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MATLAB for neuroscientists : an introduction to scientific computing in MATLAB /

This title is an introduction to MATLAB, the current standard for scientific computing, written specifically for students and researchers in neuroscience and related fields. It serves as the first comprehensive study manual and teaching resource for the use of MATLAB in the neurosciences and psychol...

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Bibliographic Details
Main Authors: Wallisch, Pascal, 1978-
Corporate Authors: Elsevier Science & Technology.
Published: Academic Press,
Publisher Address: Amsterdam :
Publication Dates: [2014]
©2014
Literature type: eBook
Language: English
Edition: Second edition.
Subjects:
MATLAB.
Neurosciences > Data processing.
Computing Methodologies.
Neurosciences.
MEDICAL > Physiology.
SCIENCE > Human Anatomy & Physiology. > Human Anatomy & Physiology.
Numerical analysis > Computer programs.
Computer science > Methodology.
Mathematical Computing.
Electronic books.
Online Access: http://www.sciencedirect.com/science/book/9780123838360
Summary: This title is an introduction to MATLAB, the current standard for scientific computing, written specifically for students and researchers in neuroscience and related fields. It serves as the first comprehensive study manual and teaching resource for the use of MATLAB in the neurosciences and psychology.
Item Description: "Elsevier science and technology books."
Carrier Form: 1 online resource (xx, 550 pages) : illustrations (some color)
Bibliography: Includes bibliographical references and index.
ISBN: 9780123838377
0123838371
Index Number: QP357
CLC: Q189
Contents: I. Fundamentals -- II. Data collection with MATLAB -- III. Data analysis with MATLAB -- IV. Data modeling with MATLAB.
MATLAB tutorial -- Mathematics and statistics tutorial -- Programming tutorial : principles and best practices -- Visualization and documentation tutorial -- Collecting reaction times I : visual search and pop out -- Collecting reaction times II : attention -- Psychophysics -- Psychophysics with GUIs -- Signal detection theory -- Frequency analysis part I : Fourier decomposition -- Frequency analysis part II : nonstationary signals and spectograms -- Wavelets -- Introduction to phase plane analysis -- Exploring the Figzhugh-Nagumo model -- Convolution -- Neural data analysis I : encoding -- Neural data analysis II : binned spike data -- Principal components analysis -- Information theory -- Neural decoding part I : discrete variables -- Neural decoding part II : continuous variables -- Local field potentials -- Functional magnetic imaging -- Voltage-gated ion channels -- Synaptic transmission -- Modeling a single neuron -- Models of the retina -- SImplified model of spiking neurons -- Fitzhugh-Nagumo model : traveling waves -- Decision theory lab -- Markov models -- Modeling spike trains as a Poisson process -- Exploring the Wilson-Cowan equations -- Neural networks as forest fires : stochastic neurodynamics -- Neural networks lab I : unsupervised learing -- Neural network lab II : supervised learning.

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