  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.
