Statistical analysis of neural data (Stat G8285)

Spring 2011


This is a Ph.D.-level topics course in statistical analysis of neural data. Students from statistics, neuroscience, and engineering are all welcome to attend.

Time: Tu 3-5
Place: Rm 1025, 1255 Amsterdam Ave (Stat Dept building)
Professor: Liam Paninski; Office: 1255 Amsterdam Ave, Rm 1028. Email: liam at stat dot columbia dot edu. Hours by appointment.
T.A.: Alex Ramirez. Email: adr2110 at gmail dot com. Hours: 424 Pupin, M 4:10-5.

Prerequisite: A good working knowledge of basic statistical concepts (likelihood, Bayes' rule, Poisson processes, Markov chains, Gaussian random vectors), including especially linear-algebraic concepts related to regression and principal components analysis, is necessary. No previous experience with neural data is required.
Evaluation: Final grades will be based on class participation, three short problem sets, and a student project. The project can involve either the implementation and justification of a novel analysis technique, or a standard analysis applied to a novel data set. Students can work in pairs or alone (if you work in pairs, of course, the project has to be twice as impressive). See this page for some links to available datasets; or talk to other students in the class, many of whom have collected their own datasets. Some project ideas are listed on the courseworks page.
Course goals: We will introduce a number of advanced statistical techniques relevant in neuroscience. Each technique will be illustrated via application to problems in neuroscience. The focus will be on the analysis of single and multiple spike train data, with a few applications to analyzing intracellular voltage and dendritic imaging data; however, we will not cover any topics in the analysis of fMRI data (for those interested in fMRI data, see e.g. courses taught by Martin Lindquist instead). A brief list of statistical concepts and corresponding neuroscience applications is below.

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

For those new to neuroscience: While we will cover all the necessary background as we go, for those who want to explore the material in greater depth, there are a bunch of good computational neuroscience resources. Four good books (each of which emphasize different topics, albeit with some overlap) are: Theoretical Neuroscience, by Dayan and Abbott; Biophysics of Computation: information processing in single neurons, by Koch; Spiking Neuron Models, by Gerstner and Kistler; and Spikes: exploring the neural code, by Rieke et al. The first chapter of the Spikes book has been kindly made available online - this makes a nice overview of some of the questions we will address in this course. Even more kindly, the full text of the Gerstner-Kistler book is online; for our purposes, the key sections are Chapter 1 and Part I. Another good online tutorial is available here. Finally, many of our examples will be drawn from the visual system: see the book Eye, Brain, and Vision (again, very kindly provided online) by David Hubel for an excellent introduction.

For those new to statistics: See this page for some introductory notes on probability and statistics (central limit theorem, Neyman-Pearson hypothesis testing, asymptotic theory for maximum likelihood estimation, sufficient statistics, etc.). Also, here is an excellent online book on convex optimization.

Finally, Cox and Gabbiani have written a nice Matlab-based book on Mathematics for Neuroscientists, available online here if your library has access. A lot of very useful background material, along with some more advanced ideas. See also the Paninski group page here for more background and some possible project ideas.

Schedule

Date Topic Reading Notes
Jan 18, 25 Introduction; background on neuronal biophysics, regression, MCMC Spikes introduction; Kass et al '05; Brown et al. '04 Neuroscience review by Alex Ramirez
Feb 1 Linear-nonlinear Poisson cascade models: spike-triggered averaging; spike-triggered covariance; Poisson regression Simoncelli et al. '04; Chichilnisky '01; Paninski '03; Sharpee et al. '04; Paninski '04; Weisberg and Welsh '94 notes; try these practice problems, courtesy of Dayan and Abbott; any problem in chapter 1; also problems 2-3 in chapter 2.
Feb 8 Hierarchical models of color coding in the retina Field, Gauthier, Sher et al '10 Guest lecture by Kolia Sadeghi; slides
Feb 8,15 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 Uri Eden's point process notes; supplementary notes. Do inhomogenous Poisson problem set, available on courseworks; send your solution to Alex by Mar 1.
Feb 15,22; Mar 1 Generalized linear models (GLM) including spike-history effects Paninski et al. '07; Truccolo et al '05; Kass et al. '01; Ahrens et al '08; Pillow et al '08; Lewi et al '09; Mineault et al '09 notes. Do GLM problem set, available on courseworks; send your solution to Alex by Mar 22.
Mar 8 Bayesian decoding of spike trains; Markov chain Monte Carlo (MCMC) Warland et al '97; Pillow et al '11; Ahmadian et al '11a; Ahmadian et al '11b; Mischchenko and Paninski '11 Guest lectures by Yashar Ahmadian; notes
Mar 15 Spring break Presentations of project ideas are due next class...
Mar 22-29 Presentations of project ideas; the expectation-maximization (EM) algorithm for maximum likelihood given indirect measurements / hidden data; mixture models; spike sorting Neal and Hinton '98; Lewicki '98; Salakhutdinov et al '03; Shoham et al '03; Pouzat et al '04 notes
Dirichlet process mixture models for spike sorting Teh's notes on Dirichlet processes; Neal's TR on sampling methods for Dirichlet process mixture models; Wood and Black '08 slides from a guest lecture by Frank Wood (no lecture on this topic this year)
Apr 5 Hidden Markov models (HMM) in discrete space; multistate GLMs for neurons with bistable firing properties; ion channel models Rabiner tutorial; Jordan review of graphical models; Gat et al '97; Colquhoun and Hawkes '82 notes
Apr 5 Log-concave, smooth 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 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; Calabrese and Paninski '11; Paninski et al '10. Additional useful papers collected by Minka here. notes. Do state-space problem set, on courseworks; send you solutions to Alex by Apr 26.
Apr 12 Fast high-dimensional Kalman methods; smoothing problems on large dendritic trees Paninski '10; Paninski et al '11 Slides by guest lecturers: J. Huggins and E. Pnevmatikakis
Apr 19 Particle filter; stratified resampling; deconvolution of spike times from noisy calcium traces Doucet et al '00; Douc et al '05; Brockwell et al '04; Huys and Paninski '09; Vogelstein et al '09; Vogelstein et al '10. slides from a guest lecture in 2009 by Joshua Vogelstein
Apr 26 No class E-mail me if you want to discuss the project.
May 3 Project presentations Titles here. E-mail me your report as a pdf by May 10.

Thanks to the NSF for support.