(A1–A4) Neural network models used to generate the synthetic spike-count data. Two leaky integrate-and-fire neurons (“LIF1” and “LIF2”, see Section “”) receive spike inputs from three separate populations of neurons (rectangular boxes and circles), but only one population sends input to both of the neurons. All input spike trains were Poisson-distributed. Each neuron had a total inhibitory input rate of

. We had three times as many excitatory spikes as inhibitory spikes. We increased the absolute correlation between the spike-counts by shifting the rate of the left and right populations to the center population. The center population was active in half the simulation time. The total simulation time amounted to

. Spike-counts were calculated for

bins. (B) Empirical distribution for the model with an inhibitory input population (see A3) obtained for

bins and a correlation coefficient of

. (C1–C4) Log likelihoods of the best fitting Clayton copulas with negative binomial marginals as a function of the strength of the input correlation. Plots shown (C1

C4) correspond to the four different network models (A1

A4). Dotted, dashed, solid, and dashed-dotted lines correspond to the best fitting Clayton copula with lower, lower-right, upper-left, and upper orthant dependence (see ). Copulas were fitted using the IFM estimators.

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