The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE
KETCHEN Jr., D. J., & SHOOK, C. L. (1996) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(199606)17:6<441::aid-smj819>3.0.co;2-g · Cited by 2,375
Rodent models in neuroscience research: is it a rat race?
Ellenbroek, B., & Youn, J. (2016) · Disease Models & Mechanisms · doi:10.1242/dmm.026120 · Cited by 627
Ultrasonic vocalizations as indices of affective states in rats
Knutson, B., Burgdorf, J., & Panksepp, J. (2002) · Psychological Bulletin · doi:10.1037/0033-2909.128.6.961 · Cited by 618
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Deep Convolutional Neural Networks for Large-scale Speech Tasks
Sainath, T. N., Kingsbury, B., Saon, G., Soltau, H., Mohamed, A.-R., Dahl, G., & Ramabhadran, B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.08.005 · Cited by 496
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Cited by
Computational bioacoustics with deep learning: a review and roadmap
Stowell, D. (2022) · PeerJ · doi:10.7717/peerj.13152 · Cited by 471
Computational Neuroethology: A Call to Action
Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.038 · Cited by 453
Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
Sainburg, T., Thielk, M., & Gentner, T. Q. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1008228 · Cited by 319
Quantifying behavior to understand the brain
Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00734-z · Cited by 309
Measuring behavior in the home cage: study design, applications, challenges, and perspectives
Grieco, F., Bernstein, B. J., Biemans, B., Bikovski, L., Burnett, C. J., Cushman, J. D., van Dam, E. A., Fry, S. A., Richmond-Hacham, B., Homberg, J. R., et al. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.735387 · Cited by 109
Metadata
- Authors
- Coffey, K. R., Marx, R. E., & Neumaier, J. F.
- Venue
- Neuropsychopharmacology
- Published
- 2019-01-04
- Volume
- 44
- Issue
- 5
- Pages
- 859-868
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- OpenAlex
- W2907755528
- Cited by
- 389
- References
- 34
- FWCI
- 28.47
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations. https://app.cochat.ai/papers/10.1038/s41386-018-0303-6
- The paper
- Coffey, K. R., Marx, R. E., & Neumaier, J. F. (2019). DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations. Neuropsychopharmacology. https://doi.org/10.1038/s41386-018-0303-6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
