ECE 6535 - Mathematical Tools for Neural Data Analysis and Modeling

Course covers a set of mathematical and statistical methods that are fundamental for analyzing and modeling neural/cognitive data and neural signal and information processing, practiced through extensive computational exercises. Topics include linear algebra, least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, classification, linear discriminants, clustering, simple models of neural spike generation, analysis. Intended for students from quantitative backgrounds, i.e. engineering, math, statistics, computer science, physics, neuroscience, psychology.