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.

中文翻译：

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凹坑启发的 DVS 尖峰响应过滤

在本文中，我们展示了处理动态视觉传感器 (DVS) 记录的视觉模式的结果，该视网膜模型基于受中心凹坑启发的高斯差分 (DoG) 滤波器。DVS 传感器受到不同空间频率的不同数量的垂直白条和黑条的刺激，这些条以恒定速度水平移动。由 DVS 传感器产生的输出尖峰作为输入应用于一组受灵长类动物视觉通路感受野结构启发的 DoG 过滤器。特别是，这些过滤器模拟侏儒和阳伞神经节细胞（视网膜的尖峰神经元）的感受野，为中央凹的光感受器提供辅助服务。