Graduate Program in Neuroscience

Neuroscience Tutorials

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.

Programming Resources

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.

Calculus

Linear Algebra

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.

Statistics

    1. Khan Academy course on statistics and probability
    2. Methods for determining the differences between two distributions

General Resources

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.