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. - The first comprehensive textbook on MATLAB with a focus for its application in neuroscience - Problem based educational approach with many examples from neuroscience and cognitive psychology using real data - Authors are award-winning educators with strong teaching experience

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.

1. Introduction 2. MATLAB Tutorial 3. Visual Search and Pop Out 4. Attention 5. Psychophysics 6. Signal Detection Theory 7. Frequency Analysis Part I: Fourier Decomposition 8. Frequency Analysis Part II: Nonstationary Signals and Spectrograms 9. Wavelets 10. Convolution 11. Introduction to Phase Plane Analysis 12. Exploring the Fitzhugh-Nagumo Model 13. Neural Data Analysis: Encoding 14. Principal Components Analysis 15. Information Theory 16. Neural Decoding Part I: Discrete Variables 17. Neural Decoding Part II: Continuous Variables 18. Functional Magnetic Imaging 19. Voltage-Gated Ion Channels 20. Models of a Single Neuron 21. Models of the Retina 22. Simplified Model of Spiking Neurons 23. Fitzhugh-Nagumo Model: Traveling Waves 24. Decision Theory 25. Markov Models 26. Modeling Spike Trains as a Poisson Process 27. Synaptic Transmission 28. Neural Networks Part I: Unsupervised Learning 29. Neural Network Part II: Supervised Learning