LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neuroscie… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code needed.
MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as "black boxes".
Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research.
Chapter 1. Introduction
Chapter 2. MATLAB Tutorial
Chapter 3. Visual Search and Pop Out
Chapter 4. Attention
Chapter 5. Psychophysics
Chapter 6. Signal Detection Theory
Chapter 7. Frequency Analysis Part I: Fourier Decomposition
Chapter 8. Frequency Analysis Part II: Nonstationary Signals and Spectrograms
Chapter 9. Wavelets
Chapter 10. Convolution
Chapter 11. Introduction to Phase Plane Analysis
Chapter 12. Exploring the Fitzhugh-Nagumo Model
Chapter 13. Neural Data Analysis: Encoding
Chapter 14. Principal Components Analysis
Chapter 15. Information Theory
Chapter 16. Neural Decoding Part I: Discrete Variables
Chapter 17. Neural Decoding Part II: Continuous Variables
Chapter 18. Functional Magnetic Imaging
Chapter 19. Voltage-Gated Ion Channels
Chapter 20. Models of a Single Neuron
Chapter 21. Models of the Retina
Chapter 22. Simplified Model of Spiking Neurons
Chapter 23. Fitzhugh-Nagumo Model: Traveling Waves
Chapter 24. Decision Theory
Chapter 25. Markov Models
Chapter 26. Modeling Spike Trains as a Poisson Process
Chapter 27. Synaptic Transmission
Chapter 28. Neural Networks Part I: Unsupervised Learning
Chapter 29. Neural Network Part II: Supervised Learning
Appendix A. Thinking in MATLAB
Appendix B. Linear Algebra Review
Appendix C. Master Equation List
PW
ML
MB
TB
AD
NH