Computational Methods for Neurobiology & Behavior

3.0 credits · Undergraduate

This course is intended for advanced undergraduate and graduate students interested in computational methods for analyzing neural and behavioral data. Potential topics include signal processing, spectral analysis, information theory, cluster analysis, fitting parametrized models and maximum entropy models. We will also look at applications of modern machine learning techniques to scoring, analyzing and interpreting data. Although the course will be geared towards students who are new to computational methods, familiarity with calculus is recommended. Students will be asked to give a short oral presentation at the end of the semester on the use of methods from the class to analyze a real dataset.