Figure 5.

Pairwise maximum entropy models were better at fitting mitral/tufted (M/T) cell ensemble activity when the animal was stationary as compared with when it was running. A: schematic of the process for converting raw spiking data into binary states, and then estimating statistical features of neuronal activity that are used by a maximum entropy model to predict the distribution of network states. B: schematic of features in a pairwise maximum entropy model of a 4-neuron population. C: example histogram of patterns of activity observed in a 4-neuron population (24 = 16 possible patterns) when the animal was stationary (bottom) versus when it was running (top). Purple and green bar graphs correspond to the actual probabilities observed from the recordings and gray bar graphs correspond to the probabilities estimated from the maximum entropy model. D: for an example 8-unit population, the rate occurrence of each firing pattern from the pairwise maximum entropy model was plotted against the observed data for running (green) and stationary (purple) epochs. Insets: histograms of prediction error of rate of occurrence of the firing patterns showed that error was higher when the animal was running. E: Kullback–Leibler divergence (KLD), which measures the goodness of pairwise maximum entropy model prediction for different subpopulations sizes from 3 to 19 units during running vs. stationary epochs. *P < 0.05, two-sided Wilcoxon signed-rank test with Bonferroni correction. Inset: mean KLD for each recording session during running vs. stationary epochs for a 11-neuron subpopulation.