Prediction of spatio-temporal patterns of neural activity from pairwise correlations.
Olivier Marre, Sami El Boustani, Yves Frégnac and Alain Destexhe

Physical Review Letters 102: 138101, 2009.

Copy of the full paper (PDF)
We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatio-temporal patterns, significantly better than Ising models only based on pairwise correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.
    Method to analyze spatiotemporal correlations from spike trains (zip format)
    This MATLAB code implements the model-based analysis of spike trains described in the article above. The approach is applicable to unit recordings from any region of the brain. The MATLAB code was written by Sami El Boustani and Olivier Marre.