Performance of multiplicative and additive models. ILD singular vectors for BC = 1 are plotted against ILD vectors for BC = 0 to show their similarity in shape (a). The solid line represents the linear regression and R2 is the coefficient of determination. There is a high correlation between the ILD singular vector for BC = 1 and all lower BCs (b). The correlation between the ITD vector for BC = 1 and BC = 0, where the ITD input is reduced to noise, is low. However, ITD vectors for nonzero BCs were highly correlated with that for BC = 1 (c). The multiplicative model better fits the data than the additive model for those BCs (d). The means of squared differences obtained by subtracting the additive (empty circles) and multiplicative (solid circles) models from the data matrices are plotted against BC. The number of neurons used in each case (N) is indicated above the plot. Error bars represent SD.
