| Statistical concept / technique | Neuroscience application |
|---|---|
| Point processes; conditional intensity functions | Neural spike trains; photon-limited image data |
| Time-rescaling theorem for point processes | Fast simulation of network models; goodness-of-fit tests for spiking models |
| Bias, consistency, principal components | Spike-triggered averaging; spike-triggered covariance |
| Generalized linear models | Neural encoding models including spike-history effects; inferring network connectivity |
| Regularization; shrinkage estimation | Maximum a posteriori estimation of high-dimensional neural encoding models |
| Laplace approximation; Fisher information | Model-based decoding and information estimation; adaptive design of optimal stimuli |
| Mixture models; EM algorithm; Dirichlet processes | Spike-sorting / clustering |
| Optimization and convexity techniques | Spike-train decoding; ML estimation of encoding models |
| Markov chain Monte Carlo: Metropolis-Hastings and hit-and-run algorithms | Firing rate estimation and spike-train decoding |
| State-space models; sequential Monte Carlo / particle filtering | Decoding spike trains; optimal voltage smoothing |
| Fast high-dimensional Kalman filtering | Optimal smoothing of voltage and calcium signals on large dendritic trees |
| Markov processes; first-passage times; Fokker-Planck equation | Integrate-and-fire-based neural models |
| Date | Topic | Reading | Notes |
|---|---|---|---|
| Sep 8,15 | Introduction; background on neuronal biophysics, regression, MCMC | Spikes introduction; Kass et al '05; Brown et al. '04 | Neuroscience review by Ella Batty. Regression notes |
| Sep 22 | Signal acquisition: spike sorting, calcium imaging | Lewicki '98; Shoham et al '03; Pouzat et al '04, Pillow et al `13, Carlson et al '13, Pnevmatikakis et al '14 | EM notes |
| Sept 29 | No class | ||
| Oct 6 | Estimating time-varying firing rates | Kass et al (2003), Wallstrom et al (2008) | Generalized linear model notes |
| Oct 13 | Linear-nonlinear Poisson cascade models: spike-triggered averaging; Poisson regression | Simoncelli et al. '04; Chichilnisky '01; Paninski '03; Sharpee et al. '04; Paninski '04; Weisberg and Welsh '94; Williamson et al '13 | Try these practice problems, courtesy of Dayan and Abbott; any problem in chapter 1; also problems 2-3 in chapter 2. |
| Oct 20 | Expected log-likelihood; quadratic models; spike-triggered covariance; sparsity-promoting and rank-penalizing priors; hierarchical models. Experimental design. | Park and Pillow '11, Ramirez and Paninski, '13, Field, Gauthier, Sher et al '10, Ahrens et al '08, Lewi et al '09, Shababo et al '13, Soudry et al '15 | |
| Oct 27 | Presentations of project ideas | Just two minutes each | |
| Nov 3 | No class (University holiday) | ||
| Nov 10 | Point processes: Poisson process, renewal process, self-exciting process, Cox process; time-rescaling: goodness-of-fit, fast simulation of network models | Brown et al. '01, Mena and Paninski '14 | Uri Eden's point process notes; supplementary notes. |
| Nov 17 - Dec 1 | State space models; autoregressive models; Kalman filter; extended Kalman filter; fast tridiagonal methods. Applications in neural prosthetics, optimal smoothing of voltage/calcium traces, fitting common-input models for population spike train data, and analysis of nonstationary spike train data | HMM tutorial by Rabiner; Kalman filter notes by Minka; Roweis and Ghahramani '99; Huys et al '06; Paninski et al '04; Jolivet et al '04; Beeman's notes on conductance-based neural modeling; Wu et al '05; Brown et al '98; Smith et al '04; Yu et al '05; Kulkarni and Paninski '08; Paninski et al '10, Calabrese and Paninski '11; Vogelstein et al '10, Buesing et al '12, Vidne et al '12, Pfau et al '13. | state-space notes (need updating) |
| Dec 8 | No class (office hours) | Stop by if you want to discuss your project. | |
| Dec 15 | Project presentations | E-mail me your report as a pdf by Dec 18. |