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
Author(s): Pascal Wallisch, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey and Nicholas G. Hatsopoulos (Auth.)
Edition: 2
Publisher: Academic Press
Year: 2014
Language: English
Pages: 536
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;
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
