Analyzing Neural Time Series Data
Theory and Practice
By Mike X Cohen
"Analyzing Neural Time Series Data" is published by The MIT Press in Jan 17, 2014 - Cambridge, Mass. [u.a] and it has 600 pages.
“Analyzing Neural Time Series Data” Metadata:
- Title: ➤ Analyzing Neural Time Series Data
- Author: Mike X Cohen
- Number of Pages: 600
- Publisher: The MIT Press
- Publish Date: Jan 17, 2014
- Publish Location: Cambridge, Mass. [u.a]
“Analyzing Neural Time Series Data” Subjects and Themes:
- Subjects: ➤ Neural networks (neurobiology) - Neural networks (computer science) - Artificial intelligence - Neural networks (Neurobiology) - Neural networks (Computer science) - Computational neuroscience - Biological applications - Evoked Potentials - Nerve Net - Physiology - Wavelet Analysis - Neural circuitry - Computer Neural Networks - Réseaux neuronaux (Neurobiologie) - Réseaux neuronaux (Informatique) - Neurosciences informatiques - Intelligence artificielle - Applications biologiques - Réseaux nerveux - MEDICAL - SCIENCE - Life Sciences - Human Anatomy & Physiology
Edition Specifications:
- Format: hardcover
Edition Identifiers:
- The Open Library ID: OL27560376M - OL20349811W
- Online Computer Library Center (OCLC) ID: 849822345 - 869520413
- Library of Congress Control Number (LCCN): 2013016461
- ISBN-13: 9780262019873 - 9780262319553
- ISBN-10: 0262019876
- All ISBNs: 0262019876 - 9780262019873 - 9780262319553
AI-generated Review of “Analyzing Neural Time Series Data”:
"Analyzing Neural Time Series Data" Description:
Open Data:
"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."--
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