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.

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Abstract

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.

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