Research related source codes

neuroGLM

This MATLAB library for temporal GLM regression.

• Il Memming Park, Miriam L. R. Meister, Alexander C. Huk, Jonathan W. Pillow. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience 17, 1395-1403, 2014

CDMentropy

This MATLAB library for estimating discrete entropy for binary data such as binned spike trains in the under-sampled regime.
Publications that use the toolbox:

• Evan Archer, Il Memming Park, Jonathan W. Pillow. Bayesian entropy estimation for binary spike train data using parametric prior knowledge. Neural Information Processing Systems (NIPS) 2013

PYMentropy

This MATLAB library is for estimating discrete entropy in the under-sampled regime.
Publications that use the toolbox:

• Evan Archer, Il Memming Park, Jonathan W. Pillow. Bayesian estimation of discrete entropy with mixtures of stick breaking priors. Neural Information Processing Systems (NIPS) 2012
• Evan Archer, Il Memming Park, Jonathan W. Pillow. Bayesian Entropy Estimation for Countable Discrete Distributions. (submitted, arXiv)

spiketrainlib: spike train kernels, divergences, distances, regression, and more

This MATLAB library is developed around the core of positive definite and strictly positive definite spike train kernels.
Publications that use the toolbox:

• Il Memming Park, Sohan Seth, António R. C. Paiva, Lin Li, José C. Príncipe. Kernel methods on spike train space for neuroscience: a tutorial. IEEE Signal Processing Magazine (in press, July 2013) [arXiv:1302.5964]
• Il Memming Park, Sohan Seth, Murali Rao, José C. Príncipe. Strictly Positive Definite Spike Train Kernels for Point Process Divergences. Neural Computation Volume 24, Issue 8, August 2012
• Il Memming Park, Sohan Seth, José C. Príncipe. Spike Train Kernel Methods for Neuroscience. JSM 2011 (invited speaker)
• Il Park. Capturing spike train similarity structure: A point process divergence approach. Ph.D. dissertation (2010)

iocane: point process divergence measures

This MATLAB toolbox is not kernel based, and mostly focused on computing nonparameteric statistics between sets of spike trains.
Publications that use the toolbox:

• Il Park, Sohan Seth, Murali Rao, José C. Príncipe. Estimation of symmetric chi-square divergence for point processes. IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP) 2011
• Sohan Seth, Il Park, Austin J. Brockmeier, Mulugeta Semework, John Choi, Joe Francis, José C. Príncipe. A Novel Family of Non-Parametric Cumulative based Divergences for Point Processes. Neural Information Processing Systems (NIPS) 2010
• Il Park, José C. Príncipe. Quantification of Inter-trial Non-stationarity in Spike Trains from Periodically Stimulated Neural Cultures, IEEE International Conference on Acoustics, Speech and Signal Processing 2010, Special session on “Multivariate Analysis of Brain Signals: Methods and Applications” (accepted #3459, 48.8% acceptance rate)
• Il Park, José C. Príncipe. Significance test for spike trains based on finite point process estimation. (Society for Neuroscience 2009) poster# 789.1/GG119
• Il Park. Capturing spike train similarity structure: A point process divergence approach. Ph.D. dissertation (2010)

Continuous-time cross correlogram

Continuous-time Cross Correlogram (CCC)	[HTML] [.m]
Normalized (standardized) CCC	[HTML] [.m]

Related publications:

• Il Park, António R. C. Paiva, José C. Príncipe, Thomas B. DeMarse. An Efficient Algorithm for Continuous-time Cross Correlogram of Spike Trains, Journal of Neuroscience Methods, Volume 168, Issue 2, 15 March 2008, 514-523
• Il Park. Continuous time correlation analysis techniques for spike trains. M.S. thesis (2007)