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HST 576
Topics in Neural Signal Processing

Term: Spring

Course Director(s): Emery N. Brown
Time:
MW 10:30-12
Location:
46-3015
Course Website:
None
Prerequisite:
permission of instructor
Restrictions:
None
MIT Units:
3-0-9 (G-Level Credit )
Harvard Units:
N/A
Presents signal processing and statistical methods used to study neural systems and analyze neurophysiological data. Topics include state-space modeling formulated using the Bayesian Chapman-Kolmogorov system, theory of point processes, EM algorithm, Bayesian and sequential Monte Carlo methods. Applications include dynamic analyses of neural encoding, neural spike train decoding, studies of neural receptive field plasticity, algorithms for neural prosthetic control, EEG and MEG source localization. Students should know introductory probability theory and statistics.