1. Good habits for data analysis (paths, backups, versioning, annotation)
2. Visualizing neural data in MATLAB
3. Anatomy of neural data: time series, sampling, aliasing
4. Fourier series, transforms, power spectra
5. Filtering: filter design, use, caveats
6. Time-frequency analysis: spectrograms
7. Time-frequency analysis II: cross-frequency coupling
8. Interactions between multiple signals: coherence and other connectivity measures
9. Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations
10. Spike train analysis II: tuning curves, encoding, decoding
11. Spike-field relationships: spike-triggered average, phase locking, phase precession
12. Basic hypothesis testing: parametric, nonparametric, bootstrapping
13. Basic model fitting: regression, general linear models
14. Dimensionality reduction methods, classification
15. Spike sorting

• Textbook: Leis, Digital Signal Processing using MATLAB for Students and Researchers
• Textbook: Wallisch, MATLAB for Neuroscientists
• Textbook: Johnston and Wu, Foundations of Cellular Neurophysiology (for reference)
• Textbook: Dayan & Abbott, Theoretical Neuroscience (for reference)

A hands-on introduction to basic management, visualization, and analysis of vandermeerlab data (spike trains and LFPs from behaving rats) using MATLAB. We will make contact with a few concepts from computer science, signal processing, and statistics. However, the focus is on making initial steps that work and getting pointers to more complete treatment, rather than a thorough theoretical grounding.

The general format is that you work through the materials presented here on the wiki, you upload your work, I look at it, and we all discuss together at the following class meeting. So for the Week 2 meeting, work through the material under (1) above.

Class meetings are initially set for 1.30-3.00pm in PAS 2464, but this might change – changes will be announced by e-mail. There is no course website other than this one.

This course will provide a brief introduction to a number of concepts which are themselves the subject of multiple courses and thick textbooks. These include signal processing topics such as Fourier analysis and filter design, computer science concepts such as object-oriented programming and binary data formats, and a number of statistical ideas and tools. Be aware that if any of these are particularly important to your research, you should consider taking more in-depth coursework and/or working through relevant textbooks on your own.

Most weeks have you accomplish a series of tasks for which it will be obvious if you have succeeded or not. If you do only these and no more, you will receive a mark of 70%, but there are many ways to do better.

For instance, most weeks additionally will have more open-ended problems, for which a range of solutions and accompanying interpretations are possible, some of which will be better than others.

Contributions to the course wiki (elaborations, clarifications, useful resources, etc.) are also valued.

Collaboration on all aspects of the course is encouraged! As always, work you submit should reflect your own understanding; copying code is not allowed. Recall the uWaterloo guidelines on this.

The lab codebase is set up for machines running 64-bit Windows 7. I have seen everything done on a Mac, but I don't own one and cannot tell you much about how to make it work. Neuralynx has OS X versions of their MATLAB data loaders.

If you are using a lab computer, make sure you have read the General info. Note that one of the steps is to send me confirmation that you understand and agree with certain things. Do this before going any further.

Next, add your name and e-mail address to the course participants page.

Each wiki page has a Subscribe option, so you can get updates by e-mail when the page is changed; please do this, the subscription link is on the right of the page.