MATLAB for neuroscientists : an introduction to scientific computing in MATLAB

__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 many 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. This book will aid researchers and students in achieving the fluency required to meet the computational needs of neuroscience practitioners. * Completely revised and expanded second edition keeps up with developments within neuroscience as well as MATLAB * All computational approaches explained by using genuine experimental data, providing the unique look and feel of real empirical data * The first comprehensive textbook on MATLAB with a focus on its applications in neuroscience and psychology * Careful didactic approach focused on problem solving * Authors are award-winning educators with strong teaching experience * Companion website with image bank, executable code, examples, and solutions available Content: Front-matter , Pages i-iii Copyright , Page iv Preface to the Second Edition , Pages xv-xvi Preface to the First Edition , Pages xi-xiii About the Authors , Pages xvii-xviii How to Use this Book , Pages xix-xx Chapter 1 - Introduction , Pages 3-6 Chapter 2 - MATLAB Tutorial , Pages 7-56 Chapter 3 - Mathematics and Statistics Tutorial , Pages 57-102 Chapter 4 - Programming Tutorial: Principles and Best Practices , Pages 103-139 Chapter 5 - Visualization and Documentation Tutorial , Pages 141-150 Chapter 6 - Collecting Reaction Times I: Visual Search and Pop Out , Pages 153-164 Chapter 7 - Collecting Reaction Times II: Attention , Pages 165-171 Chapter 8 - Psychophysics , Pages 173-191 Chapter 9 - Psychophysics with GUIs , Pages 193-207 Chapter 10 - Signal Detection Theory , Pages 209-225 Chapter 11 - Frequency Analysis Part I: Fourier Decomposition , Pages 229-236 Chapter 12 - Frequency Analysis Part II: Nonstationary Signals and Spectrograms , Pages 237-243 Chapter 13 - Wavelets , Pages 245-252 Chapter 14 - Introduction to Phase Plane Analysis , Pages 253-262 Chapter 15 - Exploring the Fitzhugh-Nagumo Model , Pages 263-271 Chapter 16 - Convolution , Pages 273-285 Chapter 17 - Neural Data Analysis I: Encoding , Pages 287-296 Chapter 18 - Neural Data Analysis II: Binned Spike Data , Pages 297-303 Chapter 19 - Principal Components Analysis , Pages 305-315 Chapter 20 - Information Theory , Pages 317-327 Chapter 21 - Neural Decoding I: Discrete Variables , Pages 329-336 Chapter 22 - Neural Decoding II: Continuous Variables , Pages 337-348 Chapter 23 - Local Field Potentials , Pages 349-360 Chapter 24 - Functional Magnetic Resonance Imaging , Pages 361-379 Chapter 25 - Voltage-Gated Ion Channels , Pages 383-393 Chapter 26 - Synaptic Transmission , Pages 395-402 Chapter 27 - Modeling a Single Neuron , Pages 403-410 Chapter 28 - Models of the Retina , Pages 411-417 Chapter 29 - Simplified Model of Spiking Neurons , Pages 419-424 Chapter 30 - Fitzhugh-Nagumo Model: Traveling Waves , Pages 425-438 Chapter 31 - Decision Theory , Pages 439-447 Chapter 32 - Markov Models , Pages 449-462 Chapter 33 - Modeling Spike Trains as a Poisson Process , Pages 463-471 Chapter 34 - Exploring the Wilson-Cowan Equations , Pages 473-480 Chapter 35 - Neural Networks as Forest Fires: Stochastic Neurodynamics , Pages 481-487 Chapter 36 - Neural Networks Part I: Unsupervised Learning , Pages 489-500 Chapter 37 - Neural Networks Part II: Supervised Learning , Pages 501-517 Appendix A - Creating Publication-Quality Figures , Pages 519-526 Appendix B - Relevant Toolboxes , Pages 527-532 References , Pages 533-539 Index , Pages 541-550 Annotation 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 many 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. This book will aid researchers and students in achieving the fluency required to meet the computational needs of neuroscience practitioners. Completely revised and expanded second edition keeps up with developments within neuroscience as well as MATLABAll computational approaches explained by using genuine experimental data, providing the unique look and feel of real empirical dataThe first comprehensive textbook on MATLAB with a focus on its applications in neuroscience and psychologyCareful didactic approach focused on problem solving Authors are award-winning educators with strong teaching experienceCompanion website with image bank, executable code, examples, and solutions available

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.

• The first complete volume on MATLAB focusing on neuroscience and psychology applications
• Problem-based approach with many examples from neuroscience and cognitive psychology using real data
• Illustrated in full color throughout
• Careful tutorial approach, by authors who are award-winning educators with strong teaching experience