 STAR is an R package
to analyze spike trains. It provides tools to visualize spike trains
and fit, test and compare models of discharge applied to actual data.
 STAR works with spike train(s) from one neuron or several neurons
simultaneously recorded.
 STAR can perform automatic analysis of spike trains and generate html reports.
The following two links show reports generated by a single command of STAR:
 A more systematic presentation of automatic analysis results as well as of the smoothing spline based intensity estimation results (in progress) of the STAR data sets is presented in the gallery.
 STAR runs with the last version of R: R2.9.2
 STAR version 0.32 can be downloaded from CRAN.
 STAR comes with a short tutorial describing how to estimate the (conditional) intensity of a spike train. A long version of this tutorial is available from this web site together with the vignette allowing you to reproduce entirely the document (you need to have the R package snow installed in order to do that since the vignette runs computations in parallel on multicore CPUs).
 If you are new to R start by looking at the appendix of the first manuscript of the "STAR trilogy" bellow. The latter is a short tutorial to R and to the basic STAR features.

The STAR trilogy:
 Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR (in press J Neurosci Methods) describes the basic functionalities of STAR and includes a tutorial. Work by C Pouzat and A Chaffiol. A vignette is available from this page both in pdf and in Rnw formats.
 A manuscript on goodness of fit tests for spike train models shows additional STAR features. Work by C Pouzat and A Chaffiol. An HTML version is also available as well as the vignette and the associated BibTeX file.
 Static and dynamic models for spike train analysis: Models, model diagnostics and opensource software: a manuscript describing how to estimate the conditionnal intensity of a spike train with the smoothing spline approach. Work by C Pouzat, A Chaffiol and C Gu.
Features presently implemented in STAR
include:
 Instantaneous firing rate estimates. They are obtained with
a convolution of a Gaussian kernel with the spike train.
 Test of independence of successive interspike intervals
(ISIs) in stationary discharge regimes.
 Fit of an ISI sample from a single neuron in stationary
regime with various models: lognormal, inverse Gaussian, gamma,
Weibull, refractoryexponential, loglogistic. The model parameters are obtained with the
method of maximum likelihood and models are compared with AIC.
 Test of the adequacy of the best model above.
 Crosscorrelograms of spike trains from different neurons
recorded simultaneously.
 The time rescaling (Brown et al, 2002, Neural Comp. 14: 325) / time transformation (Ogata, 1988, JASA, 83: 9)
is implemented with the full collection of tests of Ogata (1988). The following example
illustrates the need for the full tests collection. This is a case
where Berman's test (the one used by Brown et al, 2002) is passed but
not the other three (look at the bottom of the page). This does not
mean that Berman's test is bad and the others good (we have example
where the former fails while one of the latter is succesfull), just
that more than a single test should be used. If you want to try out for
yourself, the data are available
(505 spike times in ASCII format, one time per line).
 A new goodness of fit test is implemented based on the mapping of the "martingale": counting process  integrated conditional intensity on a Wiener Process. A proof of the mapping exists (I thank Vilmos Prokaj for telling me where to find it). The finite sample size performances of the test are excellent. See the documentation of summary.CountingProcessSamplePath in the present STAR release as well as our manuscript on goodness of fit tests for spike train models.
 The statistical smoothing approach of Kass, Ventura and Cai (2003) Network: Computation in Neural Systems 14: 5.
But instead of their BARS method a smoothing spline approach with package gss of Chong Gu or a GAM (General Additive Model) based approach with the R package mgcv of
Simon Wood are used.
We provide an R script comparing BARS and
gss performances on the examples of functions: bars and barsP. Another R script
shows that one can do better with smoothing splines on the example of Fig. 3 and Table 1 of Kaufman, Ventura and Kass (2005) (we do better but we still don't beat BARS on this example).
 The spiketrain probability model approach of Kass and Ventura (2001) Neural Comp. 13: 1713.
The job is done again using the smoothing spline based approach of gss or the GAM based approach of mgcv.
 Hidden Markov Models can be used in the spontaneous regime.
 A demo reproducing Fig. 213 of Ogata (1988) shows how to work with (fully) parametric models.
Other R packages required to run STAR:
You will have to download from
your favorite CRAN
server and install the R package R2HTML of Eric Lecoutre in order to generate reports in html format
as well package.
Hidden Markov modeling requires the installation of the
HiddenMarkov package of David Harte.
This package is now also located on CRAN.
STAR is released under the
same conditions (license, etc) as SpikeOMatic.
It is installed like any other R package.
STAR comes with a full documentation and a couple of demo files as well as the
vignette of the manuscript describing the basic functionalities of the software.
STAR includes several data sets:
 Several neurons recorded extracellularly and simultaneously from the Cockroach antennal lobe. These data were recorded
and the spike sorting was performed by Antoine Chaffiol.
The five data sets include both spontaneous activity and odor responses.
 Purkinje cells recording from rat cerebellar slices. Data obtained and sorted by Matthieu Delescluse.
 The earthquakes data set used by Ogata (1988).
STAR is under active development any comments, suggestions and
contributions are warmly welcomed.
The fomer version of STAR is still available.
STAR is for now developed by:
 Christophe
Pouzat, Laboratoire de Physiologie Cérébrale,
CNRS UMR 8118, Paris, France.
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