Computational Neuroscience: From Bayesian Analysis to Neural Network Models

Catalog description

This course introduces students in neuroscience and mathematics to theoretical studies of neural systems. The course material is models of the nervous system at many different levels, including the biophysical, the circuit and the systems levels for biological sensing, motor control, perception, and learning. We will follow the classic textbook, “Theoretical neuroscience” by Dayan and Abbott. This broad introduction of topics in computational neuroscience aims to provide initial guidance for students to choose the computational approach to describe and analyze the data. The students will be encouraged to read the references and utilize the online materials before the lectures so that the students can participate in the discussion during the class. Mathematical tools in probability and differential equations and programming in Matlab will be introduced as needed within the course. Prerequisite: Undergraduates: Mathematical Tools for Life Sciences ( NEUR-SHU 100 ) or MATH-SHU 235 (Probability and Statistics), or MATH-SHU 238 (Honors Theory of Probability). Graduates: Mathematical Tools for Neural and Cognitive Science (NEURL-GA.2201). Fulfillment: Neural Science Electives.