Humans can categorize an object in different semantic levels. For example, a dog can be categorized as an animal (superordinate), a terrestrial animal (basic), or a dog (subordinate). Recent studies have shown that the duration of stimulus presentation can affect the mechanism of categorization in the brain. Rapid stimulus presentation will not allow top-down influences to be applied on the visual cortex, whereas in the nonrapid, top-down influences can be established and the final result will be different. In this paper, a spiking recurrent temporal model based on the human visual system for semantic levels of categorization is introduced. We showed that the categorization problem for up-right and inverted images can be solved without taking advantage of feedback, but for the occlusion and deletion problems, top-down feedback is necessary. The proposed computational model has three feedback paths that express the effects of expectation and the perceptual task, and it is described by the type of problem that the model seeks to solve and the level of categorization. Depending on the semantic level of the asked question, the model changes its neuronal structure and connections. Another application of recursive paths is solving the expectation effect problem, that is, compensating the reduce in firing rate by the top-down influences due to the available features in the object. In addition, in this paper, a psychophysical experiment is performed and top-down influences are investigated through this experiment. In this experiment, by top-down influences, the speed and accuracy of the categorization of the subjects increased for all three categorization levels. In both the presence and absence of top-down influences, the remarkable point is the superordinate advantage.

中文翻译：

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基于人类视觉系统的语义层次分类的时态递归模型

人类可以按不同的语义级别对对象进行分类。例如，狗可以分类为动物（上级），陆生动物（基础）或狗（下级）。最近的研究表明，刺激表现的持续时间会影响大脑的分类机制。快速的刺激表现将不允许自上而下的影响应用于视觉皮层，而在非快速的情况下，可以建立自上而下的影响，最终结果将有所不同。本文提出了一种基于人类视觉系统的尖峰递归时态模型，用于语义层次的分类。我们表明，可以在不利用反馈的情况下解决直立图像和倒置图像的分类问题，但是对于遮挡和删除问题，自上而下的反馈是必要的。所提出的计算模型具有三个表达期望和感知任务效果的反馈路径，并通过模型寻求解决的问题类型和分类级别进行描述。根据所问问题的语义水平，模型会更改其神经元结构和连接。递归路径的另一种应用是解决期望效应问题，即，由于对象中可用的特征而导致的自上而下的影响来补偿点火速率的降低。另外，本文进行了一项心理物理实验，并通过该实验研究了自上而下的影响。在此实验中，受自上而下的影响，对于所有三个分类级别，主题分类的速度和准确性均得到提高。