As described above, the correlation between LGN inputs is necessary for this variability to appear in simple cells despite the pooling of multiple inputs at the simple cell membrane. Unlike the variability in Vm of both the model and data (Figures 5B and 5C), the variability in the modeled synaptic input from the LGN (conductance, g) is strongly orientation dependent ( Figures 7B and 7F). This dependence is a function of the elongation of the subfields,

and that larger numbers of LGN afferents are activated simultaneously by the preferred stimulus compared to the null stimulus. As discussed above, the orientation dependent variability in g is transformed into the orientation independent variability in Vm by the saturating nonlinear relationship between g and Vm; removing the nonlinearity increases the orientation dependence of Vm variability ( Figures 6G–6I). this website check details The mechanism

underlying this transformation is illustrated in Figure 7C. The variability in g at the preferred orientation (gray) is higher than at the null orientation (cyan). Because that variability is occurring around a high mean g ( Figure 7C, gray)—where the slope of the g-Vm curve is flatter—it gives rise to a comparable level of variability in Vm as does the variability in g at the null orientation, which varies around the much lower resting g ( Figure 7C, cyan). The same compressive effect occurs, to a lesser degree, at low contrast ( Figures 7F and 7G, magenta and green). As a result, the variability in Vm is less dependent on orientation ( Figures 7D and 7G) than else the variability in visually evoked conductance. Note that a more-rapidly saturating relationship between LGN activity

and Vm could potentially make the variability more equal across orientations. Historically, the feedforward model of visual cortex has been rightfully questioned for its failure to account for a large number of the response properties of simple cells: the sharpness of orientation tuning and its mismatch with receptive field maps, contrast invariance of orientation tuning and contrast-set gain control, cross-orientation suppression, contrast dependence of response phase, contrast dependence of preferred temporal frequency, and direction selectivity. All of these properties can be accounted for in models that incorporate cross-orientation inhibition or orientation-independent inhibition (Heeger, 1992, Troyer et al., 1998, Kayser et al., 2001, Lauritzen et al., 2001, Martinez et al., 2002, Lauritzen and Miller, 2003 and Hirsch et al., 2003). In gain-control models, almost all of these properties emerge from a single underlying mechanism: a large shunting inhibition that is contrast dependent and orientation independent (Heeger, 1992, Carandini and Heeger, 1994 and Carandini et al., 1997).