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Jointly efficient encoding and decoding in neural populations

Fig 7

Optimal allocation of neural resources.

In all simulations, N = 12 and . Results are illustrated for regions of the stimulus space where the coding performance is sufficiently high, defined as the region where the MSE is lower than the variance of the stimulus distribution. Below, we mention exponents of the power law fit when the variance explained is larger then a threshold, R² ≥ 0.7. (A) Neural density as a function x (dashed curves) and power-law fits (solid curves, R² = (0.21, 0.83, 0.95), γ_d = (−, 0.43, 0.62)), for three values of (low, intermediate, and high); the grey curve illustrates the stimulus distribution. The density is computed by applying kernel density estimation to the set of the preferred positions of the neurons. (B) Tuning width, w_i, as a function of preferred stimuli, c_i (dots), and power-law fits (solid curves, R² = (0.78, 0.42, 0.82), γ_w = (1.15, −, 0.7)) for three values of ; the grey curve illustrates the stimulus distribution. (C) Tuning width, w_i, as a function of the neural density, d(c_i), for three values of ; Pearson correlation coefficient ρ = (−0.74, −0.66, −0.79).

doi: https://doi.org/10.1371/journal.pcbi.1012240.g007