pillow lab @ princeton

neural coding & computation group

Matlab code

1. GLM tutorial - code and slides from 2016 SFN short course on data science, illustrating basics of Gaussian and Poisson GLMs for spike train data. [zip | readme]
2. Model comparison for stepping and ramping spike train models
[relevant pub: Latimer et al, Science 2015]
3. Kalman filter estimation for continuous psychophyics / target tracking [zip | github]
[relevant pub: Bonnen et al, Journal of Vision 2015]
4. Single-neuron GLM for trial-based data
[relevant pub: IM Park et al, Nat Neurosci 2014]
5. Binary Pursuit spike sorting for detecting synchronous and overlapping spikes
[relevant pub: Pillow et al, PLoS ONE 2013]
6. Entropy estimation for binary spike trains using centered-Dirichlet mixture priors
[relevant pub: Archer et al NIPS 2013]
7. Entropy estimation under Pitman-Yor Mixture (PYM) prior, for discrete distributions with unknown support
[relevant pub: Archer et al, NIPS 2012]
8. Receptive field estimation under localized priors (ALD)
[relevant pub: M Park & Pillow, PLoS CB 2011]
9. Generalized Linear Model (GLM) point process model for spike trains
[relevant pub: Pillow et al, Nature 2008]
10. info-theoretic spike-triggered average & covariance (iSTAC)
[relevant pub: Pillow & Simoncelli, J Vision 2006]
11. Spike-Triggered Covariance (STC) analysis
[relevant pub: Schwartz et al, J Vision 2006]
11. Generalized Integrate-and-Fire model (maximum-likelihood fitting & simulations)
[relevant pub: Pillow et al, J Neurosci 2005]

Please report any bugs to pillow@princeton.edu