CBA/CaJ mouse ultrasonic vocalizations depend on prior social experience

Burke, Kali; Screven, Laurel A.; Dent, Micheal L.

doi:10.1371/journal.pone.0197774

Cited by 32 publications

(28 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies on other data sets have led to mixed results, with some studies finding differences in USV properties [33], others finding no structural differences but only on the level of vocalization rates [16,34]. However, to our knowledge, no study was so far able to detect differences on the level of single vocalizations, as in the present analysis.…”

Section: Comparison With Previous Work On Sex Differences In Vocalizacontrasting

confidence: 67%

“…The present experimental dataset was restricted to only two strains and two types of interaction. It has been shown previously that different strains [20,39] and different social contexts [2,21,33] influence the vocalization behavior of mice. As the present study depended partially on the availability of human-classified USV features, we chose to work with more limited sets here.…”

Section: Generalization To Other Strains Social Interactions and Usvmentioning

confidence: 97%

See 1 more Smart Citation

Classifying sex and strain from mouse ultrasonic vocalizations using deep learning

et al. 2020

View full text Add to dashboard Cite

Vocalizations are widely used for communication between animals. Mice use a large repertoire of ultrasonic vocalizations (USVs) in different social contexts. During social interaction recognizing the partner's sex is important, however, previous research remained inconclusive whether individual USVs contain this information. Using deep neural networks (DNNs) to classify the sex of the emitting mouse from the spectrogram we obtain unprecedented performance (77%, vs. SVM: 56%, Regression: 51%). Performance was even higher (85%) if the DNN could also use each mouse's individual properties during training, which may, however, be of limited practical value. Splitting estimation into two DNNs and using 24 extracted features per USV, spectrogram-to-features and features-to-sex (60%) failed to reach single-step performance. Extending the features by each USVs spectral line, frequency and time marginal in a semi-convolutional DNN resulted in a performance midway (64%). Analyzing the network structure suggests an increase in sparsity of activation and correlation with sex, specifically in the fully-connected layers. A detailed analysis of the USV structure, reveals a subset of male vocalizations characterized by a few acoustic features, while the majority of sex differences appear to rely on a complex combination of many features. The same network architecture was also able to achieve above-chance classification for cortexless mice, which were considered indistinguishable before. In summary, spectrotemporal differences between male and female USVs allow at least their partial classification, which enables sexual recognition between mice and automated attribution of USVs during analysis of social interactions.

show abstract

Section: Comparison With Previous Work On Sex Differences In Vocalizacontrasting

confidence: 67%

Section: Generalization To Other Strains Social Interactions and Usvmentioning

confidence: 97%

Classifying sex and strain from mouse ultrasonic vocalizations using deep learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The stimuli for experiment 2 were pure tones (16 and 64 kHz) of five durations (20, 50, 200, 400, and 800 ms, with 10% rise/fall cosine ramps). The stimuli were chosen to cover peak behavioral sensitivity (Radziwon et al, 2009) and the mouse's vocalization range (Burke, Screven, & Dent, 2018;Portfors, 2007). A random order of testing on these 10 conditions was generated for each subject.…”

Section: Stimulimentioning

confidence: 99%

The effects of age and sex on the detection of pure tones by adult CBA/CaJ mice (Mus musculus)

Kobrina

Dent

2019

J of Neuroscience Research

Self Cite

View full text Add to dashboard Cite

Age‐related hearing loss (ARHL) is a neurodegenerative disorder characterized by a gradual decrease in hearing sensitivity. Previous electrophysiological and behavioral studies have demonstrated that the CBA/CaJ mouse strain is an appropriate model for the late‐onset hearing loss found in humans. However, few studies have characterized hearing in these mice behaviorally using longitudinal methodologies. The goal of this research was to utilize a longitudinal design and operant conditioning procedures with positive reinforcement to construct audiograms and temporal integration functions in aging CBA/CaJ mice. In the first experiment, thresholds were collected for 8, 16, 24, 42, and 64 kHz pure tones in 30 male and 35 female CBA/CaJ mice. Similar to humans, mice had higher thresholds for high frequency tones than for low frequency pure tones across the lifespan. Female mice had better hearing acuity than males after 645 days of age. In the second experiment, temporal integration functions were constructed for 18 male and 18 female mice for 16 and 64 kHz tones varying in duration. Mice showed an increase in thresholds for tones shorter than 200 ms, reaching peak performance at shorter durations than other rodent species. Overall, CBA/CaJ mice experience ARHL for pure tones of different frequencies and durations, making them a good model for studies on hearing loss. These findings highlight the importance of using a wide range of stimuli and a longitudinal design when comparing presbycusis across different species.

show abstract

“…The present experimental dataset was restricted to a single strain in a single type of interaction, an awake male/female mouse with an anesthetized female mouse. It has been shown previously that different strains (Van Segbroeck et al 2017;Sugimoto et al 2011) and different social contexts (Chabout et al 2015;Zala et al 2017;Burke et al 2018) influence the vocalization behavior of mice. As the present study depended partially on the availability of human classified USV features, we chose to work with a more limited set here.…”

Section: Generalization To Other Strains and Social Interactionsmentioning

confidence: 90%

“…Previous studies on other data sets have lead to mixed results, with some studies finding differences in USV properties (Burke et al 2018) , others finding no structural differences but only on the level of vocalization rates (Guo and Holy 2007) . However, to our knowledge, no study was so far able to detect differences on the level of single vocalizations, as in the present analysis.…”

Section: Comparison With Previous Work On Sex Differences In Vocalizamentioning

confidence: 93%

Classification of mouse ultrasonic vocalizations using deep learning

Ivanenko

Watkins

Gerven

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract:Vocalizations are a widespread means of communication in the animal kingdom. Mice use a large repertoire of ultrasonic vocalizations (USVs) in different social contexts, for instance courtship, territorial dispute, dominance and mother-pup interaction. Previous studies have pointed to differences in the USVs in different context, sexes, strains and individuals, however, in many cases the outcomes of the analyses remained inconclusive.We here provide a more general approach to automatically classify USVs using deep neural networks (DNN). We classified the sex of the emitting mouse (C57Bl/6) based on the vocalization's spectrogram, reaching unprecedented performance (~84% correct) in comparison with other techniques (Support Vector Machines: 64%, Ridge regression: 52%). Vocalization characteristics of individual mice only contribute mildly, and sex-only classification reaches ~78%. The performance can only partially be explained by a set of classical shape features, with duration, volume and bandwidth being the most useful predictors. Splitting estimation into two DNNs, from spectrograms to features (57-82%) and features to sex (67%) does not reach the single-step performance.In summary, the emitter's sex can be successfully predicted from their spectrograms using DNNs, excelling over other classification techniques. In contrast to previous research, this suggests that male and female vocalizations differ in their spectrotemporal structure, recognizable even in single vocalizations. 1All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/358143 doi: bioRxiv preprint first posted online Jun. 28, 2018; Introduction Identification of sex on the basis of sensory cues provides important information for successful reproduction. When listening to a conversation, humans can typically make an educated guess about the sexes of the participants. Limited research on this topic has identified multiple acoustic predictors, ranging from the fundamental frequency to formant measures (Pisanski et al. 2016) .Similar to humans, mice vocalize in particular during social interactions (Chabout et al. 2015;Heckman et al. 2016;Heckman et al. 2017;Neunuebel et al. 2015;Portfors and Perkel 2014) . The complexity of the vocalizations produced during social interactions can be substantial (Holy and Guo 2005) . While in humans and other species sex-specific differences in body dimensions (vocal tract length, vocal fold characteristics) lead to predictable differences in vocalization (Markova et al. 2016;Pfefferle and Fischer 2006) , the vocal tract properties of male and female have not been shown to differ significantly (Mahrt et al. 2016;Roberts 1975) . Hence, for mice the expected differences in male/female USVs are less predictable from physiological characteristics.Previous research on the properties of male and female ultrasonic vocalizat...

show abstract

CBA/CaJ mouse ultrasonic vocalizations depend on prior social experience

Cited by 32 publications

References 46 publications

Classifying sex and strain from mouse ultrasonic vocalizations using deep learning

Classifying sex and strain from mouse ultrasonic vocalizations using deep learning

The effects of age and sex on the detection of pure tones by adult CBA/CaJ mice (Mus musculus)

Classification of mouse ultrasonic vocalizations using deep learning

Contact Info

Product

Resources

About