Psychological and Brain Sciences, Boston University, Boston, United States.
Bernstein Center for Computational Neuroscience, Bernstein Center for Computational Neuroscience, Berlin, Germany.
Elife. 2021 Aug 6;10:e67490. doi: 10.7554/eLife.67490.
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method () that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using , we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
皮质电路被认为包含大量协调产生行为的细胞类型。目前的活体方法依赖于对细胞外波形特定特征的聚类来识别可能的细胞类型，但这些方法只能捕捉到一小部分变化。在这里，我们开发了一种新的方法 ()，它结合了非线性降维和图聚类来识别可能的细胞类型。我们将 应用于猕猴执行决策任务时记录的背侧运动前皮层的细胞外波形。使用 ，我们稳健地建立了八个波形聚类，并表明这些聚类再现了先前确定的窄峰和宽峰类型，同时揭示了这些亚型内以前未知的多样性。八个聚类表现出不同的层分布、特征性的发放率模式和与决策相关的动力学。当使用基于特征的方法时，这种洞察力就不那么强了。因此，它提供了对皮质电路中细胞类型动态的更细致的理解。
