MATLAB for Neuroscientists, 2nd Edition

  • Published By:
  • ISBN-10: 0123838371
  • ISBN-13: 9780123838377
  • DDC: 612.80285
  • Grade Level Range: College Freshman - College Senior
  • 570 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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This is the first truly comprehensive and educational resource to study and teach Matlab in the Neurosciences and in Psychology. Used in virtually all laboratories around the world, Matlab is the standard software package for scientific computing, including stimulus generation, experimental control, data collection, data analysis and modeling.For the first time this book uses a systematic data driven and problem oriented approach to teach the use of Matlab, rather than the traditional function based style used by the Matlab manual and most other books.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication Page.
Preface to the First Edition.
Preface to the Second Edition.
About the Authors.
How to Use this Book.
1: Fundamentals.
2: Introduction.
3: MATLAB Tutorial.
4: Mathematics and Statistics Tutorial.
5: Programming Tutorial: Principles and Best Practices.
6: Visualization and Documentation Tutorial.
7: Data Collection with MATLAB.
8: Collecting Reaction Times I: Visual Search and Pop Out.
9: Collecting Reaction Times II: Attention.
10: Psychophysics.
11: Psychophysics with GUIs.
12: Signal Detection Theory.
13: Data Analysis with MATLAB.
14: Frequency Analysis Part I: Fourier Decomposition.
15: Frequency Analysis Part II: Nonstationary Signals and Spectrograms.
16: Wavelets.
17: Introduction to Phase Plane Analysis.
18: Exploring the Fitzhugh-Nagumo Model.
19: Convolution.
20: Neural Data Analysis I: Encoding.
21: Neural Data Analysis II: Binned Spike Data.
22: Principal Components Analysis.
23: Information Theory.
24: Neural Decoding I: Discrete Variables.
25: Neural Decoding II: Continuous Variables.
26: Local Field Potentials.
27: Functional Magnetic Resonance Imaging.
28: Data Modeling with MATLAB.
29: Voltage-Gated Ion Channels.
30: Synaptic Transmission.
31: Modeling a Single Neuron.
32: Models of the Retina.
33: Simplified Model of Spiking Neurons.
34: Fitzhugh-Nagumo Model: Traveling Waves.
35: Decision Theory.
36: Markov Models.
37: Modeling Spike Trains as a Poisson Process.
38: Exploring the Wilson-Cowan Equations.
39: Neural Networks as Forest Fires: Stochastic Neurodynamics.
40: Neural Networks Part I: Unsupervised Learning.
41: Neural Networks Part II: Supervised Learning.
Creating Publication-Quality Figures.
Relevant Toolboxes.