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Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons

机译:最短的回路是电耦合轴突随机网络中的起搏器

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摘要

High-frequency oscillations (HFOs) are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bi-directional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100–200 Hz) are predicted to be caused by spontaneously spiking axons in a network with strong (high conductance) gap junctions. Type II oscillations (200–300 Hz) require no spontaneous spiking and relatively weak (low-conductance) gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network’s loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate. The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.
机译:高频振荡(HFO)是大脑活动在健康和疾病中的重要组成部分。但是,它们的起源仍然晦涩而有争议。一种可能的机制取决于电耦合主细胞轴突的稀疏分布的间隙连接的存在。电耦合轴突丛被建模为随机网络,其节点之间具有双向连接。在某些条件下,网络可以证明两种振荡活动之一。预计I型振荡(100–200)Hz)是由具有强(高电导)间隙连接的网络中的自发尖峰轴突引起的。 II型振荡(200–300 Hz)不需要自发的尖峰和相对较弱的(低电导)间隙结,在这种情况下会发生尖峰传播故障。 II型振荡是可重入且自持的。在这里,我们检查什么因素决定了II型振荡的频率。通过仿真我们可以看出,环路长度的分布是确定II型网络振荡频率的关键因素。我们首先使用解剖重建的CA1锥体神经元模型分析两个电耦合细胞之间的突波破坏。然后,通过具有随机网络连通性的元胞自动机模型研究网络振荡,在该模型中我们控制环路统计信息。我们证明了可以从网络的环路统计数据中预测振荡周期。起搏器可以绕过的最短循环是最可能的起搏器候选者。作为起搏器的一个回路的原理非常引人注目,因为随机网络包含大量并置和叠加的回路,并且它们的数量会随着网络规模的增长而迅速增长。该原理使我们能够预测网络连接引起的振荡频率,反之亦然。我们最终提出I型振荡可能对应于波纹,而II型振荡则对应于所谓的快速波动。

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