The ventral tegmental area (VTA) is a heterogeneous brain region, containing different neuronal populations. During in vivo recordings, electrophysiological characteristics are classically used to distinguish the different populations. However, the VTA is also considered as a region harboring neurons with heterogeneous properties. In the present study, we aimed to classify VTA neurons using in silico approaches, in an attempt to determine if homogeneous populations could be extracted. Thus, we recorded 291 VTA neurons during in vivo extracellular recordings in anesthetized rats. Initially, 22 neurons with high firing rates (>10 Hz) and short-lasting action potentials (AP) were considered as a separate subpopulation, in light of previous studies. To segregate the remaining 269 neurons, presumably dopaminergic (DA), we performed in silico analyses, using a combination of different electrophysiological parameters. These parameters included: (1) firing rate; (2) firing rate coefficient of variation (CV); (3) percentage of spikes in a burst; (4) AP duration; (5) Δt1 duration (i.e., time from initiation of depolarization until end of repolarization); and (6) presence of a notched AP waveform. Unsupervised hierarchical clustering revealed two neuronal populations that differed in their bursting activities. The largest population presented low bursting activities (<17.5% of total spikes in burst), while the remaining neurons presented higher bursting activities (>17.5%). Within non-high-firing neurons, a large heterogeneity was noted concerning AP characteristics. In conclusion, this analysis based on conventional electrophysiological criteria clustered two subpopulations of putative DA VTA neurons that are distinguishable by their firing patterns (firing rates and bursting activities) but not their AP properties.

中文翻译：

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基于细胞外电生理特性的大鼠腹侧被盖区神经元群的计算机分层聚类。

腹侧被盖区 (VTA) 是一个异质的大脑区域，包含不同的神经元群。在体内记录期间，电生理特征通常用于区分不同的人群。然而，VTA 也被认为是一个包含具有异质特性的神经元的区域。在本研究中，我们旨在使用计算机方法对 VTA 神经元进行分类，以确定是否可以提取同质种群。因此，我们在麻醉大鼠的体内细胞外记录期间记录了 291 个 VTA 神经元。根据之前的研究，最初将 22 个具有高放电率 (>10 Hz) 和短持续动作电位 (AP) 的神经元视为一个单独的亚群。为了分离剩余的 269 个神经元，大概是多巴胺能 (DA)，我们使用不同电生理参数的组合进行了计算机分析。这些参数包括： (1) 射速；(2)射速变异系数(CV)；(3) 突发中尖峰的百分比；(4) AP持续时间；(5) Δt1 持续时间（即从除极开始到复极结束的时间）；(6) 存在缺口 AP 波形。无监督层次聚类揭示了两个神经元群，它们的爆发活动不同。最大的群体呈现低爆发活动（爆发总峰值的 <17.5%），而其余神经元呈现更高的爆发活动（>17.5%）。在非高激发神经元中，注意到 AP 特征存在很大的异质性。综上所述，