We then calculated the correlation coefficient between the observed

response pattern and the predicted response pattern. Note that the fine-scale orientation maps contain both a spatial response component and an orientation-tuning component. To investigate the contribution of these components, we also considered two reduced versions of the pooling model (see Experimental Procedures; Figure S5C). A space-only version was obtained by averaging across orientation at each BMS-354825 supplier fine-grid location. This model did not have any local orientation tuning. An orientation-only version was obtained by subtracting the space-only response from the measured data at each fine-grid location, leaving only orientation tuning. Thus, this model did not contain any local spatial information. The predicted response maps for two example neurons

(neurons II and III in Figures 2 and 3) are shown in Figure 7A (panels labeled “prediction”). Maps are shown for three different RF locations for each neuron. For the RF location marked “1”, the left panel shows the empirical data, while the other three panels show the predicted buy NVP-AUY922 responses from the full model and the two reduced models. Shown below the predicted response maps are the corresponding sections of the fine-scale orientation map, which were used to generate the predictions. To take the example of RF location 1 in neuron II, we can see clearly that the selectivity for medium-curvature shapes pointing upward arises from the layout of the fine-scale map; the middle location is tuned to horizontal elements, the upper-left location

mafosfamide is tuned to elements tilted 45 degrees counterclockwise, and the upper-right location is tuned to elements tilted 45 degrees clockwise (and also vertical). There is a close correspondence between the data and the predicted patterns both for the full model and the orientation-only model. The space-only model performed less well but still explained significant parts of the response (ρ=0.43ρ=0.43 for the space-only model versus ρ=0.58ρ=0.58 for the orientation-only model). Thus, both spatial and orientation components contribute giving the best correlation (ρ=0.67ρ=0.67) for the full model. Only the predictions of the full model are shown for RF locations “2” and “3”. The model correlations (full model only) at each spatially significant location are shown in the lower left panel of Figure 7A. In the case of example neuron III, the local orientation tuning was highly heterogeneous and most of its curvature selectivity could be explained by local spatial tuning alone. As seen for RF location 1, the largest responses occur for composite shapes whose ends fall in the upper part of the fine-scale grid where the spatial response is higher (i.e., on the RF boundary).