to fit a GLM. (This is arguably the part that would be hardest to port to python; you can do it with statmodel, but the exact commands will be quite different.)
This will not be your lab’s only neuroscience analysis book. KE does not go into the details of how to acquire good EEG or how to design a psychophysics experiment, which both Cohen and Luck (2005) cover. If you want to do anything with networks, you will want a book specifically on graph theory in neuroscience. KE does not cover wavelet analysis in detail or compare situations in which you might want to approach a time series with a bandpass/Hilbert vs. multi-taper vs. wavelet approach. No book, however, is going to be that comprehensive. KE is well organized, clearly (and sometimes humorously) written, and a great resource for teaching and learning the fundamentals. It should be on every electrophysiology lab’s common bookshelf and would also be a good text for a beginning neural engineer.
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February 22, 2022
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