ELEC 483 - NEURAL SIGNAL PROCESSING
Long Title: MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING
Department: Electrical & Computer Eng.
Grade Mode: Standard Letter
Language of Instruction: Taught in English
Course Type: Lecture
Credit Hours: 3
Must be enrolled in one of the following Level(s):
Prerequisite(s): (MATH 354 OR MATH 355 OR CAAM 335 OR CMOR 302) AND (ELEC 303 OR STAT 305 OR STAT 310 OR ECON 307) AND (CAAM 210 OR CMOR 220 OR COMP 140)
Description: This course covers advanced statistical signal processing and machine learning approaches for modern neuroscience data (primarily many-channel spike trains). Topics include latent variable models, point processes, Bayesian inference, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, decoding. Graduate/Undergraduate Equivalency: ELEC 548. Recommended Prerequisite(s): ELEC 475 and STAT 413 and COMP 540 and (ELEC 242 or ELEC 243) Mutually Exclusive: Cannot register for ELEC 483 if student has credit for ELEC 548.