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Ari Pakman – Tutorial on Machine Learning Methods for Neuroscience

by Ari Pakman (Brown University)
 

We will review at an elementary level some statistical methods useful
in the analysis of spike train data. No background needed beyond basic
probability.

Wednesday June 15th

11:30-1:00, Simons Center for Geometry and Physics, Room 313
Lecture 1: The neural encoding problem
Action potentials and spike trains. Poisson and renewal models.
Conditional intensity function. Maximum likelihood estimation. Model
validation and the time-rescaling theorem. 

Thursday June 16th

11:30-1:00, Simons Center for Geometry and Physics, Room 313

Lecture 2: Unobserved neural processes
Models with hidden variables. Probabilistic classification. The
Expectation-Maximization algorithm. Probabilistic graphical models.

Wednesday June 22nd
11:30-1:00, Simons Center for Geometry and Physics, Room 313

Lecture 3: Multi-state neural systems
Hidden Markov models. Forward and backwards probabilities. The Viterbi
algorithm for decoding.

Thursday June 23rd
11:30-1:00, Simons Center for Geometry and Physics, Room 313

Lecture 4: Sampling
Rejection and importance sampling. Markov Chains. Metropolis-Hastings
and Gibbs sampling.