Center for Advancing Electronics Dresden, TU Dresden, Mommsenstrasse 15, 01069, Dresden, Germany.
Department of Physics and Astronomy, Ohio University, Athens, Ohio, 45701, USA.
Sci Rep. 2017 Jun 21;7(1):3956. doi: 10.1038/s41598-017-04193-8.
We study the stochastic dynamics of strongly-coupled excitable elements on a tree network. The peripheral nodes receive independent random inputs which may induce large spiking events propagating through the branches of the tree and leading to global coherent oscillations in the network. This scenario may be relevant to action potential generation in certain sensory neurons, which possess myelinated distal dendritic tree-like arbors with excitable nodes of Ranvier at peripheral and branching nodes and exhibit noisy periodic sequences of action potentials. We focus on the spiking statistics of the central node, which fires in response to a noisy input at peripheral nodes. We show that, in the strong coupling regime, relevant to myelinated dendritic trees, the spike train statistics can be predicted from an isolated excitable element with rescaled parameters according to the network topology. Furthermore, we show that by varying the network topology the spike train statistics of the central node can be tuned to have a certain firing rate and variability, or to allow for an optimal discrimination of inputs applied at the peripheral nodes.
我们研究了树状网络上强耦合激发元件的随机动力学。外围节点接收独立的随机输入，这些输入可能会引发通过树分支传播的大尖峰事件，并导致网络中的全局相干振荡。这种情况可能与某些感觉神经元中的动作电位产生有关，这些神经元具有带有髓鞘的远端树突状分支树，在周围和分支节点处具有可兴奋的郎飞氏结，并表现出噪声周期性的动作电位序列。我们专注于中央节点的尖峰统计，该节点响应外围节点的噪声输入而触发。我们表明，在与有髓树突相关的强耦合状态下，根据网络拓扑结构，可以根据孤立的可兴奋元件的参数进行缩放来预测尖峰序列统计。此外，我们表明通过改变网络拓扑结构，可以调整中央节点的尖峰序列统计，以具有特定的发放率和可变性，或者允许对外围节点施加的输入进行最佳区分。
