Applies mathematical and computational methods to neuroscience. Techniques from linear algebra, differential equations, introductory dynamical systems, probability, stochastic processes, model validation, and machine learning will be learned and used. Neuroscience topics include neural spiking, network dynamics, probabilistic inference, learning, and plasticity. Will learn how the brain uses computational principles to enact decision making, vision, and memory. Recommended background includes linear algebra, differential equations, probability, and programming. Students will hone programming skills in MATLAB/Python and TensorFlow. Recommended prerequisites: APPM 2360 and APPM 3310 and STAT 4000 or equivalent courses. Same as APPM 4370.
instructor(s)
Kilpatrick, Zachary P
Primary Instructor
- Spring 2020 / Fall 2021 / Fall 2023 / Fall 2024
