Figure 3

Influence of noise gain on discrimination error. (A) Scenario where the noise gains of both populations (\(\beta _{x}\) and \(\beta _{y}\)) are adjusted simultaneously. In this case, the value of noise correlation leading to maximal error (\(\rho _{*}\)) remains constant. Inset: tuning curves for the two populations. (B) Error as a function of noise correlation for four different values of noise gain, with colors corresponding to the colored circles in panel “A”. Filled circles indicate \(\rho _{*}\). (C) Impact of modifying the noise gain of population x only. (D) Different values of noise gain for population x. (E) Scenario taken from panel D of Fig. 2, showing a monotonic decrease in \(\rho _{*}\) when increasing \(\beta _{x}\). (F) Impact of \(\beta _{x}\) on classification error