Prediction of spatio-temporal patterns of neural activity from pairwise correlations
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 taking into account only 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.
Keywords
Cite
@article{arxiv.0903.0127,
title = {Prediction of spatio-temporal patterns of neural activity from pairwise correlations},
author = {Olivier Marre and Sami El Boustani and Yves Fregnac and Alain Destexhe},
journal= {arXiv preprint arXiv:0903.0127},
year = {2009}
}
Comments
Physical Preview Letters (in press, 2009)