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Neural Circuits: Automated in vivo patch-clamp evaluation of extracellular multielectrode array spike recording capability

机译:神经回路:自动化的膜片钳对细胞外多电极阵列尖峰记录能力的评估

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摘要

Much innovation is currently aimed at improving the number, density, and geometry of electrodes on extracellular multielectrode arrays for in vivo recording of neural activity in the mammalian brain. To choose a multielectrode array configuration for a given neuroscience purpose, or to reveal design principles of future multielectrode arrays, it would be useful to have a systematic way of evaluating the spike recording capability of such arrays. We describe an automated system that performs robotic patch-clamp recording of a neuron being simultaneously recorded via an extracellular multielectrode array. By recording a patch-clamp data set from a neuron while acquiring extracellular recordings from the same neuron, we can evaluate how well the extracellular multielectrode array captures the spiking information from that neuron. To demonstrate the utility of our system, we show that it can provide data from the mammalian cortex to evaluate how the spike sorting performance of a close-packed extracellular multielectrode array is affected by bursting, which alters the shape and amplitude of spikes in a train. We also introduce an algorithmic framework to help evaluate how the number of electrodes in a multielectrode array affects spike sorting, examining how adding more electrodes yields data that can be spike sorted more easily. Our automated methodology may thus help with the evaluation of new electrode designs and configurations, providing empirical guidance on the kinds of electrodes that will be optimal for different brain regions, cell types, and species, for improving the accuracy of spike sorting.>NEW & NOTEWORTHY We present an automated strategy for evaluating the spike recording performance of an extracellular multielectrode array, by enabling simultaneous recording of a neuron with both such an array and with patch clamp. We use our robot and accompanying algorithms to evaluate the performance of multielectrode arrays on supporting spike sorting.
机译:当前,许多创新旨在改善细胞外多电极阵列上电极的数量,密度和几何形状,以在体内记录哺乳动物大脑中的神经活动。为了为给定的神经科学目的选择多电极阵列配置,或者揭示未来多电极阵列的设计原理,采用一种系统的方法来评估此类阵列的尖峰记录能力将非常有用。我们描述了一个自动化的系统,该系统执行通过细胞外多电极阵列同时记录的神经元的机器人膜片钳记录。通过记录来自神经元的膜片钳数据集,同时从同一神经元获取细胞外记录,我们可以评估细胞外多电极阵列捕获来自该神经元的尖峰信息的能力。为了证明我们系统的实用性,我们表明它可以提供来自哺乳动物皮质的数据,以评估密排细胞外多电极阵列的突波分选性能如何受到爆破的影响,从而改变火车中突波的形状和振幅。我们还介绍了一种算法框架,以帮助评估多电极阵列中的电极数量如何影响尖峰排序,研究添加更多电极如何产生可以更容易地尖峰排序的数据。因此,我们的自动化方法可以帮助评估新的电极设计和配置,为不同脑区域,细胞类型和种类的最佳电极种类提供经验指导,以提高尖峰分选的准确性。> NEW&NOWORTHY 我们提出了一种自动化策略,用于评估细胞外多电极阵列的峰值记录性能,方法是同时使用这种阵列和膜片钳同时记录神经元。我们使用机器人和随附的算法来评估多电极阵列在支持尖峰分选方面的性能。

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