Cognitive Science 0281 - Statistics of Neural Data

The activity in our brains allows us to perform complex behaviors and (presumably) gives rise to our conscious experience. A variety of technologies exist to record neural activity at different spatial and temporal scales. However, in order to turn these recorded signals into meaningful insights about how the brain works, statistical methods are needed. In this course we will discuss several statistical analyses that are used to analyze neural data. The types of data we will examine include electro/magneto encephalographic signals (EEG/MEG), functional magnetic resonance imaging responses (fMRI) and neural spiking activity. The methods covered will range from classical univariate statistics such as ANOVAs, to multivariate machine-learning-based 'decoding' analyses. Exercises will consist of analyzing real data from these different modalities, and there will be a final project where one dataset is explored in more detail. Prerequisites: completed courses equivalent to Introduction to Statistics and Introduction to Computer Programming.

Independent Work Quantitative Skills Writing and Research In this course, students are expected to spend at least six to eight hours a week of preparation and work outside of class time. This time includes reading, writing, research.