diff --git a/README.md b/README.md new file mode 100644 index 0000000..e80d727 --- /dev/null +++ b/README.md @@ -0,0 +1,66 @@ +## 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](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. diff --git a/readme.html b/readme.html deleted file mode 100644 index 27ec1c7..0000000 --- a/readme.html +++ /dev/null @@ -1,67 +0,0 @@ -
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%% 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.
-