Behavioral Learning Software

We provide matlab- and winBUGS-based software that estimate the learning curve and learning trial of a single subject or a population of subjects by analyzing behavioral data obtained in a learning experiment. The learning curve characterizes the dynamics of the learning process as a function of trial number and is plotted along with its upper and lower 95% confidence bounds. The learning trial is defined as the first trial on which there is reasonable certainty ( 0.95) that for the remainder of the experiment the subject performs better than chance.

SSGLM Software

This web site contains illustrations and Matlab code for analysis of within-and between-trials dynamics of spiking activity from multiple spike trains using state-space generalized linear models as described in the paper.

Heart Rate Variability Software

This software is actively being developed and is currently only available as a demo version.

Granger Causality Measure for Point Processes (GCPP) Software

Matlab code for identifying Granger causality between multiple neural spike train data is provided

mPSCAnalysis Software

Coming Soon

nSTAT: neural Spike Train Analysis Toolbox

Cajigas I, Malik WQ, Brown EN. nSTAT: Open-Source Neural Spike Analysis Train Analysis Toolbox for Matlab

J. Neurosci. Methods (link)

Barbieri R, Matten EC, Alabi AA, Brown EN. A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol. 2005 Jan;288(1):H424-35. Epub 2004 Sep 16.

Barbieri R, Brown EN. Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng. 2006 Jan;53(1):4-12.

Smith AC, Wirth S, Suzuki WA, Brown EN. Bayesian analysis of interleaved learning and response bias in behavioral experiments. J Neurophysiol. 2007 Mar;97(3):2516-24. Epub 2006 Dec 20.

Czanner G, Eden UT, Wirth S, Yanike M, Suzuki WA, Brown EN. Analysis of between-trial and within-trial neural spiking dynamics. J Neurophysiol. 2008 May;99(5):2672-93. Epub 2008 Jan 23.

Haslinger R, Pipa G, Brown EN. Discrete Time Rescaling Theorem

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking

Neural Computation, 22:2477-2506, (2010)

This website provides MATLAB codes for the SS-MT algorithm as a demo version.

Kim S-E, Behr M, Ba D, Brown EN. State-Space Multitaper Time-Frequency Analysis. State-Space Multitaper (SS-MT) Time-Frequency Analysis Software PNAS, 115(1):E5-E14, (2018)