2 Classes (24 Units)

6.021 (12), 6.438 (12)

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6.021 Cellular Neurophysiology and Computing

Class Info

Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Preference to juniors and seniors.

This class has 8.02, 18.03, 2.005, 6.002, 6.003, 6.071, 10.301, and 20.110 as prerequisites.

6.021 will be offered this semester (Fall 2017). It is instructed by J. Han and T. Heldt.

Lecture occurs 11:00 AM to 12:30 PM on Mondays and Wednesdays in 32-124.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.021&btnG=Google+Search&inurl=https site or on the 6.021 Stellar site.

Required Textbooks
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MIT 6.021 Cellular Neurophysiology and Computing Related Textbooks
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6.438 Algorithms for Inference

Class Info

Introduction to statistical inference with probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Particle methods and filtering. Building graphical models from data, including parameter estimation and structure learning; Baum-Welch and Chow-Liu algorithms. Selected special topics.

This class has 6.008, 6.041B, 6.436, and 18.06 as prerequisites.

6.438 will be offered this semester (Fall 2017). It is instructed by G. Bresler.

Lecture occurs 9:30 AM to 11:00 AM on Tuesdays and Thursdays in 4-370.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.438&btnG=Google+Search&inurl=https site or on the 6.438 Stellar site.

MIT 6.438 Algorithms for Inference Related Textbooks

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