Prediction of spatio-temporal patterns of neural activity from
Olivier Marre, Sami El Boustani, Yves Frégnac and Alain
Physical Review Letters 102: 138101, 2009.
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.
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.
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