ebooks and download videos Search All  Title  Author 
Home / Nonfiction / Science / Life Sciences / Neuroscience

Statistical Techniques for Neuroscientists

| £183.27 | €206.11 | Ca$297.33 | Au$293.43
by Young K. Truong
What is this?DRM-EPUB by download  |  $219.95
What is this?DRM-PDF by download  |  $219.95
add to wish list
Statistical Techniques for Neuroscientists by Young K. Truong

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.

To view this DRM protected ebook on your desktop or laptop you will need to have Adobe Digital Editions installed. It is a free software. We also strongly recommend that you sign up for an AdobeID at the Adobe website. For more details please see FAQ 1&2. To view this ebook on an iPhone, iPad or Android mobile device you will need the Adobe Digital Editions app, or BlueFire Reader or Txtr app. These are free, too. For more details see this article.

SHARE  Share by Email  Share on Facebook  Share on Twitter  Share on Linked In  Share on Delicious
or call in the US toll free 1-888-866-9150 product ID: 875666

Ebook Details
Pages: 445
Size: 5.0 MB
Publisher: CRC Press
Date published:   2016
ISBN: 9781315356754 (DRM-EPUB)
9781466566156 (DRM-PDF)

DRM Settings
Copying:not allowed
Printing:not allowed
Read Aloud:  not allowed

Territory Restrictions
This ebook will only be sold to customers with a billing address in:

This product is listed in the following categories:

Nonfiction > Mathematics > Probability & Statistics
Nonfiction > Science > Life Sciences > Neuroscience

If you find anything wrong with this product listing, perhaps the description is wrong, the author is incorrect, or it is listed in the wrong category, then please contact us. We will promptly address your feedback.

Submit 5 page SummaryWhat is this?

© 2016