5 and 2.5), and those that came from neurons with average shape preference for high curvature/C

(between 3 and 4)—were tested for statistical difference (using the same procedure described above using the KL divergence measure). The marginal distribution of pattern correlation for the Galunisertib purchase low/straight neurons was significantly different from those of the high-curvature/C-preferring (p = 0.0001) and the medium-curvature-preferring neurons (p = 0.001). The distributions of pattern reliability were not significantly different from each other, indicating that differences in data quality were not an issue. To examine the idea that local pooling of orientation signals within subregions of the RF determines the patterns of selectivity to more complex features, we generated predictions of location-specific response maps. This was done by spatially interpolating the fine-scale orientation-tuning map in a three step process: first, the pure spatial information in the fine-scale map, obtained by averaging across orientation at each fine-grid location, was subject to a two-dimensional (2D) nearest-neighbor interpolation (20 interpolation points) followed by a 2D Gaussian

smoothing operator (σ=2/3×thespacingbetweenfine-gridlocations); second, the pure orientation information in the map, obtained by subtracting the average orientation response from the measured data at each fine-grid location, was subject to a 2D nearest-neighbor interpolation (20 interpolation points) followed by a 2D INK1197 Gaussian smoothing operator (σ=4/3×thespacingbetweenfine-gridlocations); finally, the two components were

combined by addition. The composite stimuli (at each coarse grid location) were then projected onto this interpolated space. The response to each component element was read off as the value of the closest orientation match in the interpolated space at the location corresponding to the center of the component element. The predicted response to each composite stimulus was taken as the average of the three component responses. We Linifanib (ABT-869) then calculated the correlation coefficient, ρmodelρmodel, between the response patterns in the predicted map and the observed map. Since we were only concerned with pattern selectivity and not with rate matching, the correlation measure was sufficient for our purpose. To test for the predictive power of the model, we also calculated a null distribution of the correlation coefficients. This was done by spatially shuffling the nine tuning curves of the fine-scale orientation map within a 3 × 3 fine grid that underlay a coarse grid location (see Figure S5A), generating the predicted responses from this shuffled map (same procedure as above for the original unshuffled map) and hence the correlation coefficient between the predicted map and the observed map.