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用于在亚细胞、细胞和网络水平研究神经元的高分辨率互补金属氧化物半导体微电极阵列平台。

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.

作者信息

Müller Jan, Ballini Marco, Livi Paolo, Chen Yihui, Radivojevic Milos, Shadmani Amir, Viswam Vijay, Jones Ian L, Fiscella Michele, Diggelmann Roland, Stettler Alexander, Frey Urs, Bakkum Douglas J, Hierlemann Andreas

机构信息

ETH Zurich, Bio Engineering Laboratory, Department of Biosystems Science and Engineering, Mattenstrasse 26, CH-4058 Basel, Switzerland.

出版信息

Lab Chip. 2015 Jul 7;15(13):2767-80. doi: 10.1039/c5lc00133a. Epub 2015 May 14.

Abstract

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

摘要

对神经网络的信息处理和学习特性进行研究,若能同时并行获取此类网络中大部分神经元的活动情况,将会受益匪浅。在此,我们展示一种基于互补金属氧化物半导体(CMOS)的设备,它能够同时记录体外神经网络中一千多个细胞的电活动。该设备具备足够高的时空分辨率,能同时在亚细胞、细胞和网络层面获取神经元制剂的信息。其关键特性是一个快速可重构的阵列,由26400个微电极组成,这些微电极以低间距(17.5微米)排列在一个较大的整体传感区域(3.85×2.10平方毫米)内。电极的任意子集可同时连接到1024个低噪声读出通道以及32个刺激单元。每个电极或电极子集可用于电刺激或记录阵列上几乎任何神经元的信号。我们展示了该设备在各种不同实验范式中的适用性和潜力:对整个神经元网络进行大规模记录以及对单个神经元的轴突特性进行研究。

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本文引用的文献

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Spatial information in large-scale neural recordings.大规模神经记录中的空间信息。
Front Comput Neurosci. 2015 Jan 21;8:172. doi: 10.3389/fncom.2014.00172. eCollection 2014.
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Localising and classifying neurons from high density MEA recordings.从高密度微电极阵列记录中定位和分类神经元。
J Neurosci Methods. 2014 Aug 15;233:115-28. doi: 10.1016/j.jneumeth.2014.05.037. Epub 2014 Jun 19.
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