This fundamental difference between the two models creates some difficulty in thinking about them. In particular, the existence of functional architecture confounds the two potential mechanisms of topographic specificity and functional specificity. For instance, in two species, there is strong evidence that topographic specificity, rather than (local) functional specificity,

can help account for the generation of orientation specificity. In the ferret, as noted above, the LGN cells projecting to a single column have receptive fields that line up in a row whose orientation matches that of the local cortical neurons (Chapman et al., 1991). Thus, cortical orientation selectivity can be achieved by nonspecific summation of the locally available afferents. In the tree shrew, there is a similar arrangement, except it is KPT 330 caused by anisotropic intracortical projection of axons. In the tree shrew, layer 4 neurons are not orientation selective, so orientation selectivity is generated first in layer 2/3 but FG-4592 price otherwise the arrangement is similar to the ferret.

Unlike in the ferret, however, the spatial elongation of the afferent connections was demonstrated anatomically, rather than physiologically. Using a clever combination of optical imaging and anterograde axonal tracing, Fitzpatrick and colleagues (Mooser et al., 2004) demonstrated an orientation-specific arrangement of layer 4 afferents to layer 2/3. As in the ferret,

the receptive fields of the afferents line up in a row retinotopically, so that orientation selectivity could be generated with indiscriminate pooling by layer 2/3 neurons of their local afferents. By the Ergoloid definitions of the terms (above), this is an example of topographic specificity rather than local functional specificity. Because functional architecture can often make it difficult to differentiate topographic from functional specificity, it is fortunate therefore that two of the currently favored species for visual physiology, rats and mice, do not have functional architecture for orientation selectivity (Ohki et al., 2005; Figure 2A). Instead, cells that respond to different orientations are completely intermingled, as are cells that have different configurations of their simple receptive fields (Bonin et al., 2011). Thus, almost by definition, any specificity of wiring that underlies receptive-field properties must be due to some combination of cell-type and functional specificity (Figures 2B and 2C). For many reasons, the mouse is not the best model for understanding human vision, of course. But the mouse visual cortex is proving to be an excellent model for studying general principles of cortical computation.