树型可激元网络中涌现的随机振荡和信号检测
Emergent stochastic oscillations and signal detection in tree networks of excitable elements
摘要：
研究了树状网络上强耦合可激元的随机动力学。外围节点接收独立的随机输入，这可能引起通过树的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的动作电位产生有关，这些感觉神经元具有有髓远端树突状树状结构，在外周和分支节点处具有可兴奋的Ranvier节点，并表现出嘈杂的动作电位周期序列。我们专注于尖峰统计的中心节点，火灾响应于在外围节点的噪声输入。我们发现，在强耦合制度，相关的有髓树突树，穗训练统计可以预测从一个孤立的可兴奋的元素与重新调整的参数，根据网络拓扑结构。此外，我们表明，通过改变网络拓扑结构的尖峰训练统计的中心节点可以调整有一定的发射率和变异性，或允许在外围节点应用的输入的最佳歧视。
英文摘要：
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.
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