Extended Data Fig. 3: Performance of the likelihood functions decoded by DNN-based decoders.
From: A neural basis of probabilistic computation in visual cortex
a, b, Results on independent Poisson population responses. a, KL divergence between the ground truth likelihood function and likelihood function decoded with: a trained DNN \(D_{{\mathrm{DNN}}}\) vs. independent Poisson distribution assumption \(D_{{\mathrm{Poiss}}}\). Each point is a single trial in the test set. The distributions of \(D_{{\mathrm{DNN}}}\) and \(D_{{\mathrm{Poiss}}}\) are shown at the top and right margins, respectively. The distribution of pair-wise difference between \(D_{{\mathrm{DNN}}}\) and \(D_{{\mathrm{Poiss}}}\)is shown on the diagonal. b, Example likelihood functions. The ground truth (solid blue), independent-Poisson based (dotted orange), and DNN-based (dashed green) likelihood functions are shown for selected trials from the test set. Four random samples (columns) were drawn from the top, middle and bottom 1/3 of trials sorted by the \(D_{{\mathrm{DNN}}}\) (rows). c, d, Same as in a, b but for simulated population responses with correlated Gaussian distribution where the variance is scaled by the mean.
