The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Commun Biol. 2020 Jun 26;3(1):333. doi: 10.1038/s42003-020-1053-7.
Mice emit sequences of ultrasonic vocalizations (USVs) but little is known about the rules governing their temporal order and no consensus exists on the classification of USVs into syllables. To address these questions, we recorded USVs during male-female courtship and found a significant temporal structure. We labeled USVs using three popular algorithms and found that there was no one-to-one relationships between their labels. As label assignment affects the high order temporal structure, we developed the Syntax Information Score (based on information theory) to rank labeling algorithms based on how well they predict the next syllable in a sequence. Finally, we derived a novel algorithm (Syntax Information Maximization) that utilizes sequence statistics to improve the clustering of individual USVs with respect to the underlying sequence structure. Improvement in USV classification is crucial for understanding neural control of vocalization. We demonstrate that USV syntax holds valuable information towards achieving this goal.
老鼠会发出一连串的超声波叫声(USVs),但人们对控制这些叫声时间顺序的规则知之甚少,也没有就将 USVs 分类为音节达成共识。为了解决这些问题,我们在雄性和雌性求偶期间记录了 USVs,并发现了它们具有显著的时间结构。我们使用三种流行的算法对 USVs 进行了标记,发现它们的标记之间没有一一对应的关系。由于标签的分配会影响到高阶时间结构,因此我们开发了语法信息评分(基于信息论),根据算法对序列中下一个音节的预测能力,对标记算法进行排名。最后,我们提出了一种新的算法(语法信息最大化),它利用序列统计信息来改善个体 USV 相对于潜在序列结构的聚类。改善 USV 的分类对于理解神经控制发声至关重要。我们证明了 USV 语法对于实现这一目标具有重要价值。