Jurjuţ Ovidiu F, Gheorghiu Medorian, Singer Wolf, Nikolić Danko, Mureşan Raul C
Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania.
Technical University of Cluj-Napoca, Cluj-Napoca, Romania.
Front Syst Neurosci. 2019 May 17;13:21. doi: 10.3389/fnsys.2019.00021. eCollection 2019.
Responses of neuronal populations play an important role in the encoding of stimulus related information. However, the inherent multidimensionality required to describe population activity has imposed significant challenges and has limited the applicability of classical spike train analysis techniques. Here, we show that these limitations can be overcome. We first quantify the collective activity of neurons as multidimensional vectors (patterns). Then we characterize the behavior of these patterns by applying classical spike train analysis techniques: peri-stimulus time histograms, tuning curves and auto- and cross-correlation histograms. We find that patterns can exhibit a broad spectrum of properties, some resembling and others substantially differing from those of their component neurons. We show that in some cases pattern behavior cannot be intuitively inferred from the activity of component neurons. Importantly, silent neurons play a critical role in shaping pattern expression. By correlating pattern timing with local-field potentials, we show that the method can reveal fine temporal coordination of cortical circuits at the mesoscale. Because of its simplicity and reliance on well understood classical analysis methods the proposed approach is valuable for the study of neuronal population dynamics.
神经元群体的反应在刺激相关信息的编码中起着重要作用。然而，描述群体活动所需的内在多维性带来了重大挑战，并限制了经典脉冲序列分析技术的适用性。在这里，我们表明这些限制是可以克服的。我们首先将神经元的集体活动量化为多维向量（模式）。然后，我们通过应用经典脉冲序列分析技术来表征这些模式的行为：刺激周围时间直方图、调谐曲线以及自相关和互相关直方图。我们发现模式可以展现出广泛的特性，有些与它们的组成神经元的特性相似，而有些则有很大不同。我们表明，在某些情况下，无法从组成神经元的活动直观推断出模式行为。重要的是，沉默神经元在塑造模式表达中起着关键作用。通过将模式时间与局部场电位相关联，我们表明该方法可以揭示中尺度下皮质回路的精细时间协调。由于其简单性以及对广为人知的经典分析方法的依赖，所提出的方法对于研究神经元群体动力学具有重要价值。
