Allen Brian D, Moore-Kochlacs Caroline, Bernstein Jacob G, Kinney Justin P, Scholvin Jorg, Seoane Luís F, Chronopoulos Chris, Lamantia Charlie, Kodandaramaiah Suhasa B, Tegmark Max, Boyden Edward S
Media Lab and McGovern Institute for Brain Research, Departments of Biological Engineering and Brain and Cognitive Sciences, and Koch Institute, Massachusetts Institute of Technology , Cambridge, Massachusetts.
Department of Neuroscience, Boston University , Boston, Massachusetts.
J Neurophysiol. 2018 Nov 1;120(5):2182-2200. doi: 10.1152/jn.00650.2017. Epub 2018 Jul 11.
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.
目前，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，用于在哺乳动物大脑中进行神经活动的体内记录。为了针对特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则，拥有一种系统的方法来评估此类阵列的尖峰记录能力将是很有用的。我们描述了一种自动化系统，该系统通过细胞外多电极阵列对同时记录的神经元进行机器人膜片钳记录。通过在从同一神经元获取细胞外记录的同时记录该神经元的膜片钳数据集，我们可以评估细胞外多电极阵列从该神经元捕获尖峰信息的能力。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层的数据，以评估紧密排列的细胞外多电极阵列的尖峰分类性能如何受到爆发活动的影响，爆发活动会改变一串尖峰的形状和幅度。我们还引入了一个算法框架，以帮助评估多电极阵列中电极数量如何影响尖峰分类，研究增加更多电极如何产生更容易进行尖峰分类的数据。因此，我们的自动化方法可能有助于评估新的电极设计和配置，为不同脑区、细胞类型和物种的最佳电极类型提供经验指导，以提高尖峰分类的准确性。新内容及值得注意之处我们提出了一种自动化策略，通过同时使用细胞外多电极阵列和膜片钳记录一个神经元来评估细胞外多电极阵列的尖峰记录性能。我们使用我们的机器人和配套算法来评估多电极阵列在支持尖峰分类方面的性能。
