MATLAB for Neuroscientists book cover

MATLAB for Neuroscientists

An Introduction to Scientific Computing in MATLAB

MATLAB for Neuroscientists second edition 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. This book is unique in that it can be used to learn techniques that span the entire empirical and experimental process including stimulus generation, experimental control, data collection, data analysis, and modeling. The aim is to empower beginning users of MATLAB, by enabling them to design and implement their own analytical tools. This book will aid researchers and students in achieving the fluency required to meet the computational needs of neuroscience practitioners.

Hardbound, 570 Pages

Published: November 2013

Imprint: Academic Press

ISBN: 978-0-12-383836-0

Reviews

  • “The book is clear, cogent, and systematic. It provides much more than the essential nuts-and-bolts-it also leads the reader to learn to think about the empirical enterprise writ large…This book should be given a privileged spot on the bookshelf of every teacher, student, and researcher in the behavioral and cognitive sciences.” -Stephen M. Kosslyn, John Lindsley Professor of Psychology, Dean of Social Science, Harvard University, Cambridge, MA, USA

    “This is an excellent book that should be on the desk of any neuroscientist or psychologist who wants to analyze and understand his or her own data by using MATLAB…Several books with MATLAB toolboxes exist; I find this one special both for its clarity and its focus on problems related to neuroscience and cognitive psychology.” -Nikos Logothetis, Director, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    “MATLAB for Neuroscientists provides a unique and relatively comprehensive introduction to the MATLAB programming language in the context of brain sciences…The book would work well as a supplementary source for an introductory course in computational analysis and modeling in visual neuroscience, for graduate students or advanced undergraduates.” -Eero P. Simoncelli, Investigator, Howard Hughes Medical Institute; Professor, Neural Science, Mathematics, and Psychology, New York University, New York, USA


Contents

  • Part I: Fundamentals
    1. Introduction
    2. MATLAB Tutorial
    3. Mathematics and Statistics Tutorial
    4. Programming Tutorial - Principles and Best Practices
    5. Visualization and Documentation Tutorial
    Part II: Data Collection with MATLAB
    6. Collecting Reaction Times I: Visual Search and Pop Out
    7. Collecting Reaction Times II: Attention
    8. Psychophysics
    9. Psychophysics with GUIs
    10. Signal Detection Theory
    Part III: Data Analysis with MATLAB
    11. Frequency Analysis Part I: Fourier Decomposition
    12. Frequency Analysis Part II: Nonstationary Signals and Spectrograms
    13. Wavelets
    14. Introduction to Phase Plane Analysis
    15. Exploring the Fitzhugh-Nagumo Model
    16. Convolution
    17. Neural Data Analysis I: Encoding
    18. Neural Data Analysis II: Binned Spike Data
    19. Principal Components Analysis
    20. Information Theory
    21. Neural Decoding Part I: Discrete Variables
    22. Neural Decoding Part II: Continuous Variables
    23. Local Field Potentials
    24. Functional Magnetic Imaging
    Part IV: Data Modeling with MATLAB
    25. Voltage-Gated Ion Channels
    26. Synaptic Transmission
    27. Modeling a Single Neuron
    28. Models of the Retina
    29. Simplified Model of Spiking Neurons
    30. Fitzhugh-Nagumo Model: Traveling Waves
    31. Decision Theory Lab
    32. Markov Models
    33. Modeling Spike Trains as a Poisson Process
    34. Exploring the Wilson-Cowan Equations
    35. Neural Networks as Forest Fires: Stochastic Neurodynamics
    36. Neural Networks Lab I: Unsupervised Learning
    37. Neural Networks Lab II: Supervised Learning
    Appendix A: Creating Publication-Quality Figures
    Appendix B: Relevant Toolboxes

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