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Package for analyzing multi-electrode data with a Markov-Ising model.

This package corresponds to the paper:

Prediction of spatio-temporal patterns of neural activity from
pairwise correlations.

Olivier Marre, Sami El Boustani, Yves Frégnac and Alain Destexhe

Physical Review Letters, 2009.

http://arxiv.org/abs/0903.0127

The code here allows to reproduce easily the fig 1, analyze your own
multi-electrode data, and generate surrogate data with the same
statistics than the ones captured by the Markov model. The approach is
the following:

  • load or generate a raster
  • compute the mean activity of each neuron (m), the instantaneous
    pairwise correlations (C), and the pairwise correlations between time
    t and time t+1.
  • estimate the h, J and J1 parameters of the model corresponding to the
    m, C and C1: by an analytical approximation followed by a gradient
    descent. This might not be enough for a large number of neurons.
  • Estimating the performance of the fit by comparing the prediction,
    and the empirical estimation, of the ocurrence rate of different
    spatio-temporal spiking patterns. This is done for different temporal
    sizes of these patterns.

The program "BatchOctestGlauber" is performing all these steps.

1) How to use this program:

  • The best is probably to first have a look on the code which
    reproduces the figure 1. First launch "i2mPath" to set all the
    sub-directories. Then "BatchOctestGlauber" will do all the analysis
    (it takes several minutes), and stores the results in the WorkSpace
    directory. Then launch "Fig1" to draw the figure.
  • To analyze your data, construct a file "spikes.txt" which contain the
    spike times, with the format explained in /LoadRaster/LoadRaster.m
  • The directory InfoTools contains some simple methods to measure the
    Kullback-Leibler (KL) and the Jensen Shannon (Djs) divergences.
  • /Surrogate/Surrogate.m will generate some surrogate data having the
    same statistics than the ones captured by the model.

2) The code is organized in different directories:

  • Common: The core of the program. Contains all the functions needed to
    fit the model to mean and correlations measured from the data.
  • FigurePlot: routines to plot the Figure 1 of the paper
  • Figures: directory where automatically generated figures will be
    stored.
  • Glauber: to simulate the Glauber model
  • InfoTools: contains some simple methods to measure the
    Kullback-Leibler (KL) and the Jensen Shannon (Djs) divergences.
  • LoadRaster: to load and bin a raster
  • surrogate: Surrogate.m will generate some surrogate data having the
    same statistics than the ones captured by the model.
  • Workspace: where the workspace is stored after running one of the
    batchs.

2025-06-02: Converted README to Markdown.