{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:11:57Z","timestamp":1767175917240,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012990","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000}}],"reference-count":84,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["PIA\/ANR-16-CONV-0005"],"award-info":[{"award-number":["PIA\/ANR-16-CONV-0005"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-19-P3IA-0001"],"award-info":[{"award-number":["ANR-19-P3IA-0001"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-20-CE45-0021"],"award-info":[{"award-number":["ANR-20-CE45-0021"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-20-IDEES-0002"],"award-info":[{"award-number":["ANR-20-IDEES-0002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-17-CE37-0019-01"],"award-info":[{"award-number":["ANR-17-CE37-0019-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-22-CE37-0027"],"award-info":[{"award-number":["ANR-22-CE37-0027"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["798050"],"award-info":[{"award-number":["798050"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the\n                    <jats:italic>Drosophila melanogaster<\/jats:italic>\n                    larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280\u2009716 larvae across 569 genetic lines.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012990","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T16:03:24Z","timestamp":1745251404000},"page":"e1012990","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Statistical signature of subtle behavioral changes in large-scale assays"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9148-5223","authenticated-orcid":true,"given":"Alexandre","family":"Blanc","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1615-6400","authenticated-orcid":true,"given":"Fran\u00e7ois","family":"Laurent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex","family":"Barbier-Chebbah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugues","family":"Van Assel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin T.","family":"Cocanougher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin M.W.","family":"Jones","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Hague","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Zlatic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rayan","family":"Chikhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian L.","family":"Vestergaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0525-8620","authenticated-orcid":true,"given":"Tihana","family":"Jovanic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5484-9056","authenticated-orcid":true,"given":"Jean-Baptiste","family":"Masson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chlo\u00e9","family":"Barr\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"issue":"2","key":"pcbi.1012990.ref001","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2017.06.032","article-title":"Mapping the neural substrates of behavior","volume":"170","author":"AA Robie","year":"2017","journal-title":"Cell"},{"issue":"6182","key":"pcbi.1012990.ref002","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1126\/science.1250298","article-title":"Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning","volume":"344","author":"JT Vogelstein","year":"2014","journal-title":"Science"},{"issue":"6636","key":"pcbi.1012990.ref003","doi-asserted-by":"crossref","DOI":"10.1126\/science.add9330","article-title":"The connectome of an insect brain","volume":"379","author":"M Winding","year":"2023","journal-title":"Science"},{"key":"pcbi.1012990.ref004","doi-asserted-by":"crossref","unstructured":"Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu Sc, McKellar CE, et al. Neuronal wiring diagram of an adult brain; 2023.","DOI":"10.1101\/2023.06.27.546656"},{"key":"pcbi.1012990.ref005","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.57443","article-title":"A connectome and analysis of the adult Drosophila central brain","volume":"9","author":"LK Scheffer","year":"2020","journal-title":"eLife"},{"issue":"28","key":"pcbi.1012990.ref006","doi-asserted-by":"crossref","first-page":"9715","DOI":"10.1073\/pnas.0803697105","article-title":"Tools for neuroanatomy and neurogenetics in Drosophila","volume":"105","author":"BD Pfeiffer","year":"2008","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"2","key":"pcbi.1012990.ref007","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1534\/genetics.110.119917","article-title":"Refinement of tools for targeted gene expression in Drosophila","volume":"186","author":"BD Pfeiffer","year":"2010","journal-title":"Genetics"},{"issue":"8","key":"pcbi.1012990.ref008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0071706","article-title":"High-throughput analysis of stimulus-evoked behaviors in Drosophila larva reveals multiple modality-specific escape strategies","volume":"8","author":"T Ohyama","year":"2013","journal-title":"PLoS ONE"},{"issue":"3","key":"pcbi.1012990.ref009","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2016.09.009","article-title":"Competitive disinhibition mediates behavioral choice and sequences in Drosophila","volume":"167","author":"T Jovanic","year":"2016","journal-title":"Cell"},{"issue":"11","key":"pcbi.1012990.ref010","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1038\/s41386-020-0776-y","article-title":"Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions","volume":"45","author":"O Sturman","year":"2020","journal-title":"Neuropsychopharmacol"},{"issue":"1","key":"pcbi.1012990.ref011","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1146\/annurev-neuro-070815-013845","article-title":"Computational analysis of behavior","volume":"39","author":"SER Egnor","year":"2016","journal-title":"Annu Rev Neurosci"},{"issue":"9","key":"pcbi.1012990.ref012","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","article-title":"DeepLabCut: markerless pose estimation of user-defined body parts with deep learning","volume":"21","author":"A Mathis","year":"2018","journal-title":"Nat Neurosci"},{"issue":"4","key":"pcbi.1012990.ref013","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/s41592-022-01426-1","article-title":"SLEAP: a deep learning system for multi-animal pose tracking","volume":"19","author":"TD Pereira","year":"2022","journal-title":"Nat Methods"},{"issue":"7","key":"pcbi.1012990.ref014","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1038\/s41596-019-0176-0","article-title":"Using DeepLabCut for 3D markerless pose estimation across species and behaviors","volume":"14","author":"T Nath","year":"2019","journal-title":"Nat Protoc"},{"key":"pcbi.1012990.ref015","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.63377","article-title":"DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels","volume":"10","author":"JP Bohnslav","year":"2021","journal-title":"eLife"},{"key":"pcbi.1012990.ref016","doi-asserted-by":"crossref","unstructured":"Nilsson SR, Goodwin NL, Choong JJ, Hwang S, Wright HR, Norville ZC, et al. Simple Behavioral Analysis (SimBA) \u2013 an Open Source Toolkit for Computer Classification of Complex Social Behaviors in Experimental Animals; 2020.","DOI":"10.1101\/2020.04.19.049452"},{"key":"pcbi.1012990.ref017","doi-asserted-by":"crossref","unstructured":"Segalin C, Williams J, Karigo T, Hui M, Zelikowsky M, Sun JJ, et al.. The Mouse Action Recognition System (MARS): a software pipeline for automated analysis of social behaviors in mice; 2021.","DOI":"10.7554\/eLife.63720.sa2"},{"issue":"11","key":"pcbi.1012990.ref018","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1038\/s41551-019-0396-1","article-title":"Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning","volume":"3","author":"F de Chaumont","year":"2019","journal-title":"Nat Biomed Eng"},{"issue":"4","key":"pcbi.1012990.ref019","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1038\/nmeth.1310","article-title":"Automated monitoring and analysis of social behavior in Drosophila","volume":"6","author":"H Dankert","year":"2009","journal-title":"Nat Methods"},{"key":"pcbi.1012990.ref020","doi-asserted-by":"crossref","unstructured":"Graving JM, Couzin ID. VAE-SNE: a deep generative model for simultaneous dimensionality reduction and clustering; 2020.","DOI":"10.1101\/2020.07.17.207993"},{"key":"pcbi.1012990.ref021","doi-asserted-by":"crossref","unstructured":"Reddy G, Desban L, Tanaka H, Roussel J, Mirat O, Wyart C. A lexical approach for identifying behavioral action sequences; 2021.","DOI":"10.1101\/2020.08.27.270694"},{"issue":"1","key":"pcbi.1012990.ref022","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-022-04080-7","article-title":"Identifying behavioral structure from deep variational embeddings of animal motion","volume":"5","author":"K Luxem","year":"2022","journal-title":"Commun Biol"},{"issue":"99","key":"pcbi.1012990.ref023","doi-asserted-by":"crossref","first-page":"20140672","DOI":"10.1098\/rsif.2014.0672","article-title":"Mapping the stereotyped behaviour of freely moving fruit flies","volume":"11","author":"GJ Berman","year":"2014","journal-title":"J R Soc Interface"},{"issue":"1","key":"pcbi.1012990.ref024","doi-asserted-by":"crossref","first-page":"015006","DOI":"10.1088\/1478-3975\/aa5c50","article-title":"An unsupervised method for quantifying the behavior of paired animals","volume":"14","author":"U Klibaite","year":"2017","journal-title":"Phys Biol"},{"issue":"3","key":"pcbi.1012990.ref025","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.neuron.2020.11.016","article-title":"Continuous whole-body 3D kinematic recordings across the rodent behavioral repertoire","volume":"109","author":"JD Marshall","year":"2021","journal-title":"Neuron"},{"key":"pcbi.1012990.ref026","article-title":"BehaveNet: nonlinear embedding and bayesian neural decoding of behavioral videos.","author":"E Batty","year":"2019"},{"issue":"1","key":"pcbi.1012990.ref027","doi-asserted-by":"crossref","first-page":"5188","DOI":"10.1038\/s41467-021-25420-x","article-title":"B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors","volume":"12","author":"AI Hsu","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"pcbi.1012990.ref028","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuron.2019.09.038","article-title":"Computational neuroethology: a call to action","volume":"104","author":"SR Datta","year":"2019","journal-title":"Neuron"},{"key":"pcbi.1012990.ref029","doi-asserted-by":"crossref","first-page":"37891","DOI":"10.1109\/ACCESS.2020.2975926","article-title":"Limbs detection and tracking of head-fixed mice for behavioral phenotyping using motion tubes and deep learning","volume":"8","author":"W Abbas","year":"2020","journal-title":"IEEE Access"},{"key":"pcbi.1012990.ref030","article-title":"Machine learning reveals the control mechanics of an insect wing hinge","author":"JM Melis","year":"2024","journal-title":"bioRxiv"},{"key":"pcbi.1012990.ref031","doi-asserted-by":"crossref","unstructured":"Sommer C, Str\u0308ahle C, K\u0308othe U, Hamprecht FA. Ilastik: interactive learning and segmentation toolkit. In: Eighth IEEE international symposium on biomedical imaging (ISBI 2011). Proceedings; 2011. p. 230\u20133.","DOI":"10.1109\/ISBI.2011.5872394"},{"issue":"1","key":"pcbi.1012990.ref032","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/nmeth.2281","article-title":"JAABA: interactive machine learning for automatic annotation of animal behavior","volume":"10","author":"M Kabra","year":"2012","journal-title":"Nature Methods"},{"issue":"2","key":"pcbi.1012990.ref033","article-title":"Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila","volume":"16","author":"J-B Masson","year":"2020","journal-title":"PLOS Genet"},{"issue":"12","key":"pcbi.1012990.ref034","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1016\/j.cub.2023.05.043","article-title":"Comparative connectomics and escape behavior in larvae of closely related Drosophila species","volume":"33","author":"J Zhu","year":"2023","journal-title":"Curr Biol"},{"key":"pcbi.1012990.ref035","doi-asserted-by":"crossref","unstructured":"Lehman M, Barr\u00e9 C, Hasan MA, Flament B, Autran S, Dhiman N, et al. Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva. 2023. bioRxiv. 10.1101\/2023.12.24.573276","DOI":"10.1101\/2023.12.24.573276"},{"issue":"31","key":"pcbi.1012990.ref036","doi-asserted-by":"crossref","first-page":"10465","DOI":"10.1523\/JNEUROSCI.1631-10.2010","article-title":"Fine thermotactic discrimination between the optimal and slightly cooler temperatures via a TRPV channel in chordotonal neurons","volume":"30","author":"Y Kwon","year":"2010","journal-title":"J Neurosci"},{"issue":"33","key":"pcbi.1012990.ref037","doi-asserted-by":"crossref","first-page":"13612","DOI":"10.1073\/pnas.1312477110","article-title":"Sound response mediated by the TRP channels NOMPC, NANCHUNG, and INACTIVE in chordotonal organs of Drosophila larvae","volume":"110","author":"W Zhang","year":"2013","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"2","key":"pcbi.1012990.ref038","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/0896-6273(95)90290-2","article-title":"Targeted expression of tetanus toxin light chain in Drosophila specifically eliminates synaptic transmission and causes behavioral defects","volume":"14","author":"ST Sweeney","year":"1995","journal-title":"Neuron"},{"issue":"7","key":"pcbi.1012990.ref039","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/nmeth.1625","article-title":"High-throughput behavioral analysis in C. elegans","volume":"8","author":"NA Swierczek","year":"2011","journal-title":"Nat Methods"},{"issue":"3","key":"pcbi.1012990.ref040","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1103\/RevModPhys.83.943","article-title":"Bayesian inference in physics","volume":"83","author":"U von Toussaint","year":"2011","journal-title":"Rev Mod Phys"},{"key":"pcbi.1012990.ref041","article-title":"High-throughput automated methods for classical and operant conditioning of Drosophila larvae","volume":"11","author":"EC Croteau-Chonka","year":"2022","journal-title":"eLife"},{"key":"pcbi.1012990.ref042","article-title":"Pattern recognition and machine learning.","author":"CM Bishop","year":"2011"},{"key":"pcbi.1012990.ref043","doi-asserted-by":"crossref","unstructured":"Schneider S, Baevski A, Collobert R, Auli M. Wav2vec: unsupervised pre-training for speech recognition. arXiv preprint 2019. https:\/\/arxiv.org\/abs\/1904.05862 [cs]","DOI":"10.21437\/Interspeech.2019-1873"},{"key":"pcbi.1012990.ref044","doi-asserted-by":"crossref","unstructured":"Doersch C, Gupta A, Efros AA. Unsupervised visual representation learning by context prediction. arXiv preprint 2016. https:\/\/arxiv.org\/abs\/1505.05192 [cs]","DOI":"10.1109\/ICCV.2015.167"},{"key":"pcbi.1012990.ref045","doi-asserted-by":"crossref","unstructured":"Doersch C, Zisserman A. Multi-task self-supervised visual learning. arXiv preprint 2017. https:\/\/arxiv.org\/abs\/1708.07860 [cs]","DOI":"10.1109\/ICCV.2017.226"},{"key":"pcbi.1012990.ref046","unstructured":"Wei C, Fan H, Xie S, Wu CY, Yuille A, Feichtenhofer C. Masked feature prediction for self-supervised visual pre-training. arXiv preprint 2021. https:\/\/arxiv.org\/abs\/2112.09133 [cs]"},{"key":"pcbi.1012990.ref047","unstructured":"Li Z, Chen Z, Yang F, Li W, Zhu Y, Zhao C, et al. MST: masked self-supervised transformer for visual representation. arXiv preprint 2021. https:\/\/arxiv.org\/abs\/2106.05656 [cs]"},{"key":"pcbi.1012990.ref048","unstructured":"Goyal P, Caron M, Lefaudeux B, Xu M, Wang P, Pai V, et al. Self-supervised pretraining of visual features in the wild; 2021."},{"key":"pcbi.1012990.ref049","unstructured":"Balestriero R, Ibrahim M, Sobal V, Morcos A, Shekhar S, Goldstein T, et al. A cookbook of self-supervised learning; 2023."},{"key":"pcbi.1012990.ref050","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction; 2018.","DOI":"10.21105\/joss.00861"},{"issue":"7549","key":"pcbi.1012990.ref051","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/nature14297","article-title":"A multilevel multimodal circuit enhances action selection in Drosophila","volume":"520","author":"T Ohyama","year":"2015","journal-title":"Nature"},{"key":"pcbi.1012990.ref052","doi-asserted-by":"crossref","unstructured":"Gandara L, Jacoby R, Laurent F, Spatuzzi M, Vlachopoulos N, Borst NO, et al. Pervasive sublethal effects of agrochemicals as contributing factors to insect decline. 2024. bioRxiv. 10.1101\/2024.01.12.575373","DOI":"10.1101\/2024.01.12.575373"},{"issue":"1","key":"pcbi.1012990.ref053","first-page":"162","article-title":"Studying neural circuits of decision-making in Drosophila larva.","volume":"34","author":"T Jovanic","year":"2020"},{"issue":"4","key":"pcbi.1012990.ref054","doi-asserted-by":"crossref","DOI":"10.1016\/j.cub.2019.01.009","article-title":"Neural substrates of Drosophila larval anemotaxis","volume":"29","author":"T Jovanic","year":"2019","journal-title":"Curr Biol"},{"issue":"25","key":"pcbi.1012990.ref055","first-page":"723","article-title":"A Kernel two-sample test","volume":"13","author":"A Gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"pcbi.1012990.ref056","unstructured":"Sutherland DJ, Tung HY, Strathmann H, De S, Ramdas A, Smola A, et al. Generative models and model criticism via optimized maximum mean discrepancy. arXiv preprint 2021. https:\/\/arxiv.org\/abs\/1611.04488 [cs, stat]"},{"key":"pcbi.1012990.ref057","unstructured":"Gretton A, Borgwardt KM, Rasch M, Scholkopf B. A Kernel approach to comparing distributions. In: AAAI Conference on Artificial Intelligence. 2007;5."},{"key":"pcbi.1012990.ref058","unstructured":"Gretton A, Sriperumbudur B, Sejdinovic D, Strathmann H, Balakrishnan S, Pontil M. Optimal Kernel choice for large-scale two-sample tests. In: Adv Neural Inf Process Syst. 2012:1205\u201313."},{"issue":"5","key":"pcbi.1012990.ref059","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1073\/pnas.1116772109","article-title":"Noninvasive inference of the molecular chemotactic response using bacterial trajectories","volume":"109","author":"J-B Masson","year":"2012","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1012990.ref060","doi-asserted-by":"crossref","unstructured":"Weiner P. Linear pattern matching algorithms. In: 14th Annual Symposium on Switching and Automata Theory (Swat 1973). 1973. p. 1\u201311","DOI":"10.1109\/SWAT.1973.13"},{"issue":"3","key":"pcbi.1012990.ref061","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/BF01206331","article-title":"On-line construction of suffix trees","volume":"14","author":"E Ukkonen","year":"1995","journal-title":"Algorithmica"},{"issue":"2","key":"pcbi.1012990.ref062","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1137\/S0036144598347035","article-title":"Matrices, vector spaces, and information retrieval","volume":"41","author":"MW Berry","year":"1999","journal-title":"SIAM Rev"},{"key":"pcbi.1012990.ref063","author":"L McInnes"},{"issue":"1","key":"pcbi.1012990.ref064","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","article-title":"An information-theoretic perspective of Tf\u2013Idf measures","volume":"39","author":"A Aizawa","year":"2003","journal-title":"Inf Process Manag"},{"issue":"9","key":"pcbi.1012990.ref065","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TKDE.2008.50","article-title":"Efficient phrase-based document similarity for clustering","volume":"20","author":"H Chim","year":"2008","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pcbi.1012990.ref066","doi-asserted-by":"crossref","unstructured":"Arias-Castro E, Pelletier B, Saligrama V. Remember the curse of dimensionality: the case of goodness-of-fit testing in arbitrary dimension; 2018.","DOI":"10.1080\/10485252.2018.1435875"},{"key":"pcbi.1012990.ref067","doi-asserted-by":"crossref","article-title":"Can shared-neighbor distances defeat the curse of dimensionality?","author":"ME Houle","DOI":"10.1007\/978-3-642-13818-8_34"},{"issue":"2","key":"pcbi.1012990.ref068","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1093\/biomet\/asac049","article-title":"Kernel two-sample tests in high dimension: interplay between moment discrepancy and dimension-and-sample orders","volume":"110","author":"J Yan","year":"2023","journal-title":"Biometrika"},{"key":"pcbi.1012990.ref069","doi-asserted-by":"crossref","unstructured":"Reddi SJ, Ramdas A, P\u00b4oczos B, Singh A, Wasserman L. On the decreasing power of Kernel and distance based nonparametric hypothesis tests in high dimensions; 2014.","DOI":"10.1609\/aaai.v29i1.9692"},{"key":"pcbi.1012990.ref070","unstructured":"Binkowski M, Sutherland DJ, Arbel M, Gretton A. Demystifying MMD GANs; 2021."},{"key":"pcbi.1012990.ref071","unstructured":"Gretton A, Borgwardt K, Rasch MJ, Scholkopf B, Smola AJ. A Kernel method for the two-sample problem. arXiv preprint 2008. https:\/\/arxiv.org\/abs\/0805.2368 [cs]"},{"issue":"1","key":"pcbi.1012990.ref072","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev.neuro.29.051605.113038","article-title":"The neural basis of decision making","volume":"30","author":"JI Gold","year":"2007","journal-title":"Annu Rev Neurosci"},{"issue":"4","key":"pcbi.1012990.ref073","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1038\/s41593-023-01281-z","article-title":"Ascending neurons convey behavioral state to integrative sensory and action selection brain regions","volume":"26","author":"C-L Chen","year":"2023","journal-title":"Nat Neurosci"},{"issue":"19","key":"pcbi.1012990.ref074","doi-asserted-by":"crossref","DOI":"10.1016\/j.cub.2008.07.081","article-title":"Neuronal decision-making circuits","volume":"18","author":"WB Kristan","year":"2008","journal-title":"Curr Biol"},{"issue":"7","key":"pcbi.1012990.ref075","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1038\/nn.3741","article-title":"A spike-timing mechanism for action selection","volume":"17","author":"CR von Reyn","year":"2014","journal-title":"Nat Neurosci"},{"key":"pcbi.1012990.ref076","first-page":"195","article-title":"Policy compression: an information bottleneck in action selection","author":"L Lai","year":"2021"},{"issue":"1485","key":"pcbi.1012990.ref077","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1098\/rstb.2007.2054","article-title":"Cortical mechanisms of action selection: the affordance competition hypothesis","volume":"362","author":"P Cisek","year":"2007","journal-title":"Philos Trans R Soc Lond B Biol Sci"},{"issue":"1655","key":"pcbi.1012990.ref078","doi-asserted-by":"crossref","first-page":"20130623","DOI":"10.1098\/rstb.2013.0623","article-title":"A continuous-time neural model for sequential action","volume":"369","author":"G Kachergis","year":"2014","journal-title":"Philos Trans R Soc Lond B Biol Sci"},{"key":"pcbi.1012990.ref079","unstructured":"Gershman SJ, Goodman ND. Amortized inference in probabilistic reasoning. Cognit Sci. 2014;36."},{"key":"pcbi.1012990.ref080","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.02951","article-title":"A suppression hierarchy among competing motor programs drives sequential grooming in Drosophila","volume":"3","author":"AM Seeds","year":"2014","journal-title":"eLife"},{"issue":"6999","key":"pcbi.1012990.ref081","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1038\/nature02713","article-title":"Median bundle neurons coordinate behaviours during Drosophila male courtship","volume":"430","author":"DS Manoli","year":"2004","journal-title":"Nature"},{"issue":"3","key":"pcbi.1012990.ref082","doi-asserted-by":"crossref","DOI":"10.1016\/j.cub.2018.12.019","article-title":"Threshold-based ordering of sequential actions during Drosophila courtship","volume":"29","author":"CE McKellar","year":"2019","journal-title":"Curr Biol"},{"issue":"1","key":"pcbi.1012990.ref083","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1037\/0033-2909.96.1.3","article-title":"Learning of movement sequences.","volume":"96","author":"JA Adams","year":"1984","journal-title":"Psychol Bullet"},{"issue":"7315","key":"pcbi.1012990.ref084","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/nature09352","article-title":"Genome-wide analysis of a long-term evolution experiment with Drosophila","volume":"467","author":"MK Burke","year":"2010","journal-title":"Nature"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1012990","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012990","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T14:03:34Z","timestamp":1748527414000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012990"}},"subtitle":[],"editor":[{"given":"Daniele","family":"Marinazzo","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":84,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4,21]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012990","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,21]]}}}