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Number before OR corresponds to the ownership code as listed in Table 1.
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Thus, our model predicts that local border detection and border ownership coding employ different neural mechanisms.
Figure 8 shows the border-ownership coding for a single figure and for two partially overlapping figures.
By means of feedforward surround inhibition, our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding.
Our simple, 3 layered feed-forward spiking model performs figure-ground segmentation and one-sided border-ownership coding.
By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding.
According to our model data, one-sided border-ownership coding does not depend on local feature contrast but is based on surface segregation of the figure.
For border-ownership coding single squares were placed to either the left or the right side for clarifying the side preference (fig. 4a).
In essence, the idea of opposite receptive field sub-regions is reminiscent of the opponent model for border-ownership coding as proposed by Zhou et al. [15].
The aim of this study is therefore to have a better understanding of the role of feedforward connections in figure-ground organization, in particular in surface segmentation and border-ownership coding.
Perhaps the more specific software engineering team factors, for example the agile development method's collective ownership and code management, are decisive.
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