Introduction to Computational Neuroscience

3 credits · Graduate Programs

Introduces the mathematical foundations of computational neuroscience. This includes how to model and simulate neurons (Hodgkin-Huxley or conductance-based neurons, integrate-and-fire neurons, fitting neurons to data), how to connect neurons into networks (spiking neural networks, artificial neural networks, recurrent neural networks), analytical and computational simplifications to these networks (mean-field modelling), and includes a brief introduction to techniques machine learning. This course is intended for students with a quantitative background, with a particular emphasis on dynamical systems theory, ordinary differential equations, and linear algebra. Prerequisite(s): Mathematics 213, 313, 367 and 375 or equivalent. Also known as (formerly Medical Science 755.10)