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High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

机译:用于闭环实验的高密度微电极阵列记录和实时峰值分选:研究神经可塑性的新兴技术

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摘要

Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.
机译:理解神经网络的可塑性是理解其发展和功能的关键。研究神经可塑性的强大技术包括例如通过同时胞内记录和刺激多个神经元来记录和控制突触前和突触后神经活动。然而,细胞内记录是一项苛刻的技术,并且其局限性在于,只能同时刺激和记录少量的神经元。细胞外技术提供了相对容易地同时从大量神经元进行记录的可能性,但付出了更多的努力从记录的混合物中分选出单个神经元活动,这是一个耗时且容易出错的步骤,称为尖峰分选。在此微型审查中,我们描述了两个独立领域中的最新技术发展,即基于CMOS的高密度微电极阵列,该阵列还允许神经元的细胞外刺激和实时尖峰分选。我们认为,这些技术结合使用将为体外研究由数千个神经元组成的神经网络中的可塑性提供强大的工具。

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