MATLAB for neuroscientists : an introduction to scientific computing in MATLAB
著者
書誌事項
MATLAB for neuroscientists : an introduction to scientific computing in MATLAB
Academic Press, an imprint of Elsevier, c2014
2nd ed
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注記
Other authors: Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam S. Dickey, Nicholas G. Hatsopoulos
Includes bibliographical references (p. 533-539) and index
内容説明・目次
内容説明
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.
目次
PrefacePart I: FundamentalsIntroductionTutorialPart II: Data Collection with MatlabVisual Search and Pop OutAttentionPsychophysicsSignal Detection Theory Part III: Data Analysis with MatlabFrequency Analysis Part IFrequency Analysis Part II: Non-stationary Signals and SpectrogramsWaveletsConvolutionIntroduction to Phase Plane AnalysisExploring the Fitzhugh-Nagumo ModelNeural Data Analysis: EncodingPrincipal Components AnalysisInformation TheoryNeural Decoding: Discrete variablesNeural Decoding: Continuous variablesFunctional Magnetic ImagingPart IV: Data Modeling with MatlabVoltage-Gated Ion ChannelsModels of a Single NeuronModels of the RetinaSimplified Models of Spiking NeuronsFitzhugh-Nagumo Model: Traveling WavesDecision TheoryMarkov ModelModeling Spike Trains as a Poisson ProcessSynaptic TransmissionNeural Networks: Unsupervised learningNeural Network: Supervised LearningAppendicesAppendix 1: Thinking in MatlabAppendix 2: Linear Algebra Review
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