Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as allow novel studies otherwise not possible due to prohibitively low throughput. Real-time feedback is especially important in studies that involve optogenetic cell type identification by enabling a systematic search for neurons of interest. However, such tools are scarce and limited to costly commercial systems with high degree of specialization, which hitherto prevented wide-ranging benefits for the community. To address this, we present an open-source tool that enables online feedback during electrophysiology experiments and provides a Python interface for the widely used Open Ephys open source data acquisition system. Specifically, our software allows flexible online visualization of spike alignment to external events, called the online peri-event time histogram (OPETH). These external events, conveyed by digital logic signals, may indicate photostimulation time stamps for in vivo optogenetic cell type identification or the times of behaviorally relevant events during in vivo behavioral neurophysiology experiments. Therefore, OPETH allows real-time identification of genetically defined neuron types or behaviorally responsive populations. By allowing “hunting” for neurons of interest, OPETH significantly reduces experiment time and thus increases the efficiency of experiments that combine in vivo electrophysiology with behavior or optogenetic tagging of neurons.

中文翻译：

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OPETH：基于开放 Ephys 的实时围事件时间直方图开源解决方案


单细胞电生理学仍然是系统神经科学最广泛使用的方法之一。实验者在电生理学记录过程中做出的决定很大程度上决定了记录质量、项目的持续时间和所收集数据的价值。因此，帮助这些决策的在线反馈可以降低金钱和时间投资，并大大加快项目速度，并允许进行新颖的研究，否则由于吞吐量极低而不可能进行。实时反馈在涉及通过系统搜索感兴趣的神经元进行光遗传学细胞类型识别的研究中尤其重要。然而，此类工具非常稀缺，并且仅限于专业化程度高、成本高昂的商业系统，迄今为止，这阻碍了社区的广泛受益。为了解决这个问题，我们提出了一种开源工具，可以在电生理学实验期间实现在线反馈，并为广泛使用的 Open Ephys 开源数据采集系统提供 Python 接口。具体来说，我们的软件允许灵活地在线可视化尖峰与外部事件的对齐，称为在线事件周围时间直方图（OPETH）。这些由数字逻辑信号传递的外部事件可以指示用于体内光遗传学细胞类型识别的光刺激时间戳或体内行为神经生理学实验期间行为相关事件的时间。因此，OPETH 可以实时识别基因定义的神经元类型或行为反应群体。通过允许“狩猎”感兴趣的神经元，OPETH 显着减少了实验时间，从而提高了将体内电生理学与神经元行为或光遗传学标记相结合的实验效率。