HNL Courses

• Statistical Computing and Modeling
• An introductory course using the mathematical programming language MATLAB to introduce first year and more advanced students to : (1) the use of MATLAB in scientific work, (2) basic methods of data analysis such as classification, dimension-reduction, (3) statistical inference (parametric and non-parametric), and (4) techniques for modeling data with an emphasis on Bayesian approaches.
• Theoretical Methods in Neuroscience I
• The first course is divided into three parts. Part one familiarizes students with some aspects of statistical mechanics. Part two covers a few concrete problems in biophysics that will introduce physical and mathematical concepts important for studying molecular signaling and biochemical networks. Part three focuses on methods useful for data analysis in many domains including independent component analysis, principal component analysis, and singular value decomposition all applied to generic problems in 'image' reconstruction.
• Theoretical Methods in Neuroscience II
• Much of the HNL's work focuses on how people learn and make decisions. Many of our paradigms center on economic decision making. The goal of this course will be to give students: 1) an introduction to the neo-classical theory of choice under uncertainty, expected utility theory, and two alternatives (Prospect Theory and Regret Theory); 2) an introduction to reinforcement learning theory; 3) an introduction to Bayesian inference.