How spiking activity reverberates through neuronal networks, how evoked and spontaneous activity interacts and blends, and how the combined activities represent external stimulation are pivotal questions in neuroscience. We simulated minimal models of unstructured spiking networks in silico, asking whether and how gentle external stimulation might be subsequently reflected in spontaneous activity fluctuations. Consistent with earlier findings in silico and in vitro, we observe a privileged subpopulation of 'pioneer neurons' that, by their firing order, reliably encode previous external stimulation. We also confirm that pioneer neurons are 'sensitive' in that they are recruited by small fluctuations of population activity. We show that order-based representations rely on a 'chain' of pioneer neurons with different degrees of sensitivity and thus constitute an emergent property of collective dynamics. The forming of such representations is greatly favoured by a broadly heterogeneous connection topology-a broad 'middle class' in degree of connectedness. In conclusion, we offer a minimal model for the representational role of pioneer neurons, as observed experimentally in vitro. In addition, we show that broadly heterogeneous connectivity enhances the representational capacity of unstructured networks.

中文翻译：

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非结构化网络拓扑通过特权神经元产生基于顺序的表示。

尖峰活动如何通过神经元网络产生回响，诱发和自发活动如何相互作用和混合，以及组合活动如何代表外部刺激，这些都是神经科学中的关键问题。我们在计算机中模拟了非结构化尖峰网络的最小模型，询问温和的外部刺激是否以及如何随后反映在自发活动波动中。与早期的计算机和体外研究结果一致，我们观察到“先锋神经元”的特权亚群，它们通过其放电顺序可靠地编码先前的外部刺激。我们还证实先驱神经元是“敏感的”，因为它们是通过群体活动的小波动而被招募的。我们表明，基于顺序的表征依赖于具有不同敏感程度的先锋神经元“链”，因此构成了集体动力学的新兴属性。这种表示的形成受到广泛异构连接拓扑的极大支持——连接程度的广泛“中产阶级”。总之，我们为先锋神经元的代表性作用提供了一个最小模型，如体外实验所观察到的。此外，我们表明广泛的异构连接增强了非结构化网络的表示能力。