9.272 Topics in Neural Signal Processing

Class Info

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.

This class has no prerequisites.

9.272 will be offered this semester (Spring 2018). It is instructed by E. N. Brown.

Lecture occurs 10:30 AM to 12:00 PM on Mondays and Wednesdays in 46-3015.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+9.272&btnG=Google+Search&inurl=https site or on the 9.272 Stellar site.

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