An Information-Theoretic Framework to Measure the Dynamic Interaction Between Neural Spike Trains
An Information-Theoretic Framework to Measure the Dynamic Interaction Between Neural Spike Trains
复制标题
测量神经尖峰序列之间动态相互作用的信息论框架
DOI：
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发表时间：
2020
影响因子：
4.6
通讯作者：
L. Faes
中科院分区：
文献类型：
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作者：
G. Mijatović;Y. Antonacci;T. Lončar;L. Minati;L. Faes
<Italic>目标：</Italic>虽然了解来自多个神经元单元的尖峰列车同时记录之间的相互作用模式是神经科学的关键主题，但现有方法要么不考虑峰值火车的固有的点过程，要么是基于峰值火车的固有方法或参数假设。这项工作介绍了一个信息理论框架，用于对尖峰列车之间的无向（对称）和定向（格兰杰 - 库）相互作用的无模型，连续的时间估计。 <Italic>方法：</Italic>框架计算两个点过程的相互信息率（miR）和转移熵率（TER）<inline-formula> <tex-math-math notegy =“ latex”> $ x $ < /tex-math> </inline-formula>和<inline-formula> <tex-math notegement =“ latex”> $ y $ </tex-math> </inline-formula>，表明该表明<inline-formula> <tex-math notege =“ latex”> $ x $ </tex-math> </inline-formula>和<inline-formula> <tex-math notegement =“ latex”> $之间的miR y $ </tex-math> </inline-formula>可以分解为沿指示<inline-formula> <tex-math notegement =“ latex”> $ x的总和
ightarrow y $ </tex-math> </inline-formula>和<inline-formula> <tex-math notegy =“ latex”> $ y
ightarrow x $ </tex-math> </inline-formula>。我们提出理论表达式，并引入策略，以通过最近的邻居统计数据有效地估算这两种措施。 <Italic>结果：</Italic>使用独立和耦合点过程的模拟，我们显示了MiR和TER的准确性，即使对于弱耦合和短暂的实现，也可以评估相互作用，并证明了连续时间估计的优势 - 时间方法。我们还将MIR和TER应用于现实世界数据，特别是来自自发生长的皮质神经元培养物的录音。使用此数据集，我们演示了MIR和TER描述记录单元之间的功能网络如何在神经元文化的成熟过程中出现的能力。 <Italic>结论和意义：</Italic>所提出的框架提供了与以前的离散时间或参数方法相比，具有更大的效率和灵活性来评估无向和定向的尖峰火车相互作用的原则措施在神经科学和许多其他领域。
<italic>Objective:</italic> While understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience, existing methods either do not consider the inherent point-process nature of spike trains or are based on parametric assumptions. This work presents an information-theoretic framework for the model-free, continuous-time estimation of both undirected (symmetric) and directed (Granger-causal) interactions between spike trains. <italic>Methods:</italic> The framework computes the mutual information rate (MIR) and the transfer entropy rate (TER) for two point processes <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$Y$</tex-math></inline-formula>, showing that the MIR between <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$Y$</tex-math></inline-formula> can be decomposed as the sum of the TER along the directions <inline-formula><tex-math notation="LaTeX">$X
ightarrow Y$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$Y
ightarrow X$</tex-math></inline-formula>. We present theoretical expressions and introduce strategies to estimate efficiently the two measures through nearest neighbor statistics. <italic>Results:</italic> Using simulations of independent and coupled point processes, we show the accuracy of MIR and TER to assess interactions even for weakly coupled and short realizations, and demonstrate the superiority of continuous-time estimation over the standard discrete-time approach. We also apply the MIR and TER to real-world data, specifically, recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons. Using this dataset, we demonstrate the ability of MIR and TER to describe how the functional networks between recording units emerge over the course of the maturation of the neuronal cultures. <italic>Conclusion and Significance:</italic> the proposed framework provides principled measures to assess undirected and directed spike train interactions with more efficiency and flexibility than previous discrete-time or parametric approaches, opening new perspectives for the analysis of point-process data in neuroscience and many other fields.