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Jointly efficient encoding and decoding in neural populations
Fig 9
Generative models for the distribution of acoustic frequencies.
In all simulations, N = 12. The decoder is either Gaussian (top row) or log-normal (bottom row). (A) Solutions of the optimization problem as a function of the target rate, 
(blue curve), in the rate-distortion plane. Inset: environmental distribution of acoustic frequencies, π(f), and generative model fit for two different values of the target rate, colored according to the legend. (B) Optimal tuning curves for different values of 
. Each dot represents a neuron: the position on the y-axis corresponds to its preferred stimulus, the size of the dot is proportional to the tuning width, and the color refers to the amplitude (see legend in Fig 4). The curve on the right illustrates the stimulus distribution, p(f). Insets show two examples. (C) Frequency discrimination as a function of acoustic frequency. Red markers are data points from three different subjects, data from Ref. [61]. Solid curves are the RMSE for three values of 
, scaled by a factor of 
, with variance explained R2 = (0.42, 0.41, 0.66). (D)-(F) Same as panels (A)-(C) in the case of a log-normal decoder. In panel F, 
, with variance explained R2 = (0.92, 0.81, 0.96). Solid curves illustrate the mean across different initializations and shaded regions correspond to one standard deviation.
doi: https://doi.org/10.1371/journal.pcbi.1012240.g009