MATLAB for Neuroscientists
An Introduction to Scientific Computing in MATLAB
- 2nd Edition - November 18, 2013
- Latest edition
- Authors: Pascal Wallisch, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 8 3 8 3 6 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 8 3 8 3 7 - 7
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neuroscie… Read more
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
Undergraduate and graduate students in systems, cognitive, and behavioral neuroscience, cognitive psychology, and related fields, as well as researchers in these fields who use MATLAB
Preface to the Second Edition
Preface to the First Edition
About the Authors
How to Use this Book
Structural and Conceptual Considerations
Layout and Style
Companion Web Site
Part I: Fundamentals
Chapter 1. Introduction
Chapter 2. MATLAB Tutorial
2.1 Goal of this Chapter
2.2 Purpose and Philosophy of MATLAB
2.3 Graphics and Visualization
2.4 Function and Scripts
2.5 Data Analysis
2.6 A Word on Function Handles
2.7 The Function Browser
2.8 Summary
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 3. Mathematics and Statistics Tutorial
3.1 Introduction
3.2 Linear Algebra
3.3 Probability and Statistics
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 4. Programming Tutorial: Principles and Best Practices
4.1 Goals of this Chapter
4.2 Organizing Code
4.3 Organizing More Code: Bigger Projects
4.4 Taming Errors
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 5. Visualization and Documentation Tutorial
5.1 Goals of This Chapter
5.2 Visualization
5.3 Documentation
MATLAB Functions, Commands, and Operators Covered in This Chapter
Part II: Data Collection with MATLAB
Chapter 6. Collecting Reaction Times I: Visual Search and Pop Out
6.1 Goals of this Chapter
6.2 Background
6.3 Exercises
6.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 7. Collecting Reaction Times II: Attention
7.1 Goals of this Chapter
7.2 Background
7.3 Exercises
7.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 8. Psychophysics
8.1 Goals of this Chapter
8.2 Background
8.3 Exercises
8.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 9. Psychophysics with GUIs
Abstract
9.1 Goals of This Chapter
9.2 Introduction and Background
9.3 GUI Basics
9.4 Using a GUI to Track an IP Address
9.5 Using a GUI for Psychophysics
9.6 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 10. Signal Detection Theory
10.1 Goals of This Chapter
10.2 Background
10.3 Exercises
10.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Part III: Data Analysis with MATLAB
Chapter 11. Frequency Analysis Part I: Fourier Decomposition
11.1 Goals of this Chapter
11.2 Background
11.3 Exercises
11.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 12. Frequency Analysis Part II: Nonstationary Signals and Spectrograms
12.1 Goal of this Chapter
12.2 Background
12.3 Exercises
12.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 13. Wavelets
13.1 Goals of This Chapter
13.2 Background
13.3 Exercises
13.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 14. Introduction to Phase Plane Analysis
14.1 Goal of this Chapter
14.2 Background
14.3 Exercises
14.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 15. Exploring the Fitzhugh-Nagumo Model
15.1 Goal of this Chapter
15.2 Background
15.3 Exercises
15.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 16. Convolution
16.1 Goals of this Chapter
16.2 Background
16.3 Exercises
16.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 17. Neural Data Analysis I: Encoding
17.1 Goals of this Chapter
17.2 Background
17.3 Exercises
17.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 18. Neural Data Analysis II: Binned Spike Data
18.1 Goals of this Chapter
18.2 Background
18.3 Exercises
18.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 19. Principal Components Analysis
19.1 Goals of this Chapter
19.2 Background
19.3 Exercises
19.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 20. Information Theory
20.1 Goals of this Chapter
20.2 Background
20.3 Exercises
20.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 21. Neural Decoding I: Discrete Variables
21.1 Goals of this Chapter
21.2 Background
21.3 Exercises
21.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 22. Neural Decoding II: Continuous Variables
22.1 Goals of This Chapter
22.2 Background
22.3 Exercises
22.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 23. Local Field Potentials
23.1 Goals of This Chapter
23.2 Background
23.3 Exercises
23.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 24. Functional Magnetic Resonance Imaging
24.1 Goals of This Chapter
24.2 Background
24.3 Exercises
24.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Part IV: Data Modeling with MATLAB
Chapter 25. Voltage-Gated Ion Channels
25.1 Goal of This Chapter
25.2 Background
25.3 Exercises
25.4 Project
Matlab Functions, Commands, and Operators Covered in This Chapter
Chapter 26. Synaptic Transmission
26.1 Goals of This Chapter
26.2 Background
26.3 Exercises
26.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 27. Modeling a Single Neuron
27.1 Goal of This Chapter
27.2 Background
27.3 Exercises
27.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 28. Models of the Retina
28.1 Goal of This Chapter
28.2 Background
28.3 Exercises
28.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 29. Simplified Model of Spiking Neurons
29.1 Goal of This Chapter
29.2 Background
29.3 Exercises
29.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 30. Fitzhugh-Nagumo Model: Traveling Waves
30.1 Goals of This Chapter
30.2 Background
30.3 Exercises
30.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 31. Decision Theory
31.1 Goals of this Chapter
31.2 Background
31.3 Simple Accumulation of Evidence
31.4 Free Response Tasks
31.5 Multiple Iterators: The Race Model
31.6 Cortical Models
31.7 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
Chapter 32. Markov Models
32.1 Goal of this Chapter
32.2 Introduction
32.3 Finding the Most Probable Path: The Viterbi Algorithm
32.4 Hidden Markov Models
32.5 Training an HMM: The Baum-Welch Algorithm
32.6 A Simple Example
32.7 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 33. Modeling Spike Trains as a Poisson Process
33.1 Goals of this Chapter
33.2 Background
33.3 The Bernoulli Process: Events in Discrete Time
33.4 The Poisson Process: Events in Continuous Time
33.5 Picking Random Variables Without the Statistics Toolbox
33.6 Non-Homogeneous Poisson Processes: Time-Varying Rates of Activity
33.7 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 34. Exploring the Wilson-Cowan Equations
34.1 Goal of This Chapter
34.2 Background
34.3 The Model
34.4 Exercises
34.5 Projects
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 35. Neural Networks as Forest Fires: Stochastic Neurodynamics
35.1 Goals of This Chapter
35.2 Background
35.3 Exercises
35.4 Projects
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 36. Neural Networks Part I: Unsupervised Learning
36.1 Goals of This Chapter
36.2 Background
36.3 Exercises
36.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
Chapter 37. Neural Networks Part II: Supervised Learning
37.1 Goals of This Chapter
37.2 Background
37.3 Exercises
37.4 Project
MATLAB Functions, Commands, and Operators covered in This Chapter
Appendix A. Creating Publication-Quality Figures
A.1 Introduction
A.2 Figure Makeovers
A.3 Saving Figures in the Desired Format
A.4 How to Make Animated GIFs
MATLAB Functions, Commands, and Operators Covered in This Appendix
Appendix B. Relevant Toolboxes
B.1 The Concept of Toolboxes
B.2 Neural Network Toolbox
B.3 Parallel Computing Toolbox
B.4 Statistics Toolbox
B.5 MATLAB Compiler
B.6 Database Toolbox
B.7 Signal Processing Toolbox
B.8 Data Acquisition Toolbox
B.9 Image Processing Toolbox
B.10 Psychophysics Toolbox and MGL
B.11 Chronux
B.12 Mathworks File Exchange
References
Index
- Edition: 2
- Latest edition
- Published: November 18, 2013
- Language: English
PW
Pascal Wallisch
Pascal Wallisch received his PhD from the University of Chicago, did postdoctoral work at the Center for Neural Science at New York University, and currently serves as a clinical assistant professor of Psychology at New York University. His research interests are at the intersection of Psychology and Neuroscience, specifically Cognitive and Computational Neuroscience. His current work focuses on motion perception, autism and the appraisal of film.
Affiliations and expertise
New York University, NY, USAML
Michael E. Lusignan
Affiliations and expertise
The University of Chicago, IL, USAMB
Marc D. Benayoun
Affiliations and expertise
The University of Chicago, IL, USATB
Tanya I. Baker
Affiliations and expertise
The Salk Institute for Biological Studies, La Jolla, CA, USAAD
Adam Seth Dickey
Affiliations and expertise
The University of Chicago, IL, USANH
Nicholas G. Hatsopoulos
Affiliations and expertise
The University of Chicago, IL, USARead MATLAB for Neuroscientists on ScienceDirect