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. 2014 Jan 2;10(1):e1003408. doi: 10.1371/journal.pcbi.1003408

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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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(A) The per-neuron average log-probability of data (log-likelihood, ) under the pairwise model of Eq (19), computed on the training repeats (black dots) and on the testing repeats (red dots), for the same group of N = 100 neurons shown in Figure 1 and 2. Here the repeats have been reordered so that the training repeats precede testing repeats; in fact, the choice of test repeats is random. (B) The ratio of the log-likelihoods on test vs training data, shown as a function of the network size N. Error bars are the standard deviation across 30 subgroups at each value of N.

Inline graphic — (A) The per-neuron average log-probability of data (log-likelihood, ) under the pairwise model of Eq (19), computed on the training repeats (black dots) and on the testing repeats (red dots), for the same group of N = 100 neurons shown in Figure 1 and 2. Here the repeats have been reordered so that the training repeats precede testing repeats; in fact, the choice of test repeats is random. (B) The ratio of the log-likelihoods on test vs training data, shown as a function of the network size N. Error bars are the standard deviation across 30 subgroups at each value of N.