MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING

Credit Hours: 3 · Graduate

This course covers signal processing and machine learning approaches for modern neuroscience data. Topics include latent variable models, point processes, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, encoding, decoding, and electrical stimulation. Students will be introduced to public toolboxes that implement these methods. Cross-list: ELEC 483 , BIOE 548 . Cross-list: ELEC 548 .