Learning resources for neuroscience students
The goal of this page is to provide beginning neuroscience students with resources that will help them prepare for the first-year graduate course work in Neuroscience at the University of Washington.
I have created a public repository containing Matlab and Python tutorials designed to teach statistics and neuroscience concepts in the context of code that you can use in your own research. These tutorials can be accessed in a browser HERE or it can be cloned on your computer with the following terminal command:
git clone https://github.com/mikemanookin/NeuroscienceTeachingCode.git
I will continue to upload tutorials to this repository, so running ‘git pull‘ periodically will keep you up to date.
Proficiency in programming languages such as MATLAB or Python has become an essential tool for neuroscience research. Developing programming skills requires years of consistent work. We have compiled some resources to help you learn to program. The MATLAB and Python cheatsheets provide the most basic programming examples for those starting out. We also provide more advanced resources. The Allen Brain Institute has kindly provided resources for learning Python.
- Basic MATLAB Programming Cheatsheet
- Basic Python Programming Cheatsheet
- Matlab for the Behavioral Sciences: Ione Fine and Geoff Boynton have kindly made the PDF version of their book available to us free of charge. This book provides an excellent overview of basic programming techniques as well as more advanced methods in the context of data analysis.
- Introduction to Matlab Programming from Vanderbilt U.
- Advanced MATLAB Resources from MathWorks
- Allen Institute Python Resources
Linear algebra is a very important tool in neuroscience and machine learning. Below, are some resources for learning these concepts. I recommend starting with the videos from Grant Sanderson illustrating the basic concepts of linear algebra. In preparation for the neuroscience course work, gaining a solid conceptual understanding of matrix multiplication will help in understanding the more advanced topics covered in the course work such as eigendecomposition and singular value decomposition.
- Khan Academy course on statistics and probability
- Methods for determining the differences between two distributions
Adrienne Fairhall and Raj Rao here at UW have put together a very nice online introduction to computational neuroscience, which can be accessed HERE. This course is a very nice overview of computational neuroscience concepts and does not take much time to complete.