Computational Approaches for Neuroscience Questions

Catalog description

To understand the complex dynamics of the brain and mind, neuroscientists increasingly rely on interdisciplinary computational strategies. In this course, we will introduce quantitative tools for interpreting neural data (e.g., principal component analysis, regression) and techniques for modeling neural responses (e.g., biophysical simulations, network models). Students should have a basic familiarity with programming and matrix algebra, as the course will also review foundational mathematical techniques (e.g., linear algebra, differential equations) with a focus on their application to neuroscience and cognitive science. Students will work through exercises simulating neural responses, characterizing properties of visual and auditory neurons, and building neural network classifiers. Prerequisites: ( 85-110 or 85-211 or 85-170 or 85-219) and 15-112