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A feedback model of figure-ground assignment

机译：地物分配的反馈模型

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A computational model is proposed in order to explain how bottom-up and top-down signals are combined into a unified perception of figure and background. The model is based on the interaction between the ventral and the dorsal stream. The dorsal stream computes saliency based on boundary signals provided by the simple and the complex cortical cells. Output from the dorsal stream is projected to the surface network which serves as a blackboard on which the surface representation is formed. The surface network is a recurrent network which segregates different surfaces by assigning different firing rates to them. The figure is labeled by the maximal firing rate. Computer simulations showed that the model correctly assigns figural status to the surface with a smaller size, a greater contrast, convexity, surroundedness, horizontal–vertical orientation and a higher spatial frequency content. The simple gradient of activity in the dorsal stream enables the simulation of the new principles of the lower region and the top–bottom polarity. The model also explains how the exogenous attention and the endogenous attention may reverse the figural assignment. Due to the local excitation in the surface network, neural activity at the cued region will spread over the whole surface representation. Therefore, the model implements the object-based attentional selection.
机译：为了解释自下而上和自上而下的信号如何组合成对图形和背景的统一感知，提出了一种计算模型。该模型基于腹侧和背侧流之间的相互作用。背流根据简单皮层细胞和复杂皮层细胞提供的边界信号计算显着性。来自背流的输出被投影到表面网络，该表面网络用作形成表面表示的黑板。地表网络是一个循环网络，通过为它们分配不同的点火速率来隔离不同的表面。该图由最大燃烧速率标记。计算机仿真表明，该模型正确地将图形状态分配给具有较小尺寸，较大对比度，凸度，包围度，水平-垂直方向和较高空间频率含量的表面。背流中活动的简单梯度使得可以模拟下部区域和顶部-底部极性的新原理。该模型还解释了外在注意和内在注意如何可能逆转形象分配。由于表面网络中的局部激发，提示区域的神经活动将扩展到整个表面表示。因此，该模型实现了基于对象的注意力选择。

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