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Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009)
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This MATLAB code implements a model-based analysis of spike trains. The analysis predicts the occurrence of spatio-temporal patterns of spikes in the data, and is based on a maximum entropy principle by including both spatial and temporal correlations. The approach is applicable to unit recordings from any region of the brain. The code is based on Marre, et al., 2009. The MATLAB code was written by Sami El Boustani and Olivier Marre.
Marre O, El Boustani S, Frégnac Y, Destexhe A (2009) Prediction of spatiotemporal patterns of neural activity from pairwise correlations.
Phys Rev Lett
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Maximum entropy models;
El Boustani, Sami [elboustani at unic.cnrs-gif.fr];
Marre, Olivier [marre at unic.cnrs-gif.fr];
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function dist=DJS(P,Q) %Brute force estimation M=0.5*(P+Q); dist=0.5*(KL(P,M)+KL(Q,M));
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