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Simultaneously recorded neurons exhibit correlations whose underlying causesare not known. Here, we use a population of threshold neurons receivingcorrelated inputs to model neural population recordings. We show analyticallythat small changes in second-order correlations can lead to large changes inhigher correlations, and that these higher-order correlations have a strongimpact on the entropy, sparsity and statistical heat capacity of thepopulation. Remarkably, our findings for this simple model may explain a coupleof surprising effects recently observed in neural population recordings.

An analytically tractable model of neural population activity in the presence of common input explains higher-order correlations and entropy