Semiparametric pairwise model outperforms other models. Jan Humplik Gašper Tkačik 10.1371/journal.pcbi.1005763.g004 https://plos.figshare.com/articles/figure/Semiparametric_pairwise_model_outperforms_other_models_/5419579 <p><b>A)</b> Out-of-sample log-likelihood improvement relative to the pairwise model per sample per neuron averaged over subnetworks. Error bars denote variation over subnetworks (1 SD, no errorbars for <i>N</i> = 160 since there is only one subpopulation of that size in the entire dataset). The error in likelihood estimation is much smaller than the displayed error bars. <b>B)</b> The same as in A) but for single populations from two different experiments–one in which the population is stimulated with a random checkerboard stimulus, and the other where the population responds to a full-field flickering. <b>C)</b> The test set error rate averaged over neurons for predicting the response of a neuron from the activities of other neurons in 5 different subpopulations of 100 neurons. <b>D)</b> Average (across neurons) error rate decrease achieved by using a semiparametric pairwise model instead of a K-pairwise model for subpopulations of various sizes. Error bars denote 1 SD variation over subnetworks.</p> 2017-09-19 17:24:52 pairwise Ising model neuron population activity patterns nonlinearity exhibit criticality