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Spike to Spike Model and Applications: A biological plausible approach for the motion processing

机译:Spike to Spike模型和应用:运动处理的生物学似真方法

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摘要

We propose V1 and MT functional models for biological motion recognition. Our V1 model transforms a video stream into spike trains through local motion detectors. The spike trains are the inputs of a spiking MT network. Each entity in the MT network corresponds to a simplified model of an MT cell. From the spike trains of MT cells a motion map of velocity distribution is built representing a sequence. Biological plausibility of both models is discused in detail in the paper. In order to show the efficiency of these models, the motion maps here obtained are used in the biological motion recognition task. We ran the experiments using two databases Giese and Weizmann, containing two (march, walk) and ten (e.g., march, jump, run) different classes, respectively. The results revealed that the motion map here proposed could be used as a reliable motion representation.
机译:我们提出了用于生物运动识别的V1和MT功能模型。我们的V1模型通过本地运动检测器将视频流转换为尖峰序列。尖峰列是尖峰MT网络的输入。 MT网络中的每个实体对应于MT小区的简化模型。从MT细胞的峰值序列中,构建了一个代表序列的速度分布运动图。本文详细讨论了这两种模型的生物学可行性。为了显示这些模型的效率,将此处获得的运动图用于生物运动识别任务。我们使用两个数据库Giese和Weizmann进行了实验,分别包含两个(行军,步行)和十个(例如行军,跳跃,奔跑)不同的类。结果表明,此处提出的运动图可以用作可靠的运动表示。

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