5 and 2.5), and those from neurons with average shape preference for high-curvature/C-shaped stimuli (shape preference values 3 and 4) ( Figure 5B, lower histograms). If those neurons that showed variation in their response

pattern across locations did so due to noise in their estimates (i.e., due to low firing rates or fewer trial repeats), then we would expect them to have low reliability values. Thus, differences in spatial invariance cannot be attributed to differences in the statistical reliability of estimates. One last point that is worth highlighting is that pairs with lower pattern correlation values come from neurons with a preference for higher-curvature/C shapes, whereas those with higher pattern correlation come from neurons with a preference for straight/low-curvature shapes. The distribution of pattern correlation of the straight/low-curvature subpopulation is significantly different check details from those of the other two subpopulations ( Figure 5B, right histograms; p = 0.001 and p = 0.0001, respectively; see http://www.selleckchem.com/products/azd5363.html Experimental Procedures). We thus find evidence for a trade-off between shape selectivity and position invariance. This phenomenon is evident in terms of both the peak shape selectivity and the overall

firing rate patterns to the entire set of composite shapes. We questioned whether or not we could explain the diversity of shape tuning from the diversity in the fine-scale orientation-tuning maps of V4 neurons (Figure 6). Some neurons show high degrees of translation invariance for orientation DNA ligase at this finer scale (Figure 6, bottom row) while others show heterogeneous tuning (Figure 6, top row). As noted above, the spatial layout of the fine-scale orientation-tuning maps in our example cells (Figure 3C) seems to reflect the cell’s shape-selective properties. It has been proposed, both from experimental observations (Chapman et al., 1991; Jin et al., 2011) and theoretical simulations (Paik and Ringach, 2011), that simple pooling of the spatially segregated afferent connections from the lateral geniculate

nucleus (LGN) to the primary visual cortex (V1), might determine both the orientation-tuning characteristics of V1 neurons as well as the pinwheel structure of orientation maps in V1. We hypothesized that this pooling architecture might carry forward to downstream retinotopic extrastriate areas like V4. This hypothesis is also consistent with earlier proposals, in which neuronal responses in V4 to combinations of line elements are weighted averages of the responses evoked by individual elements (Ghose and Maunsell, 2008; Lee and Maunsell, 2010; Reynolds et al., 1999; Reynolds and Heeger, 2009), and with related proposals in MT (Heuer and Britten, 2002; Rust et al., 2006) and IT (Zoccolan et al., 2005).