Abstract
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.
Highlights
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
We developed an analysis toolkit with a parsing algorithm to detect in the audio files the exact start and end times of each USV and each sequence of USVs
We showed that analyzing the syntax imposed by a labeling algorithm could provide a good evaluation measure for its performance
Summary
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. The periods of silence between USVs (inter-syllable intervals, ISIs) follow a typical distribution with several distinct peaks, suggesting a prototypical process of producing these sounds. While the process of parsing the sound into individual USVs is primarily one of overcoming technical obstacles, the classification of the individual USVs into syllable classes based on their acoustic features presents complex and fundamental challenges
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