Figure 4: Block diagram of a typical computational neural model of V1 used to predict masked detection thresholds (here with a noise mask and a circular sinewave target). Three key stages are employed in most neural models: (1) a frequencybased decomposition which models the initially linear responses of an array of visual neurons, (2) application of a pointwise nonlinearity to the decomposition coefficients and inhibition based on the values of other coefficients [99, 102–104], and (3) pointwise differences between the adjusted coefficients and summation of these adjusted coefficient differences across space, spatial frequency, and orientation so as to arrive at a single scalar differential response value or a map of differential response values. 
