construction [@jaynes_57]: we take from experiment certain average behaviors of the network, and then search for models that match these data but otherwise have as little structure as possible. This approach works if we can show that matching a relatively small number of features produces a model which predicts many other aspects of the data.

![Maximum entropy models for retinal activity in response to natural movies [@tkacik+al_14a]. [**(A)**]{} The correlation coefficients between pairs of neurons (red = positive, blue = negative) for a 120-neurons subnetwork. Inset shows the distribution of the correlation coefficients over the population. [**(B)**]{} The pairwise coupling matrix of the inferred model, $J_{\rm ij}$ from Eq (\[HKpair\]). Inset shows the distribution of these pairwise couplings across all pairs $\rm ij$. [**(C)**]{} The average probability of spiking per time bin for all neurons (sorted). [**(D)**]{} The corresponding bias terms $h_{\rm i}$ in Eq (\[HKpair\]). [**(E)**]{} The probability $P(K)$ that $K$ out of the $N$ neurons spike in the same time bin. [**(F)**]{} The corresponding global potential $V(K)$ in Eq (\[HKpair\]). Notice that panels A, C, and E describe the statistical properties observed for these neurons, while panels B, D, and F describe parameters of the maximum entropy model 