Figure 7.

Optimal stimulus set design. A, Depiction of the theoretical framework for stimulus design. The stimulus set maps to a noisy response set through a nonlinear relationship. B, The optimal set of response distributions was identified by maximizing the minimum probability of correct classification across the stimulus set. In the diagram, the gray region indicates the range of response values for which the response would have been correctly classified, given s₃. C, The trial-to-trial cortical variability was response dependent, and two different models were compared. The IVM modeled the variability as a linear function of the response, similar to the experimental observations for sensory stimuli. The PVM modeled the variability as a Gaussian function of the response, with the peak variability occurring at the threshold, similar to the experimental observations for artificial stimuli. For n = 4, the optimal set of discriminable responses and stimuli are shown for both cases on the vertical and horizontal axes, respectively. D, The performance (minimum probability of correct classification) decreased as the number of distinct stimuli increased. The performance across the IVM and PVM was identical for their optimal stimulus sets, and both were above chance (gray curve). The performance dropped to chance when the optimal stimuli were used in the mismatched variance models (PVM → IVM, dashed black; IVM → PVM, dashed red).