We will review at an elementary level some statistical methods useful
in the analysis of spike train data. No background needed beyond basic
Wednesday June 15th
Action potentials and spike trains. Poisson and renewal models.
Conditional intensity function. Maximum likelihood estimation. Model
validation and the time-rescaling theorem.
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.
Lecture 3: Multi-state neural systems
Hidden Markov models. Forward and backwards probabilities. The Viterbi
algorithm for decoding.
Lecture 4: Sampling
Rejection and importance sampling. Markov Chains. Metropolis-Hastings
and Gibbs sampling.