DVS棘波响应的凹窝启发滤波
Foveal-pit inspired filtering of DVS spike response
摘要：
在本文中，我们提出的结果处理动态视觉传感器（DVS）记录的视觉模式与视网膜模型的基础上凹坑启发高斯差分（DoG）滤波器。用以恒定速度水平移动的不同空间频率的不同数量的垂直白色和黑色条来刺激DVS传感器。由DVS传感器产生的输出尖峰作为输入施加到受灵长类动物视觉通路的感受野结构启发的一组DoG滤波器。特别是，这些过滤器模仿的侏儒和阳伞神经节细胞（尖峰神经元的视网膜），辅助服务的光感受器的中央凹窝的感受野。使用中央凹凹坑模型提取的特征用于使用尖峰卷积神经网络进行进一步分类，所述尖峰卷积神经网络用适于脉冲神经网络的反向传播变体进行训练。
英文摘要：
In this paper, we present results of processing Dynamic Vision Sensor (DVS) recordings of visual patterns with a retinal model based on foveal-pit inspired Difference of Gaussian (DoG) filters. A DVS sensor was stimulated with varying number of vertical white and black bars of different spatial frequencies moving horizontally at a constant velocity. The output spikes generated by the DVS sensor were applied as input to a set of DoG filters inspired by the receptive field structure of the primate visual pathway. In particular, these filters mimic the receptive fields of the midget and parasol ganglion cells (spiking neurons of the retina) that sub-serve the photo-receptors of the foveal-pit. The features extracted with the foveal-pit model are used for further classification using a spiking convolutional neural network trained with a backpropagation variant adapted for spiking neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
