File:Dynamic-Excitatory-and-Inhibitory-Gain-Modulation-Can-Produce-Flexible-Robust-and-Optimal-Decision-pcbi.1003099.s007.ogv

English: Larger dynamic ranges allow robust decision-making for co-modulation of excitatory and inhibitory gains. Stability diagrams are shown as a function of excitatory gain for different inhibitory gains . Black dashed lines show parameters that fit the behavioral and neural experimental data, namely . For each stability diagram as a function of , dark shades show stable branches, while light shades show unstable ones. As the inhibitory gain is reduced, the dynamic range of the network decreases. Furthermore, our fitted parameters are not sensitive to small perturbations. If we increase or decrease or slightly, our model adequately performs its decision-making computations. However, if we had chosen a much smaller value of as our parameter, small perturbations in parameter values would have rendered the network incapable of performing its decision-making computations. On the other hand, we may increase , which would lead to the network performing its decision-making computations with a larger dynamic range. However, the unstable branch would then have a very large firing rate ( Hz) and not fit the neural experimental data.