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. 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. 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: 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. This software is actively being developed and is currently only available as a demo version.
Barbieri R, Brown EN. Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng. 2006 Jan;53(1):4-12. 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. Coming Soon Matlab code for identifying Granger causality between multiple neural spike train data is provided Kim S,
Putrino D, Ghosh S, Brown EN. nSTAT: neural Spike Train Analysis Toolbox Cajigas I, Malik WQ, Brown EN nSTAT: Open-Source Neural Spike Analysis Train Analysis Toolbox for MatlabJ. Neurosci. Methods (link) Discrete Time Rescaling Theorem Haslinger R, Pipa G, Brown EN Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural SpikingNeural Computation, 22:2477-2506, (2010) |

### Software

Subpages (1):
Discrete Time Rescaling Theorem