Welcome to Spike Train Analysis with R project!

STAR (Spike Train Analysis with R) 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 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:
  • In the spontaneous regime. The example of function/method reportHTML.spikeTrain shows how to generate this report.
  • With repeated stimulations applied. The example of function/method reportHTML.repeatedTrain shows how to generate this report.
  
  
• STAR runs with the last version of R: R-2.6.0
  
• A draft of a manuscript describing the basic functionalities of STAR and including a tutorial is available. Last update: November 13.
  



---

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 inter-spike intervals (ISIs) in stationary discharge regimes.
• Fit of an ISI sample from a single neuron in stationary regime with various models: log-normal, inverse Gaussian, gamma, Weibull, refractory-exponential, log-logistic. 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.
• Cross-correlograms 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 statistical smoothing approach of Kass, Ventura and Cai (2003) Network: Computation in Neural Systems 14: 5. But instead of their BARS method a GAM (General Additive Model) based approach with the R package mgcv of Simon Wood is used.
• The spike-train probability model approach of Kass and Ventura (2001) Neural Comp. 13: 1713. The job is done again using the GAM based approach of mgcv.
• Hidden Markov Models can be used in the spontaneous regime.
• A demo reproducing Fig. 2-13 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 sound of Matthias Heymann in order to generate genuine spikes songs. Hidden Markov modeling requires the installation of the HiddenMarkov package of David Harte.



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 threee data sets include both spontaneous activity and odor responses.
• Purkinje cells recording from rat cerebellar slices. Data obtained and sorted by Matthieu Delscluse.
• The earthquakes data set used by Ogata (1988).


STAR is under active development any comments, suggestions and contributions are warmly welcomed.


The project summary page you can find here.