Verzelli Pietro, Sacerdote Laura
Università della Svizzera italiana, Lugano, Switzerland.
Università degli studi di Torino, Turin, Italy.
Biosystems. 2019 Oct;184:104014. doi: 10.1016/j.biosystems.2019.104014. Epub 2019 Aug 8.
Despite the progresses of statistical and machine learning techniques, simultaneous recordings from many neurons hide important information and the connections characterizing the network remain generally undiscovered. Discerning the presence of direct links between neurons from data is still a not completely solved problem. We propose the use of copulas, to enlarge the number of tools for detecting the network structure, pursuing on a research direction we started in Sacerdote et al. (2012). Here, our aim is to distinguish different types of connections on a very simple network. Our proposal consists in choosing suitable random intervals in pairs of spike trains determining the shapes of their copulas. We show that this approach allows to detect different types of dependencies. We illustrate the features of the proposed method on synthetic data from suitably connected networks of two or three formal neurons directly connected or influenced by the surrounding network. We show how a smart choice of pairs of random times together with the use of empirical copulas allows to discern between direct and indirect interactions.
尽管统计和机器学习技术取得了进展，但从多个神经元同时记录的数据隐藏了重要信息，且表征网络的连接通常仍未被发现。从数据中辨别神经元之间直接连接的存在仍然是一个尚未完全解决的问题。我们建议使用copulas函数，以增加检测网络结构的工具数量，沿着我们在Sacerdote等人（2012年）开始的研究方向继续推进。在这里，我们的目标是在一个非常简单的网络上区分不同类型的连接。我们的建议是在成对的尖峰序列中选择合适的随机区间，以确定它们的copulas函数形状。我们表明，这种方法能够检测不同类型的依赖性。我们用来自由两三个直接连接或受周围网络影响的形式神经元组成的适当连接网络的合成数据说明了所提出方法的特征。我们展示了如何通过明智地选择随机时间对并使用经验copulas函数来区分直接和间接相互作用。
