< Back to Article

Subunit-specific behavioral modulation of sensory tuning in the visual cortex

Fig 4

Quantification of differential modulation of sensory subunits.

a) Left: tuning curve of an example neuron with differentially modulated subunits. Right: two bootstrap samples with simulated shared modulation corresponding to the neuron on the left. b) Same as a but for an example neuron with shared modulation between subunits. c) Left: modulators fitted to the tuning curve in a (orange and gray lines correspond to marks in panel a). Dashed black line denotes the correlation maximizing fit to both modulators. Note that the fitted curve is identical for both modulators up to a scaling and a shift. The average correlation of the fit with the two modulators is equal to 0.711. Right: the same as on the left but for bootstrap sample I depicted in panel a. b) Same as c, but for the example neuron displayed in b. e) Left: example tuning curve of a neuron with differently modulated subunits. Middle: a null distribution of the test-statistic , i.e., the average correlation of the correlation-maximizing fit with both modulators, derived from 1000 bootstrap samples with simulated shared modulation. The vertical dashed line denotes significance threshold at p = 0.01 and the orange triangle the value of the test statistic for the tuning curve on the left. Right: three example bootstrap samples with simulated shared modulation. f) Same as e, but of an example neuron whose subunits are not significantly differently modulated. g) Visualization of the test statistic distribution for 150 randomly selected neurons. Gray circles denote the value of the test statistic averaged across all bootstrap samples (x-axis) and the value for the data (y-axis). Thin-gray lines denote the range between 1st and 99th percentile. h) Fraction of neurons that pass the significance threshold of the test for differential modulation of sensory subunits as a function of the threshold value. The threshold is computed as a quantile of the null distribution obtained for each neuron individually via bootstrapping. 35 percent of neurons pass the significance threshold at p = 0.01. i) Histogram of the test statistic for the data (orange) and the average across bootstrap samples for each neuron (gray). The distributions are significantly different (KS-test, p-value < 0.001). Inset depicts the same distribution on the log-probability scale. j) Example tuning curves that pass the significance threshold at p-value thresholds of 0.01, 0.05 (top and middle rows respectively) and that do not pass the threshold of 0.1 (bottom row).

doi: https://doi.org/10.1371/journal.pcbi.1014123.g004