Editing Behavioral neuroscience (section)

=== Quantifying behavior ===

* [[File:Drosophila anipose tracking.png|thumb|Fruit fly ([[Drosophila melanogaster]]) leg joints being tracked in 3D with Anipose.<ref>{{Cite journal |last1=Karashchuk |first1=Pierre |last2=Rupp |first2=Katie L. |last3=Dickinson |first3=Evyn S. |last4=Walling-Bell |first4=Sarah |last5=Sanders |first5=Elischa |last6=Azim |first6=Eiman |last7=Brunton |first7=Bingni W. |last8=Tuthill |first8=John C. |date=2021-09-28 |title=Anipose: A toolkit for robust markerless 3D pose estimation |journal=Cell Reports |language=en |volume=36 |issue=13 |page=109730 |doi=10.1016/j.celrep.2021.109730 |issn=2211-1247 |pmc=8498918 |pmid=34592148}}</ref>]][[Pose (computer vision)|Markerless pose estimation]] – The advancement of [[computer vision]] techniques in recent years have allowed for precise quantifications of animal movements without needing to fit physical markers onto the subject. On high-speed video captured in a behavioral assay, keypoints from the subject can be extracted frame-by-frame,<ref>{{Cite journal |last1=Mathis |first1=Alexander |last2=Mamidanna |first2=Pranav |last3=Cury |first3=Kevin M. |last4=Abe |first4=Taiga |last5=Murthy |first5=Venkatesh N. |last6=Mathis |first6=Mackenzie Weygandt |last7=Bethge |first7=Matthias |date=September 2018 |title=DeepLabCut: markerless pose estimation of user-defined body parts with deep learning |url=https://www.nature.com/articles/s41593-018-0209-y |journal=Nature Neuroscience |language=en |volume=21 |issue=9 |pages=1281–1289 |doi=10.1038/s41593-018-0209-y |pmid=30127430 |s2cid=52807326 |issn=1546-1726|url-access=subscription }}</ref> which is often useful to analyze in tandem with neural recordings/manipulations. Analyses can be conducted on how keypoints (i.e. parts of the animal) move within different phases of a particular behavior (on a short timescale),<ref>{{Cite web|last1=Syeda |first1=Atika |last2=Zhong |first2=Lin |last3=Tung |first3=Renee |last4=Long |first4=Will |last5=Pachitariu |first5=Marius |last6=Stringer |first6=Carsen |date=2022-11-04 |title=Facemap: a framework for modeling neural activity based on orofacial tracking |url=https://www.biorxiv.org/content/10.1101/2022.11.03.515121v1 |language=en |pages=2022.11.03.515121 |doi=10.1101/2022.11.03.515121|s2cid=253371320 }}</ref> or throughout an animal's behavioral repertoire (longer timescale).<ref>{{Cite journal |last1=Marshall |first1=Jesse D. |last2=Aldarondo |first2=Diego E. |last3=Dunn |first3=Timothy W. |last4=Wang |first4=William L. |last5=Berman |first5=Gordon J. |last6=Ölveczky |first6=Bence P. |date=2021-02-03 |title=Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire |journal=Neuron |language=en |volume=109 |issue=3 |pages=420–437.e8 |doi=10.1016/j.neuron.2020.11.016 |issn=0896-6273 |pmc=7864892 |pmid=33340448}}</ref> These keypoint changes can be compared with corresponding changes in neural activity. A machine learning approach can also be used to identify specific behaviors (e.g. forward walking, turning, grooming, courtship, etc.), and quantify the dynamics of transitions between behaviors.<ref>{{Cite journal |last1=Berman |first1=Gordon J. |last2=Choi |first2=Daniel M. |last3=Bialek |first3=William |last4=Shaevitz |first4=Joshua W. |date=2014-10-06 |title=Mapping the stereotyped behaviour of freely moving fruit flies |journal=Journal of the Royal Society Interface |language=en |volume=11 |issue=99 |pages=20140672 |doi=10.1098/rsif.2014.0672 |issn=1742-5689 |pmc=4233753 |pmid=25142523}}</ref><ref>{{Cite journal |last1=Tillmann |first1=Jens F. |last2=Hsu |first2=Alexander I. |last3=Schwarz |first3=Martin K. |last4=Yttri |first4=Eric A. |date=April 2024 |title=A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior |url=https://www.nature.com/articles/s41592-024-02200-1 |journal=Nature Methods |language=en |volume=21 |issue=4 |pages=703–711 |doi=10.1038/s41592-024-02200-1 |pmid=38383746 |issn=1548-7105}}</ref><ref>{{Cite journal |last1=Goodwin |first1=Nastacia L. |last2=Choong |first2=Jia J. |last3=Hwang |first3=Sophia |last4=Pitts |first4=Kayla |last5=Bloom |first5=Liana |last6=Islam |first6=Aasiya |last7=Zhang |first7=Yizhe Y. |last8=Szelenyi |first8=Eric R. |last9=Tong |first9=Xiaoyu |last10=Newman |first10=Emily L. |last11=Miczek |first11=Klaus |last12=Wright |first12=Hayden R. |last13=McLaughlin |first13=Ryan J. |last14=Norville |first14=Zane C. |last15=Eshel |first15=Neir |date=2024-05-22 |title=Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience |url=https://www.nature.com/articles/s41593-024-01649-9 |journal=Nature Neuroscience |volume=27 |issue=7 |language=en |pages=1411–1424 |doi=10.1038/s41593-024-01649-9 |pmid=38778146 |pmc=11268425 |pmc-embargo-date=July 1, 2025 |issn=1546-1726}}</ref><ref>{{Citation |last1=Weinreb |first1=Caleb |title=Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics |date=2023-03-17 |language=en |doi=10.1101/2023.03.16.532307 |pmc=10055085 |pmid=36993589 |last2=Pearl |first2=Jonah |last3=Lin |first3=Sherry |last4=Osman |first4=Mohammed Abdal Monium |last5=Zhang |first5=Libby |last6=Annapragada |first6=Sidharth |last7=Conlin |first7=Eli |last8=Hoffman |first8=Red |last9=Makowska |first9=Sofia|journal=BioRxiv: The Preprint Server for Biology }}</ref>