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(b) The Jensen Shannon (JS) divergence between the EIF and the pairwise maximum entropy (PME) model.

The heat capacity for the PME model saturates at a population of approximately (N=30) neurons.

In particular, the EIF model produces departures from the PME model for a wide range of correlations ρ and mean firing rates μ.

Fig. 2 (a) Population spike-count distributions (P_{mathrm{EIF}} k)) for the EIF and (P_{mathrm{PME}} k)) for the pairwise maximum entropy (PME) model, for populations of (N= 8, 32, 64), and 100 neurons.

The PME model matches the spike probability μ for each neuron and pairwise spike correlation ρ for each pair of neurons, while making minimal further assumptions on the joint probability distribution [9, 16, 18, 31, 33], cf. [15, 30].

To answer this, we compare the population spike-count distribution (P_{mathrm{EIF}} k)) from the EIF model against that which would be predicted for a pairwise maximum entropy (PME) model of spiking neurons.

Our PME compliance prediction model included 10 behavioral determinants and its relevance and accuracy were verified by SEM in the low SDRT group.

The CpL-PME mode of action was modeled while the HG population's nanostructure and the resulting functionality of the modified pectin were characterized.

We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package.

In particular we show that, in contrast to the PME, the dichotomous Gaussian model gives a highly accurate description of the complete correlation structure of an integrate-and-fire population with common inputs.

PMe developed the statistical models and revised the statistics section of the manuscript.