瞬时神经事件因果影响的信息论测度
Information Theoretic Measures of Causal Influences during Transient Neural Events
摘要：
瞬变现象在多尺度协调大脑活动中起着关键作用，然而，其潜在机制在很大程度上仍然未知。因此，神经数据科学的一个关键挑战是描述这些事件中网络的交互作用。使用结构因果模型及其图形表示的形式主义，我们调查的理论和经验性质的信息理论为基础的因果强度措施的背景下，反复自发的瞬态事件。在说明了转移熵和动态因果强度在这种情况下的局限性之后，本文引入了一种新的测度--相对动态因果强度，并对其优势提供了理论和实证支持。这些方法被应用到模拟和实验记录的神经时间序列，并提供与我们目前的理解的基本脑回路的协议的结果。
英文摘要：
Transient phenomena play a key role in coordinating brain activity at multiple scales, however,their underlying mechanisms remain largely unknown. A key challenge for neural data science is thus to characterize the network interactions at play during these events. Using the formalism of Structural Causal Models and their graphical representation, we investigate the theoretical and empirical properties of Information Theory based causal strength measures in the context of recurring spontaneous transient events. After showing the limitations of Transfer Entropy and Dynamic Causal Strength in such a setting, we introduce a novel measure, relative Dynamic Causal Strength, and provide theoretical and empirical support for its benefits. These methods are applied to simulated and experimentally recorded neural time series, and provide results in agreement with our current understanding of the underlying brain circuits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
