Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics. The vast majority of current systems acquire a large amount of images which are used to train deep artificial neural networks. However, this approach increases both the latency and power consumption of the final apparatus. In this work-in-progress, we combine an event-based camera with a free-space optical setup to obtain spikes for each particle passing in a microfluidic channel. A spiking neural network is trained on the collected dataset, resulting in 97.7% mean training accuracy and 93.5% mean testing accuracy for the fully event-based classification pipeline.

翻译：摘要：成像流式细胞仪系统旨在基于它们的物理特征分析大量的细胞或微粒。目前大多数系统获取大量图像用于训练深度人工神经网络。然而，这种方法增加了最终设备的延迟和功耗。在这项研究中，我们将基于事件的相机与自由空间光学设置相结合，以获取每个通过微流控通道的微粒产生的尖峰。训练了一个尖峰神经网络，结果得到基于完全事件的分类流程的97.7％平均训练准确度和93.5％平均测试准确度。