Department of Mathematics "G. Peano", University of Turin, Via Carlo Alberto 10, Turin, Italy.
Brain Res. 2012 Jan 24;1434:243-56. doi: 10.1016/j.brainres.2011.08.064. Epub 2011 Sep 12.
The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky Integrate and Fire models. The method discerns dependencies determined by the surrounding network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation. This article is part of a Special Issue entitled "Neural Coding".
神经元的动态受到与其所在网络的连接的影响。由于神经元之间的相互作用，记录的尖峰序列表现出模式。然而，网络的结构是未知的。一项具有挑战性的任务是从同时记录的尖峰序列的分析中对其进行研究。我们开发了一种基于 copulas 的非参数方法，并根据不同的双变量 Leaky Integrate and Fire 模型将其应用于模拟数据。该方法可以区分由周围网络决定的依赖关系和由两个神经元之间的直接相互作用决定的依赖关系。此外，该方法还可以识别尖峰传播中的延迟。本文是特刊“神经编码”的一部分。
