MATLAB for Neuroscientists second edition is an introduction to MATLAB®, the current 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 techniques that span the entire empirical and experimental process including stimulus generation, experimental control, data collection, data analysis, and modeling. The aim is to empower beginning users of MATLAB, by enabling them 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.
1. Chapter 1. Introduction - p. XX 2. Chapter 2. MATLAB Tutorial - p. 6 3. Chapter 3. Mathematics and Statistics Tutorial - p. 56 4. Chapter 4. Programming Tutorial: Principles and Best Practices - p. 102 5. Chapter 5. Visualization and Documentation Tutorial - p. 139 6. Part II: Data Collection with MATLAB - p. 150 7. Chapter 6. Collecting Reaction Times I: Visual Search and Pop Out - p. 150 8. Chapter 7. Collecting Reaction Times II: Attention - p. 164 9. Chapter 8. Psychophysics - p. 171 10. Chapter 9. Psychophysics with GUIs - p. 191 11. Chapter 10. Signal Detection Theory - p. 207 12. Part III: Data Analysis with MATLAB - p. 225 13. Chapter 11. Frequency Analysis Part I: Fourier Decomposition - p. 225 14. Chapter 12. Frequency Analysis Part II: Nonstationary Signals and Spectrograms - p. 236 15. Chapter 13. Wavelets - p. 243 16. Chapter 14. Introduction to Phase Plane Analysis - p. 252 17. Chapter 15. Exploring the Fitzhugh-Nagumo Model - p. 262 18. Chapter 16. Convolution - p. 271 19. Chapter 17. Neural Data Analysis I: Encoding - p. 285 20. Chapter 18. Neural Data Analysis II: Binned Spike Data - p. 296 21. Chapter 19. Principal Components Analysis - p. 303 22. Chapter 20. Information Theory - p. 315 23. Chapter 21. Neural Decoding I: Discrete Variables - p. 327 24. Chapter 22. Neural Decoding II: Continuous Variables - p. 336 25. Chapter 23. Local Field Potentials - p. 348 26. Chapter 24. Functional Magnetic Resonance Imaging - p. 360 27. Part IV: Data Modeling with MATLAB - p. 379 28. Chapter 25. Voltage-Gated Ion Channels - p. 379 29. Chapter 26. Synaptic Transmission - p. 393 30. Chapter 27. Modeling a Single Neuron - p. 402 31. Chapter 28. Models of the Retina - p. 410 32. Chapter 29. Simplified Model of Spiking Neurons - p. 417 33. Chapter 30. Fitzhugh-Nagumo Model: Traveling Waves - p. 424 34. Chapter 31. Decision Theory - p. 438 35. Chapter 32. Markov Models - p. 447 36. Chapter 33. Modeling Spike Trains as a Poisson Process - p. 462 37. Chapter 34. Exploring the Wilson-Cowan Equations - p. 471 38. Chapter 35. Neural Networks as Forest Fires: Stochastic Neurodynamics - p. 480 39. Chapter 36. Neural Networks Part I: Unsupervised Learning - p. 487 40. Chapter 37. Neural Networks Part II: Supervised Learning - p. 500 41. Appendix A. Creating Publication-Quality Figures - p. 517 42. Appendix B. Relevant Toolboxes - p. 526 43. References - p. 532 44. Index - p. 532
MATLAB for Neuroscientists second edition is an introduction to MATLAB®, the current 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 techniques that span the entire empirical and experimental process including stimulus generation, experimental control, data collection, data analysis, and modeling. The aim is to empower beginning users of MATLAB, by enabling them 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.
1. Chapter 1. Introduction - p. XX 2. Chapter 2. MATLAB Tutorial - p. 6 3. Chapter 3. Mathematics and Statistics Tutorial - p. 56 4. Chapter 4. Programming Tutorial: Principles and Best Practices - p. 102 5. Chapter 5. Visualization and Documentation Tutorial - p. 139 6. Part II: Data Collection with MATLAB - p. 150 7. Chapter 6. Collecting Reaction Times I: Visual Search and Pop Out - p. 150 8. Chapter 7. Collecting Reaction Times II: Attention - p. 164 9. Chapter 8. Psychophysics - p. 171 10. Chapter 9. Psychophysics with GUIs - p. 191 11. Chapter 10. Signal Detection Theory - p. 207 12. Part III: Data Analysis with MATLAB - p. 225 13. Chapter 11. Frequency Analysis Part I: Fourier Decomposition - p. 225 14. Chapter 12. Frequency Analysis Part II: Nonstationary Signals and Spectrograms - p. 236 15. Chapter 13. Wavelets - p. 243 16. Chapter 14. Introduction to Phase Plane Analysis - p. 252 17. Chapter 15. Exploring the Fitzhugh-Nagumo Model - p. 262 18. Chapter 16. Convolution - p. 271 19. Chapter 17. Neural Data Analysis I: Encoding - p. 285 20. Chapter 18. Neural Data Analysis II: Binned Spike Data - p. 296 21. Chapter 19. Principal Components Analysis - p. 303 22. Chapter 20. Information Theory - p. 315 23. Chapter 21. Neural Decoding I: Discrete Variables - p. 327 24. Chapter 22. Neural Decoding II: Continuous Variables - p. 336 25. Chapter 23. Local Field Potentials - p. 348 26. Chapter 24. Functional Magnetic Resonance Imaging - p. 360 27. Part IV: Data Modeling with MATLAB - p. 379 28. Chapter 25. Voltage-Gated Ion Channels - p. 379 29. Chapter 26. Synaptic Transmission - p. 393 30. Chapter 27. Modeling a Single Neuron - p. 402 31. Chapter 28. Models of the Retina - p. 410 32. Chapter 29. Simplified Model of Spiking Neurons - p. 417 33. Chapter 30. Fitzhugh-Nagumo Model: Traveling Waves - p. 424 34. Chapter 31. Decision Theory - p. 438 35. Chapter 32. Markov Models - p. 447 36. Chapter 33. Modeling Spike Trains as a Poisson Process - p. 462 37. Chapter 34. Exploring the Wilson-Cowan Equations - p. 471 38. Chapter 35. Neural Networks as Forest Fires: Stochastic Neurodynamics - p. 480 39. Chapter 36. Neural Networks Part I: Unsupervised Learning - p. 487 40. Chapter 37. Neural Networks Part II: Supervised Learning - p. 500 41. Appendix A. Creating Publication-Quality Figures - p. 517 42. Appendix B. Relevant Toolboxes - p. 526 43. References - p. 532 44. Index - p. 532
