Fig. 1.

(A) The stimulus reconstruction framework. Orientation is represented in the noisy firing rates of a population of neurons. The error of estimating this stimulus orientation optimally from the firing rates serves as a measure of coding accuracy. (B) The stimulus discrimination framework. The error of an optimal classifier deciding whether a noisy rate profile was elicited by stimulus 1 or 2 is taken as a measure of coding accuracy. (C) A neurometric function is a graph of the minimum discrimination error (MDE) as a function of the difference between a fixed reference orientation (upper right) and a second varied stimulus orientation (x axis). (D) The MDE for two Gaussian firing rate distributions with different mean rates corresponds to the gray area. The optimal classifier selects the stimulus more likely to have caused the observed firing rate. (E) The optimal discrimination function in the case of two neurons, whose firing rates are described by a bivariate Gaussian distribution, is a straight line if the stimulus change causes only a change in the mean. (F) If it also changes the covariance matrix, the optimal discrimination function is quadratic.