Wallisch, Pascal; Lusignan, Michael E; Benayoun, Mare D; Baker, Tanya, I; Dickey, Adam S; Hatsopoulos, Nicholas G.
MATLAB for neuroscientists : an introduction to scientific computing in MATLAB /
Pascal Wallisch [and five others].
- Second edition.
- xx, 550 pages : illustrations (some color) ; 24 cm.
includes index
Includes bibliographical references and index.
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.
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels-advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills-will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners.