The mechanism underlying perceptual grouping of visual stimuli is not static, but dynamic. In this paper, the dynamical grouping process is implemented with a neural network model consisting of an array of (hyper)columns suggested by Hub el k Wiesel, where intracolumnar inhibition and intercolumnar facilitation are incorporated. The model was applied successfully to figures consisting of a set of dots yielding either of two ways of groupings from time to time due to neural fluctuations and fatigue. Then the model was extended to introduce dependency on fixation points as well as neural fluctuations and fatigue. Then, it was applied to the Necker Cube. The model output from time to time either of two ways of 3D interpretations depending on the fixation points.
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机译：视觉刺激的丑陋分组的机制不是静态，而是动态。在本文中，动态分组过程利用了由Hub El K Wiesel建议的（Hyper）列的阵列组成的神经网络模型来实现，其中包含绞痛和互生促进促进。该模型被成功应用于由一组点组成的数字，其由于神经波动和疲劳而不时地从时刻开始两种分组。然后扩展了该模型以引入对固定点以及神经波动和疲劳的依赖性。然后，它被施用于颈部立方体。根据固定点，模型输出两种方法中的两种方式中的任何一种。
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