State dependence of stimulus-induced variability tuning in macaque MT
Fig 6
A single parameter, gain variance, can account for the observed changes in Fano factor tuning with behavioral state.
(A-D) Model fitting captures differences in Fano factor tuning through changes in gain variance. The model is fit with the optimal α and var(g) for each population. (A) The distribution of FFTI for alert (blue, throughout) and anesthetized (orange, throughout) in the observed population (dashed trace) and the model values (solid trace). Inset: Sample tuning curves for the alert and anesthetized experiments. (B,C) The variance model predicts Fano factor direction tuning for the alert (B) and anesthetized (C) experiments. Compare model results to observed Fano factors in Fig 3C and 3D. Parameters are fit to match distribution of FFTI, but reasonably reproduce Fano factor tuning as well. (D) Sample Fano factor tunings generated by the best-fit models shown in (A-C). (E) Distribution of FFTI for alternate model fitting in which the spike count used for each neuron was replaced by the average tuning curve over all neurons recorded in each experimental condition. The α parameter is identical for all neurons and was chosen to minimize mean-squared error in the Kolmogorov-Smirnov distance between the observed and model FFTI distributions. The gain variance was fit separately for each neuron to match its observed FFTI (fit to minimize the mean-squared error). Inset: Mean tuning curves for the alert and anesthetized experiments. (F) The cumulative distribution of gain variance parameters from the model fit in (E), showing larger gain variance values in the anesthetized model.