MATLAB for Neuroscientists An Introduction to Scientific Computing in MATLAB 2nd edition by Pascal Wallisch, Michael Lusignan, Marc Benayoun, Tanya Baker, Adam Seth Dickey, Nicholas Hatsopoulos ISBN 0123838363 978-0123838360
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MATLAB for Neuroscientists An Introduction to Scientific Computing in MATLAB 2nd edition by Pascal Wallisch, Michael Lusignan, Marc Benayoun, Tanya Baker, Adam Seth Dickey, Nicholas Hatsopoulos – Ebook PDF Instant Download/Delivery: 0123838363 , 978-0123838360
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ISBN 10: 0123838363
ISBN 13: 978-0123838360
Author: Pascal Wallisch, Michael Lusignan, Marc Benayoun, Tanya Baker, Adam Seth Dickey, Nicholas Hatsopoulos
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
MATLAB for Neuroscientists An Introduction to Scientific Computing in MATLAB 2nd Table of contents:
I: Fundamentals
1 Introduction
2 MATLAB Tutorial
2.1 Goal of this Chapter
2.2 Purpose and Philosophy of MATLAB
2.2.1 Getting Started
2.2.2 MATLAB as a Calculator
2.2.3 Defining Matrices
2.2.4 Basic Matrix Algebra
2.2.5 Indexing
2.3 Graphics and Visualization
2.3.1 Basic Visualization
2.4 Function and Scripts
2.4.1 Scripts
2.4.2 Functions
2.4.3 Control Structures
2.4.4 Advanced Plotting
2.4.5 Interactive Programs
2.5 Data Analysis
2.5.1 Importing and Storing Data
2.6 A Word on Function Handles
2.7 The Function Browser
2.8 Summary
MATLAB Functions, Commands, and Operators Covered in This Chapter
3 Mathematics and Statistics Tutorial
3.1 Introduction
3.2 Linear Algebra
3.2.1 Matrices, Vectors, and Arrays
3.2.2 Transposition
3.2.3 Addition
3.2.4 Scalar Multiplication
3.2.5 Matrix Multiplication
3.2.6 Geometrical Interpretation of Matrix Multiplication
3.2.7 The Determinant
3.2.8 Eigenvalues and Eigenvectors
3.2.9 Applications of Eigenvectors: Eigendecomposition
3.2.10 Applications of Eigenvectors: PCA
3.3 Probability and Statistics
3.3.1 Introduction
3.3.2 Random Variables
3.3.2.1 Sample Estimates of Population Parameters
3.3.2.2 Joint and Conditional Probabilities
3.3.3 The Poisson Distribution
3.3.4 Normal Distribution
3.3.5 Confidence Values
3.3.6 Significance Testing
3.3.6.1 Student’s t Distribution
3.3.6.2 ANOVA Testing
3.3.7 Linear Regression
3.3.8 Introduction to Bayesian Reasoning
3.3.9 Outlook
MATLAB Functions, Commands, and Operators Covered in This Chapter
4 Programming Tutorial: Principles and Best Practices
4.1 Goals of this Chapter
4.2 Organizing Code
4.2.1 A Few Words about Maintenance
4.2.2 Variables and How to Name Them
4.2.3 Understanding Scope
4.2.4 Script or Function?
4.2.5 The Art of Commenting
4.3 Organizing More Code: Bigger Projects
4.3.1 Why Reuse Code?
4.3.2 Coupling and Cohesion
4.3.3 Separation of Concerns
4.3.4 Limiting Side Effects, or the Perils of Global State
4.3.5 Objects
4.3.5.1 Creating Objects
4.3.5.2 Inheritance
4.3.5.3 Passing Objects Around: The Handle Class
4.3.5.4 Summary
4.4 Taming Errors
4.4.1 An Introduction to the Debugger
4.4.2 Logging
4.4.3 Edge Cases and Unit Testing
4.4.4 A Few Words about Precision
4.4.5 Suggestions for Optimization
4.4.5.1 Vectorizing Matrix Operations
4.4.5.2 Conditional Expressions
4.4.5.3 Extracting Subsets from Arrays
MATLAB Functions, Commands, and Operators Covered in This 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
II: Data Collection with MATLAB
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
7 Collecting Reaction Times II: Attention
7.1 Goals of this Chapter
7.2 Background
7.2.1 So What is the Posner Paradigm?
7.3 Exercises
7.4 Project
MATLAB Functions, Commands, and Operators Covered in this 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
9 Psychophysics with GUIs
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
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
III: Data Analysis with MATLAB
11 Frequency Analysis Part I: Fourier Decomposition
11.1 Goals of this Chapter
11.2 Background
11.2.1 Real Fourier Series
11.3 Exercises
11.3.1 Complex Fourier Transform
11.3.2 Fast Fourier Transform
11.3.3 The Inverse DFT
11.3.4 Amplitude Spectrum
11.3.5 Power
11.3.6 Phase Analysis and Coherence
11.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
12 Frequency Analysis Part II: Nonstationary Signals and Spectrograms
12.1 Goal of this Chapter
12.2 Background
12.2.1 The Fourier Transform: Stationary and Ergodic
12.2.2 Windows
12.3 Exercises
12.3.1 Limitations of the STFT
12.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
13 Wavelets
13.1 Goals of This Chapter
13.2 Background
13.2.1 What is a Wavelet?
13.2.2 The Continuous Wavelet Transform
13.2.3 Choosing a Wavelet
13.2.4 Scalograms
13.2.5 The Discrete Wavelet Transform
13.2.6 Wavelet Toolbox
13.3 Exercises
13.4 Project
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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
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
16 Convolution
16.1 Goals of this Chapter
16.2 Background
16.2.1 The Visual System and Receptive Fields
16.2.2 The Mach Band Illusion
16.3 Exercises
16.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
17 Neural Data Analysis I: Encoding
17.1 Goals of this Chapter
17.2 Background
17.3 Exercises
17.3.1 Raster Plot
17.3.2 Peri-Event Time Histogram
17.3.3 Tuning Curves
17.3.4 Curve Fitting
17.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
18 Neural Data Analysis II: Binned Spike Data
18.1 Goals of this Chapter
18.2 Background
18.2.1 Exponential Function
18.2.2 Poisson Distribution
18.2.3 Log-Linear Models
18.2.4 Predicting the PETH
18.3 Exercises
18.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
19 Principal Components Analysis
19.1 Goals of this Chapter
19.2 Background
19.2.1 Covariance Matrices
19.2.2 Principal Components
19.2.3 Spike Sorting
19.3 Exercises
19.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
20 Information Theory
20.1 Goals of this Chapter
20.2 Background
20.2.1 Motor Cortical Data
20.2.2 Spike Density Functions
20.2.3 Joint, Marginal, and Conditional Distributions
20.2.4 Information Theory
20.2.5 Understanding Bias
20.2.6 Shuffle Correction
20.3 Exercises
20.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
21 Neural Decoding I: Discrete Variables
21.1 Goals of this Chapter
21.2 Background
21.2.1 Population Vector
21.2.2 Maximum Likelihood
21.2.3 Data
21.3 Exercises
21.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
22 Neural Decoding II: Continuous Variables
22.1 Goals of This Chapter
22.2 Background
22.2.1 Linear Filter
22.2.2 Maximum a Posteriori (MAP) Estimation
22.2.3 Recursive Bayesian Estimation
22.3 Exercises
22.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
23 Local Field Potentials
23.1 Goals of This Chapter
23.2 Background
23.2.1 Evoked Potentials
23.2.2 Directional tuning
23.2.3 Spectrograms
23.3 Exercises
23.4 Project
MATLAB Functions, Commands, and Operators Covered in this Chapter
24 Functional Magnetic Resonance Imaging
24.1 Goals of This Chapter
24.2 Background
24.2.1 Basic Physics of the MRI Signal
24.2.2 BOLD Signal (fMRI)
24.2.3 Preprocessing of the BOLD Signal
24.2.4 Experimental Designs
24.2.5 Analysis Methods
24.2.6 Multiple Comparisons
24.2.7 Caveats and Limitations
24.3 Exercises
24.4 Project
24.4.1 Methods Used to Collect fMRI Data
24.4.2 Group Analysis
MATLAB Functions, Commands, and Operators Covered in This Chapter
IV: Data Modeling with MATLAB
25 Voltage-Gated Ion Channels
25.1 Goal of This Chapter
25.2 Background
25.2.1 The Model
25.2.2 Kv Channel
25.2.3 The Nav Channel
25.2.4 Solving Differential Equations Numerically
25.3 Exercises
25.4 Project
Matlab Functions, Commands, and Operators Covered in This Chapter
26 Synaptic Transmission
26.1 Goals of This Chapter
26.2 Background
26.3 Exercises
26.3.1 Modeling Neurotransmitter Release
26.3.2 Modeling Random Variables
26.3.3 Modeling the Motion of a Single Molecule
26.3.4 Modeling Diffusion
26.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
27 Modeling a Single Neuron
27.1 Goal of This Chapter
27.2 Background
27.2.1 The Model
27.3 Exercises
27.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
28 Models of the Retina
28.1 Goal of This Chapter
28.2 Background
28.2.1 Neurobiological Background
28.2.2 The Model
28.2.3 Mathematical Background
28.3 Exercises
28.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
29 Simplified Model of Spiking Neurons
29.1 Goal of This Chapter
29.2 Background
29.2.1 The Model
29.3 Exercises
29.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
30 Fitzhugh-Nagumo Model: Traveling Waves
30.1 Goals of This Chapter
30.2 Background
30.3 Exercises
30.3.1 Second Derivative Operator
30.3.2 Built-in ODE Solvers
30.3.3 Fitzhugh-Nagumo Traveling Wave
30.4 Project
MATLAB Functions, Commands, and Operators Covered in This 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
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
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
33.4.1 Simulating an Event Train Using a Poisson Model
33.4.2 Picking Poisson and Exponentially Distributed Values
33.5 Picking Random Variables Without the Statistics Toolbox
33.5.1 Exponential Distributions
33.5.2 Poisson Distributions
33.6 Non-Homogeneous Poisson Processes: Time-Varying Rates of Activity
33.7 Project
MATLAB Functions, Commands, and Operators Covered in This 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
35 Neural Networks as Forest Fires: Stochastic Neurodynamics
35.1 Goals of This Chapter
35.2 Background
35.2.1 Neural Analysis
35.3 Exercises
35.4 Projects
MATLAB Functions, Commands, and Operators Covered in This Chapter
36 Neural Networks Part I: Unsupervised Learning
36.1 Goals of This Chapter
36.2 Background
36.2.1 But What is a Neural Network?
36.2.2 Unsupervised Learning and the Hebbian Learning Rule
36.2.3 Competitive Learning and Long-Term Depression
36.2.4 Neural Network Architectures: Feedforward vs. Recurrent
36.3 Exercises
36.3.1 Competitive Learning Network
36.3.2 Hopfield Network
36.3.3 The MATLAB Neural Network Toolbox
36.4 Project
MATLAB Functions, Commands, and Operators Covered in This Chapter
37 Neural Networks Part II: Supervised Learning
37.1 Goals of This Chapter
37.2 Background
37.2.1 Single-Layer Supervised Networks
37.2.2 Multilayer Supervised Networks
37.2.3 Supervised Learning in Neurobiology
37.3 Exercises
37.3.1 Perceptrons
37.3.2 Linear Networks
37.3.3 Backpropagation
37.3.4 Sound Manipulation in MATLAB
37.4 Project
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Tags: Pascal Wallisch, Michael Lusignan, Marc Benayoun, Tanya Baker, Adam Seth Dickey, Nicholas Hatsopoulos, An Introduction, Scientific Computing