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Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states

机译:突触动力学和神经网络连通性反映在Up状态的时间分布中

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摘要

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.
机译:通过突触动力学连接的神经元网络的动力学可以维持长时间的去极化,这种去极化可以持续数百毫秒,例如在睡眠或麻醉期间记录的Up状态。然而,驱动这些时期的潜在机制仍不清楚。我们在平均场模型中显示,在皮层向上状态的神经元膜电位的停留时间未遵循泊松定律,但出现了多个峰值。此外,本建模方法允许从时间分布直方图中提取有关神经网络连通性的一些信息。基于突触抑制模型,我们发现可以在膜片钳记录的直方图中观察到的这些峰不是电生理测量的伪影,而是网络动力学的固有属性。对方程式的分析表明,稳定焦点位于不稳定的极限循环附近,从而界定了定义为Up状态的区域。该模型进一步表明,在Up状态时间分布中观察到的峰值是由于在从吸引盆地逸出之前绕着焦点缠绕。最后,我们使用细胞内膜电位的体内记录,并从峰分布中恢复有关网络连接性的一些信息。我们得出结论,有可能从神经元膜电压在Up状态花费的时间分布恢复网络连通性。

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