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在社交互动中对小鼠发声进行高精度的空间定位。

High-precision spatial localization of mouse vocalizations during social interaction.

机构信息

Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Department of Mathematics, Institute for Mathematics, Astrophysics and Particle Physics, Radboud University, Nijmegen, The Netherlands.

出版信息

Sci Rep. 2017 Jun 7;7(1):3017. doi: 10.1038/s41598-017-02954-z.

Abstract

Mice display a wide repertoire of vocalizations that varies with age, sex, and context. Especially during courtship, mice emit ultrasonic vocalizations (USVs) of high complexity, whose detailed structure is poorly understood. As animals of both sexes vocalize, the study of social vocalizations requires attributing single USVs to individuals. The state-of-the-art in sound localization for USVs allows spatial localization at centimeter resolution, however, animals interact at closer ranges, involving tactile, snout-snout exploration. Hence, improved algorithms are required to reliably assign USVs. We develop multiple solutions to USV localization, and derive an analytical solution for arbitrary vertical microphone positions. The algorithms are compared on wideband acoustic noise and single mouse vocalizations, and applied to social interactions with optically tracked mouse positions. A novel, (frequency) envelope weighted generalised cross-correlation outperforms classical cross-correlation techniques. It achieves a median error of ~1.4 mm for noise and ~4-8.5 mm for vocalizations. Using this algorithms in combination with a level criterion, we can improve the assignment for interacting mice. We report significant differences in mean USV properties between CBA mice of different sexes during social interaction. Hence, the improved USV attribution to individuals lays the basis for a deeper understanding of social vocalizations, in particular sequences of USVs.

摘要

老鼠表现出广泛的发声行为,这些行为随着年龄、性别和环境而变化。特别是在求偶期间,老鼠会发出高度复杂的超声发声(USV),但其详细结构尚不清楚。由于雌雄动物都会发声,因此研究社交发声需要将单个 USV 归因于个体。目前用于 USV 的声音定位技术可以在厘米级分辨率下进行空间定位,然而,动物在更接近的范围内相互作用,涉及到触觉、口鼻接触探索。因此,需要改进算法来可靠地分配 USV。我们开发了多种 USV 定位解决方案,并为任意垂直麦克风位置推导出了一个解析解。在宽带噪声和单个老鼠发声上对算法进行了比较,并将其应用于具有光学跟踪老鼠位置的社交互动。一种新颖的(频率)包络加权广义互相关优于经典互相关技术。它在噪声情况下的中位数误差约为 1.4 毫米,在发声情况下的中位数误差约为 4-8.5 毫米。使用这种算法结合电平标准,我们可以改进对相互作用的老鼠的分配。我们报告了在社交互动期间,不同性别的 CBA 老鼠的 USV 属性存在显著差异。因此,对个体的改进的 USV 归因是深入理解社交发声的基础,特别是 USV 序列。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60f6/5462771/d4636d9c3d01/41598_2017_2954_Fig1_HTML.jpg

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