Statistical Modeling and Analysis of Neural Data

This course aims to introduce students to advanced statistical and machine learning methods for analyzing of neural data, with an emphasis on methods derived from regression (supervised) and latent factor (unsupervised) models. Each technique is illustrated via applications to neural datasets. The course has a heavy emphasis on programming, and a substantial portion of the grade comes from homework assignments that involve writing code to implement relevant methods and apply them to data. The course covers methods for analyzing single and multi-neuron spike train data, calcium imaging and fMRI datasets.