title: Prediction of spatiotemporal 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 spatiotemporal patterns significantly better than Ising models only based on spatial 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.
authors.affiliation: Unité de Neurosciences Intégratives et Computationnelles (UNIC), UPR CNRS 2191, Gif-sur-Yvette, France.
authors.forename: O
authors.initials: O
authors.lastname: Marre
authors.forename: S
authors.initials: S
authors.lastname: El Boustani
authors.forename: Y
authors.initials: Y
authors.lastname: Frégnac
authors.forename: A
authors.initials: A
authors.lastname: Destexhe
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001703:Biophysics / Q000379:methods*; D019277:Entropy; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009043:Motor Activity; D009474:Neurons / Q000378:metabolism / Q000502:physiology*; D011336:Probability; D015203:Reproducibility of Results; D012890:Sleep