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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network
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Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

机译：在尖峰神经网络中使用类似于皮质的特征对AER运动事件进行前馈分类

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摘要

This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system’s most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.
机译：本文介绍了一种事件驱动的前馈分类系统，该系统从时间对比地址事件表示（AER）传感器获取数据。拟议的系统提取基于生物的皮质样特征，并使用基于AER的速度加速器分类器（泄漏的整合并发射尖峰神经元的网络）来区分不同的模式。该系统最吸引人的特征之一是其事件驱动的处理，输入和功能均采用地址事件（峰值）的形式。该系统在AER姿态数据集上进行了评估，并与两个最新开发的生物启发模型进行了比较。实验结果表明，它消耗更少的仿真时间，同时仍保持可比的性能。此外，对美国国家标准技术研究院（MNIST）图像数据集进行的实验表明，该系统不仅可以处理原始AER数据，而且还可以处理图像（通过将图像转换为AER事件的预处理步骤），并且即使添加噪声，它也可以保持竞争优势。该系统在MNIST动态视觉传感器数据集（其中使用AER动态视觉传感器记录数据）上进行了进一步评估，测试准确性为88.14％。

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