MATLAB for Neuroscientists

by Wallisch; Lusignan; Benayoun; Baker; Dickey; Hatsopoulos

• ISBN13: 9780123745514
• ISBN10: 0123745519
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-10-29
• Publisher: Elsevier Science

Summary

This book is an introduction to MATLAB, the globally accepted 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 the entire empirical and experimental process including stimulus generation, experimental control, data collection, data analysis and modeling. Thus, a wide variety of computational problems can be addressed in a single programming environment. The aim is to empower users of MATLAB, whether they are beginners, advanced undergraduates or beginning graduate students, by enabling them to design and implement their own analytical tools. The book will aid researchers and students in achieving the fluency required to meet the computational needs of neuroscience practitioners.

Table of Contents

Preface	p. vii
About the Authors	p. xi
How to Use this Book	p. xiii
Fundamentals
Introduction	p. 3
MATLAB Tutorial	p. 7
Data Collection with Matlab
Visual Search and Pop Out	p. 59
Attention	p. 71
Psychophysics	p. 79
Signal Detection Theory	p. 97
Data Analysis with Matlab
Frequency Analysis Part I: Fourier Decomposition	p. 117
Frequency Analysis Part II: Nonstationary Signals and Spectrograms	p. 125
Wavelets	p. 133
Convolution	p. 141
Introduction to Phase Plane Analysis	p. 153
Exploring the Fitzhugh-Nagumo Model	p. 163
Neural Data Analysis: Encoding	p. 173
Principal Components Analysis	p. 183
Information Theory	p. 193
Neural Decoding Part I: Discrete Variables	p. 203
Neural Decoding Part II: Continuous Variables	p. 211
Functional Magnetic Imaging	p. 219
Data Modeling with Matlab
Voltage-Gated Ion Channels	p. 229
Models of a Single Neuron	p. 239
Models of the Retina	p. 247
Simplified Model of Spiking Neurons	p. 255
Fitzhugh-Nagumo Model: Traveling Waves	p. 261
Decision Theory	p. 275
Markov Models	p. 283
Modeling Spike Trains as a Poisson Process	p. 291
Synaptic Transmission	p. 299
Neural Networks Part I: Unsupervised Learning	p. 307
Neural Network Part II: Supervised Learning	p. 319
Thinking in MATLAB	p. 339
Linear Algebra Review	p. 345
Master Equation List	p. 355
References	p. 371
Index	p. 379
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