BGGN 201Methods in Computational Neuroscience
Introduction to the computational methods most frequently used in neuroscience research. Aimed at first-year graduate students in neuroscience and related disciplines. Minimal quantitative background will be assumed. Topics include Poisson processes, Markov Chains, auto- and cross-correlation analysis, Fourier/Spectral analysis, principal components/linear algebra, signal detection theory, information theory, Bayes Theorem, hypothesis testing. Nongraduate students may enroll with consent of instructor.
Prerequisites
None listed in the catalog.