Computational Neuroscience

1 Unit · Graduate

NUIN-443, "Computational Neuroscience" is primarily targeted at first- and second-year NUIN graduate students in labs that use advanced computational tools for modeling and data analysis, and by graduate students in other programs (e.g. biomedical engineering) who are interested in computational neuroscience. It covers core topics in systems, computational and theoretical neuroscience, including but not limited to: analysis of time series (e.g., generalized linear models, dimensionality reduction, systems identification), latent variable models, manifold inference, dynamical systems modeling, learning rules, and artificial neural networks. Students enrolling in the class are expected to be familiar with python programming and basic mathematical concepts, especially linear algebra.