Introduction to Computational Neuroscience

Undergraduate / Graduate

The size and complexity of neuroscience datasets has drastically increased over the past two decades, and with it the need for computational methods to analyze and model those datasets. This course will introduce students to the mathematical basis and implementation of major classes of computational neuroscience methods, as well as examples from the primary literature for how they are applied for neuroscientific discovery. We will cover topics that include but are not limited to regression and generalized linear models, dimensionality reduction, classification, and clustering. The course will include lectures, hands-on coding exercises in Python and student-led presentations of research papers.