references.bib

 @book{murphy2023probabilistic,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: Advanced Topics",
 publisher = "MIT Press",
 year = 2023,
 url = "http://probml.github.io/book2"
}

@book{bishop2006pattern,
  title={Pattern Recognition and Machine Learning},
  author={Bishop, Christopher M},
  year={2006},
  publisher={Springer},
  url={https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf}
}

@book{luo2020principles,
  title={Principles of Neurobiology},
  author={Luo, Liqun},
  year={2020},
  publisher={Garland Science}
}

@article{hodgkin1952quantitative,
  title={A quantitative description of membrane current and its application to conduction and excitation in nerve},
  author={Hodgkin, Alan L and Huxley, Andrew F},
  journal={The Journal of Physiology},
  volume={117},
  number={4},
  pages={500},
  year={1952}
}


@article{jun2017fully,
  title    = "Fully integrated silicon probes for high-density recording of
              neural activity",
  author   = "Jun, James J and Steinmetz, Nicholas A and Siegle, Joshua H and
              Denman, Daniel J and Bauza, Marius and Barbarits, Brian and Lee,
              Albert K and Anastassiou, Costas A and Andrei, Alexandru and
              Ayd{\i}n, {\c C}a{\u g}atay and Barbic, Mladen and Blanche,
              Timothy J and Bonin, Vincent and Couto, Jo{\~a}o and Dutta,
              Barundeb and Gratiy, Sergey L and Gutnisky, Diego A and
              H{\"a}usser, Michael and Karsh, Bill and Ledochowitsch, Peter and
              Lopez, Carolina Mora and Mitelut, Catalin and Musa, Silke and
              Okun, Michael and Pachitariu, Marius and Putzeys, Jan and Rich, P
              Dylan and Rossant, Cyrille and Sun, Wei-Lung and Svoboda, Karel
              and Carandini, Matteo and Harris, Kenneth D and Koch, Christof
              and O'Keefe, John and Harris, Timothy D",
  journal  = "Nature",
  volume   =  551,
  number   =  7679,
  pages    = "232--236",
  month    =  nov,
  year     =  2017,
  language = "en"
}

@article{steinmetz2021neuropixels,
  title={Neuropixels 2.0: {A} miniaturized high-density probe for stable, long-term brain recordings},
  author={Steinmetz, Nicholas A and Aydin, Cagatay and Lebedeva, Anna and Okun, Michael and Pachitariu, Marius and Bauza, Marius and Beau, Maxime and Bhagat, Jai and B{\"o}hm, Claudia and Broux, Martijn and others},
  journal={Science},
  volume={372},
  number={6539},
  pages={eabf4588},
  year={2021},
  publisher={American Association for the Advancement of Science}
}

@article{gold2006origin,
  title={On the origin of the extracellular action potential waveform: a modeling study},
  author={Gold, Carl and Henze, Darrell A and Koch, Christof and Buzsaki, Gyorgy},
  journal={Journal of neurophysiology},
  volume={95},
  number={5},
  pages={3113--3128},
  year={2006},
  publisher={American Physiological Society}
}

@article{ding2008convex,
  title={Convex and semi-nonnegative matrix factorizations},
  author={Ding, Chris HQ and Li, Tao and Jordan, Michael I},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={32},
  number={1},
  pages={45--55},
  year={2008},
  publisher={IEEE}
}

@book{hastie2009elements,
  title={The Elements of Statistical Learning: Data Mining, Inference, and Prediction},
  author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome H and Friedman, Jerome H},
  volume={2},
  year={2009},
  publisher={Springer}
}

@book{dayan2005theoretical,
  title={Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems},
  author={Dayan, Peter and Abbott, Laurence F},
  year={2005},
  publisher={MIT press}
}

@article{mnih2007probabilistic,
  title={Probabilistic matrix factorization},
  author={Mnih, Andriy and Salakhutdinov, Russ R},
  journal={Advances in neural information processing systems},
  volume={20},
  year={2007}
}

@article{tipping1999probabilistic,
  title={Probabilistic principal component analysis},
  author={Tipping, Michael E and Bishop, Christopher M},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={61},
  number={3},
  pages={611--622},
  year={1999},
  publisher={Wiley Online Library}
}

@article{lee1999learning,
  title={Learning the parts of objects by non-negative matrix factorization},
  author={Lee, Daniel D and Seung, H Sebastian},
  journal={Nature},
  volume={401},
  number={6755},
  pages={788--791},
  year={1999},
  publisher={Nature Publishing Group}
}

@article{blei2003latent,
  title={Latent {D}irichlet allocation},
  author={Blei, David M and Ng, Andrew Y and Jordan, Michael I},
  journal={Journal of machine Learning research},
  volume={3},
  number={Jan},
  pages={993--1022},
  year={2003}
}

@article{gopalan2013efficient,
  title={Efficient discovery of overlapping communities in massive networks},
  author={Gopalan, Prem K and Blei, David M},
  journal={Proceedings of the National Academy of Sciences},
  volume={110},
  number={36},
  pages={14534--14539},
  year={2013},
  publisher={National Acad Sciences}
}

@article{roweis1999unifying,
  title={A unifying review of linear {G}aussian models},
  author={Roweis, Sam and Ghahramani, Zoubin},
  journal={Neural computation},
  volume={11},
  number={2},
  pages={305--345},
  year={1999},
  publisher={MIT Press}
}

@article{pachitariu2016kilosort,
  title={Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels},
  author={Pachitariu, Marius and Steinmetz, Nicholas and Kadir, Shabnam and Carandini, Matteo and others},
  journal={BioRxiv},
  pages={061481},
  year={2016},
  publisher={Cold Spring Harbor Laboratory}
}


@article{pachitariu2023solving,
  title={Solving the spike sorting problem with Kilosort},
  author={Pachitariu, Marius and Sridhar, Shashwat and Stringer, Carsen},
  journal={bioRxiv},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}

@article{lee2020yass,
  title={{YASS}: {Y}et Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina},
  author={Lee, JinHyung and Mitelut, Catalin and Shokri, Hooshmand and Kinsella, Ian and Dethe, Nishchal and Wu, Shenghao and Li, Kevin and Reyes, Eduardo B and Turcu, Denis and Batty, Eleanor and others},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}

@article{chung2017fully,
  title={A fully automated approach to spike sorting},
  author={Chung, Jason E and Magland, Jeremy F and Barnett, Alex H and Tolosa, Vanessa M and Tooker, Angela C and Lee, Kye Y and Shah, Kedar G and Felix, Sarah H and Frank, Loren M and Greengard, Leslie F},
  journal={Neuron},
  volume={95},
  number={6},
  pages={1381--1394},
  year={2017},
  publisher={Elsevier}
}

@article{deng2015clusterless,
  title={Clusterless decoding of position from multiunit activity using a marked point process filter},
  author={Deng, Xinyi and Liu, Daniel F and Kay, Kenneth and Frank, Loren M and Eden, Uri T},
  journal={Neural computation},
  volume={27},
  number={7},
  pages={1438--1460},
  year={2015},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@article{trautmann2019accurate,
  title={Accurate estimation of neural population dynamics without spike sorting},
  author={Trautmann, Eric M and Stavisky, Sergey D and Lahiri, Subhaneil and Ames, Katherine C and Kaufman, Matthew T and O’Shea, Daniel J and Vyas, Saurabh and Sun, Xulu and Ryu, Stephen I and Ganguli, Surya and others},
  journal={Neuron},
  volume={103},
  number={2},
  pages={292--308},
  year={2019},
  publisher={Elsevier}
}


@ARTICLE{lin2016genetically,
  title    = "Genetically encoded indicators of neuronal activity",
  author   = "Lin, Michael Z and Schnitzer, Mark J",
  journal  = "Nat. Neurosci.",
  volume   =  19,
  number   =  9,
  pages    = "1142--1153",
  month    =  aug,
  year     =  2016,
  language = "en"
}


@ARTICLE{Giovannucci2019-os,
  title    = "{CaImAn} an open source tool for scalable calcium imaging data
              analysis",
  author   = "Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and
              Kalfon, J{\'e}r{\'e}mie and Brown, Brandon L and Koay, Sue Ann
              and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L
              and Zhou, Pengcheng and Khakh, Baljit S and Tank, David W and
              Chklovskii, Dmitri B and Pnevmatikakis, Eftychios A",
  journal  = "Elife",
  volume   =  8,
  month    =  jan,
  year     =  2019,
  keywords = "calcium imaging; data analysis; mouse; neuroscience; one-photon;
              open source; software; two-photon; zebrafish",
  language = "en"
}


@article{pachitariu2017suite2p,
  title={Suite2p: {B}eyond 10,000 neurons with standard two-photon microscopy},
  author={Pachitariu, Marius and Stringer, Carsen and Dipoppa, Mario and Schr{\"o}der, Sylvia and Rossi, L Federico and Dalgleish, Henry and Carandini, Matteo and Harris, Kenneth D},
  journal={BioRxiv},
  pages={061507},
  year={2017},
  publisher={Cold Spring Harbor Laboratory}
}


@article{pnevmatikakis2016simultaneous,
  title={Simultaneous denoising, deconvolution, and demixing of calcium imaging data},
  author={Pnevmatikakis, Eftychios A and Soudry, Daniel and Gao, Yuanjun and Machado, Timothy A and Merel, Josh and Pfau, David and Reardon, Thomas and Mu, Yu and Lacefield, Clay and Yang, Weijian and others},
  journal={Neuron},
  year={2016},
  publisher={Elsevier}
}

@article{Vogelstein-2010,
  title={Fast nonnegative deconvolution for spike train inference from population calcium imaging},
  author={Vogelstein, Joshua T and Packer, Adam M and Machado, Timothy A and Sippy, Tanya and Babadi, Baktash and Yuste, Rafael and Paninski, Liam},
  journal={Journal of Neurophysiology},
  volume={104},
  number={6},
  pages={3691--3704},
  year={2010},
  publisher={American Physiological Society}
}

@article{zhou2018efficient,
  title={Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data},
  author={Zhou, Pengcheng and Resendez, Shanna L and Rodriguez-Romaguera, Jose and Jimenez, Jessica C and Neufeld, Shay Q and Giovannucci, Andrea and Friedrich, Johannes and Pnevmatikakis, Eftychios A and Stuber, Garret D and Hen, Rene and others},
  journal={elife},
  volume={7},
  pages={e28728},
  year={2018},
  publisher={eLife Sciences Publications Limited}
}


@ARTICLE{Jewell2018-tw,
  title     = "Exact spike train inference via $\ell_\{0\}$ optimization",
  author    = "Jewell, Sean and Witten, Daniela",
  abstract  = "Project Euclid - mathematics and statistics online",
  journal   = "Annals of Applied Statistics",
  publisher = "Institute of Mathematical Statistics",
  volume    =  12,
  number    =  4,
  pages     = "2457--2482",
  month     =  dec,
  year      =  2018,
  keywords  = "Neuroscience; calcium imaging; changepoint detection; dynamic
               programming",
  language  = "en"
}


@ARTICLE{Tinbergen1963-fg,
  title     = "On aims and methods of Ethology",
  author    = "Tinbergen, N",
  abstract  = "Zusammenfassung Ich habe in diesem Aufsatz kurz anzudeuten
               versucht, was meiner Ansicht nach das Wesentliche in
               Fragestellung und Methode der Ethologie ist und weshalb wir in
               Konrad Lorenz den Begr{\"u}nder moderner Ethologie erblicken.
               Hierbei habe ich vielleicht das Arbeitsgebiet der Ethologie
               weiter gefa{\ss}t, als unter Ethologen gebr{\"a}uchlich ist.
               Wenn man aber die vielartige Arbeit jener Forscher
               {\"u}bersieht, die sich Ethologen nennen, ist man zu dieser
               weiten Fassung geradezu gezwungen. Ich habe in meiner
               Darstellung weder Vollst{\"a}ndigkeit noch Gleichgewicht
               angestrebt und, um zur Fortf{\"u}hrung des Gespr{\"a}chs
               anzuregen, ruhig meine Steckenpferde geritten, vor allem das
               Verh{\"a}ltnis zwischen Ethologie und Physiologie, die Gefahr
               der Vernachl{\"a}ssigung der Frage der Arterhaltung, Fragen der
               Methodik der ontogenetischen Forschung, und Aufgaben und
               Methoden der Evolutionsforschung. Bei der Einsch{\"a}tzung des
               Anteils, den Lorenz an der Entwicklung der Ethologie genommen
               hat und noch nimmt, habe ich als seinen Hauptbeitrag den
               bezeichnet, da{\ss} er uns gezeigt hat, wie man bew{\"a}hrtes
               ?biologisches Denken? folgerichtig auf Verhalten anwenden kann.
               Da{\ss} er dabei an die Arbeit seiner Vorg{\"a}nger
               angekn{\"u}pft hat, ist nicht mehr verwunderlich, als da{\ss}
               jeder Vater selbst einen Vater hat. Insbesondere scheint mir das
               Wesentliche an Lorenz? Arbeit zu sein, da{\ss} er klar gesehen
               hat, da{\ss} Verhaltensweisen Teile von ?Organen?, von Systemen
               der Arterhaltung sind; da{\ss} ihre Verursachung genau so exakt
               untersucht werden kann wie die gleich welcher anderer
               Lebensvorg{\"a}nge, da{\ss} ihr arterhaltender Wert ebenso
               systematisch und exakt aufweisbar ist wie ihre Verursachung,
               da{\ss} Verhaltensontogenie in grunds{\"a}tzlich gleicher Weise
               erforscht werden kann wie die Ontogenie der Form und da{\ss} die
               Erforschung der Verhaltensevolution der Untersuchung der
               Strukturevolution parallel geht. Und obwohl Lorenz ein riesiges
               Tatsachenmaterial gesammelt hat, ist die Ethologie doch noch
               mehr durch seine Fragestellung und durch k{\"u}hne Hypothesen
               gef{\"o}rdert als durch eigene Nachpr{\"u}fung dieser
               Hypothesen. Ohne den Wert solcher Nachpr{\"u}fung zu
               untersch{\"a}tzen ? ohne die es nat{\"u}rlich keine
               Weiterentwicklung g{\"a}be ? m{\"o}chte ich doch behaupten,
               da{\ss} die durch Nachpr{\"u}fung notwendig gewordenen
               Modifikationen neben der Leistung des urspr{\"u}nglichen
               Ansatzes vergleichsweise unbedeutend sind. Nebenbei sei auch
               daran erinnert, da{\ss} eine der vielen heilsamen Nachwirkungen
               der Lorenzschen Arbeit das wachsende Interesse ist, das die
               Humanpsychologie der Ethologie entgegenbringt - ein erster
               Ansatz einer Entwicklung, deren Tragweite wir noch kaum
               {\"u}bersehen k{\"o}nnen. Am Schlu{\ss} noch eine Bemerkung zur
               Terminologie. Ich habe hier das Wort ?Ethologie? auf einen
               Riesenkomplex von Wissenschaften angewandt, von denen manche,
               wie Psychologie und Physiologie, schon l{\"a}ngst anerkannte
               Namen tragen. Das hei{\ss}t nat{\"u}rlich nicht, da{\ss} ich den
               Namen Ethologie f{\"u}r dieses ganze Gebiet vorschlagen will;
               das w{\"a}re geschichtlich einfach falsch, weil das Wort
               historisch nur die Arbeit einer kleinen Gruppe von Zoologen
               kennzeichnet. Der Name ist nat{\"u}rlich gleichg{\"u}ltig;
               worauf es mir vor allem ankommt, ist darzutun, da{\ss} wir das
               Zusammenwachsen vieler Einzeldisziplinen zu einer
               vielumfassenden Wissenschaft erleben, f{\"u}r die es nur einen
               richtigen Namen gibt: ?Verhaltensbiologie?.
               Selbstverst{\"a}ndlich ist diese synthetische Entwicklung nicht
               die Arbeit eines Mannes oder gar die der Ethologen. Sie ist die
               Folge einer allgemeinen Neigung, Br{\"u}cken zwischen verwandten
               Wissenschaften zu schlagen, einer Neigung, die sich in vielen
               Disziplinen entwickelt hat. Unter den Zoologen ist es Lorenz,
               der hierzu am meisten beigetragen und zudem manche
               Nachbardisziplinen st{\"a}rker beeinflu{\ss}t hat als irgendein
               anderer. Ich bin sogar davon {\"u}berzeugt, da{\ss} diese
               Einwirkungen auf Nachbarwissenschaften noch lange anhalten
               werden und da{\ss} die Verhaltensbiologie erst am Anfang ihrer
               Ontogenie steht.",
  journal   = "Z. Tierpsychol.",
  publisher = "Wiley",
  volume    =  20,
  number    =  4,
  pages     = "410--433",
  year      =  1963,
  language  = "en"
}


@ARTICLE{Datta2019-ji,
  title    = "Computational Neuroethology: A Call to Action",
  author   = "Datta, Sandeep Robert and Anderson, David J and Branson, Kristin
              and Perona, Pietro and Leifer, Andrew",
  abstract = "The brain is worthy of study because it is in charge of behavior.
              A flurry of recent technical advances in measuring and
              quantifying naturalistic behaviors provide an important
              opportunity for advancing brain science. However, the problem of
              understanding unrestrained behavior in the context of neural
              recordings and manipulations remains unsolved, and developing
              approaches to addressing this challenge is critical. Here we
              discuss considerations in computational neuroethology-the science
              of quantifying naturalistic behaviors for understanding the
              brain-and propose strategies to evaluate progress. We point to
              open questions that require resolution and call upon the broader
              systems neuroscience community to further develop and leverage
              measures of naturalistic, unrestrained behavior, which will
              enable us to more effectively probe the richness and complexity
              of the brain.",
  journal  = "Neuron",
  volume   =  104,
  number   =  1,
  pages    = "11--24",
  month    =  oct,
  year     =  2019,
  language = "en"
}


@ARTICLE{Anderson2014-lg,
  title    = "Toward a science of computational ethology",
  author   = "Anderson, David J and Perona, Pietro",
  abstract = "The new field of ``Computational Ethology'' is made possible by
              advances in technology, mathematics, and engineering that allow
              scientists to automate the measurement and the analysis of animal
              behavior. We explore the opportunities and long-term directions
              of research in this area.",
  journal  = "Neuron",
  volume   =  84,
  number   =  1,
  pages    = "18--31",
  month    =  oct,
  year     =  2014,
  language = "en"
}


@ARTICLE{Mathis2018-uz,
  title     = "{DeepLabCut}: markerless pose estimation of user-defined body
               parts with deep learning",
  author    = "Mathis, Alexander and Mamidanna, Pranav and Cury, Kevin M and
               Abe, Taiga and Murthy, Venkatesh N and Mathis, Mackenzie
               Weygandt and Bethge, Matthias",
  abstract  = "Quantifying behavior is crucial for many applications in
               neuroscience. Videography provides easy methods for the
               observation and recording of animal behavior in diverse
               settings, yet extracting particular aspects of a behavior for
               further analysis can be highly time consuming. In motor control
               studies, humans or other animals are often marked with
               reflective markers to assist with computer-based tracking, but
               markers are intrusive, and the number and location of the
               markers must be determined a priori. Here we present an
               efficient method for markerless pose estimation based on
               transfer learning with deep neural networks that achieves
               excellent results with minimal training data. We demonstrate the
               versatility of this framework by tracking various body parts in
               multiple species across a broad collection of behaviors.
               Remarkably, even when only a small number of frames are labeled
               (~200), the algorithm achieves excellent tracking performance on
               test frames that is comparable to human accuracy. Using a deep
               learning approach to track user-defined body parts during
               various behaviors across multiple species, the authors show that
               their toolbox, called DeepLabCut, can achieve human accuracy
               with only a few hundred frames of training data.",
  journal   = "Nat. Neurosci.",
  publisher = "Nature Publishing Group",
  volume    =  21,
  number    =  9,
  pages     = "1281--1289",
  month     =  aug,
  year      =  2018,
  language  = "en"
}

@article{pereira2022sleap,
  title={{SLEAP}: {A} deep learning system for multi-animal pose tracking},
  author={Pereira, Talmo D and Tabris, Nathaniel and Matsliah, Arie and Turner, David M and Li, Junyu and Ravindranath, Shruthi and Papadoyannis, Eleni S and Normand, Edna and Deutsch, David S and Wang, Z Yan and others},
  journal={Nature methods},
  volume={19},
  number={4},
  pages={486--495},
  year={2022},
  publisher={Nature Publishing Group US New York}
}


@ARTICLE{Graving2019-fq,
  title    = "{DeepPoseKit}, a software toolkit for fast and robust animal pose
              estimation using deep learning",
  author   = "Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang
              and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D",
  abstract = "Quantitative behavioral measurements are important for answering
              questions across scientific disciplines-from neuroscience to
              ecology. State-of-the-art deep-learning methods offer major
              advances in data quality and detail by allowing researchers to
              automatically estimate locations of an animal's body parts
              directly from images or videos. However, currently available
              animal pose estimation methods have limitations in speed and
              robustness. Here, we introduce a new easy-to-use software
              toolkit, DeepPoseKit, that addresses these problems using an
              efficient multi-scale deep-learning model, called Stacked
              DenseNet, and a fast GPU-based peak-detection algorithm for
              estimating keypoint locations with subpixel precision. These
              advances improve processing speed >2x with no loss in accuracy
              compared to currently available methods. We demonstrate the
              versatility of our methods with multiple challenging animal pose
              estimation tasks in laboratory and field settings-including
              groups of interacting individuals. Our work reduces barriers to
              using advanced tools for measuring behavior and has broad
              applicability across the behavioral sciences.",
  journal  = "Elife",
  volume   =  8,
  month    =  oct,
  year     =  2019,
  keywords = "D. melanogaster; Equus grevyi; Gr{\'e}vy's zebra; Schistocerca
              gregaria; desert locust; neuroscience",
  language = "en"
}


@ARTICLE{Wu2020-fi,
  title   = "Deep Graph Pose: a semi-supervised deep graphical model for
             improved animal pose tracking",
  author  = "Wu, Anqi and Buchanan, Estefany Kelly and Whiteway, Matthew and
             Schartner, Michael and Meijer, Guido and Noel, Jean-Paul and
             Rodriguez, Erica and Everett, Claire and Norovich, Amy and
             Schaffer, Evan and {Others}",
  journal = "Adv. Neural Inf. Process. Syst.",
  volume  =  33,
  year    =  2020
}


@ARTICLE{Bala2020-ch,
  title    = "Automated markerless pose estimation in freely moving macaques
              with {OpenMonkeyStudio}",
  author   = "Bala, Praneet C and Eisenreich, Benjamin R and Yoo, Seng Bum
              Michael and Hayden, Benjamin Y and Park, Hyun Soo and Zimmermann,
              Jan",
  abstract = "The rhesus macaque is an important model species in several
              branches of science, including neuroscience, psychology,
              ethology, and medicine. The utility of the macaque model would be
              greatly enhanced by the ability to precisely measure behavior in
              freely moving conditions. Existing approaches do not provide
              sufficient tracking. Here, we describe OpenMonkeyStudio, a deep
              learning-based markerless motion capture system for estimating 3D
              pose in freely moving macaques in large unconstrained
              environments. Our system makes use of 62 machine vision cameras
              that encircle an open 2.45 m $\times$ 2.45 m $\times$ 2.75 m
              enclosure. The resulting multiview image streams allow for data
              augmentation via 3D-reconstruction of annotated images to train a
              robust view-invariant deep neural network. This view invariance
              represents an important advance over previous markerless 2D
              tracking approaches, and allows fully automatic pose inference on
              unconstrained natural motion. We show that OpenMonkeyStudio can
              be used to accurately recognize actions and track social
              interactions.",
  journal  = "Nat. Commun.",
  volume   =  11,
  number   =  1,
  pages    = "4560",
  month    =  sep,
  year     =  2020,
  language = "en"
}

@ARTICLE{Machado2015-yi,
  title     = "A quantitative framework for whole-body coordination reveals
               specific deficits in freely walking ataxic mice",
  author    = "Machado, Ana S and Darmohray, Dana M and Fayad, Jo{\~a}o and
               Marques, Hugo G and Carey, Megan R",
  abstract  = "The coordination of movement across the body is a fundamental,
               yet poorly understood aspect of motor control. Mutant mice with
               cerebellar circuit defects exhibit characteristic impairments in
               locomotor coordination; however, the fundamental features of
               this gait ataxia have not been effectively isolated. Here we
               describe a novel system (LocoMouse) for analyzing limb, head,
               and tail kinematics of freely walking mice. Analysis of visibly
               ataxic Purkinje cell degeneration (pcd) mice reveals that while
               differences in the forward motion of individual paws are fully
               accounted for by changes in walking speed and body size, more
               complex 3D trajectories and, especially, inter-limb and
               whole-body coordination are specifically impaired. Moreover, the
               coordination deficits in pcd are consistent with a failure to
               predict and compensate for the consequences of movement across
               the body. These results isolate specific impairments in
               whole-body coordination in mice and provide a quantitative
               framework for understanding cerebellar contributions to
               coordinated locomotion.",
  journal   = "Elife",
  publisher = "elifesciences.org",
  volume    =  4,
  month     =  oct,
  year      =  2015,
  keywords  = "Purkinje cell; ataxia; cerebellum; locomotion; mouse;
               neuroscience",
  language  = "en"
}


@ARTICLE{Branson2009-mz,
  title    = "High-throughput ethomics in large groups of Drosophila",
  author   = "Branson, Kristin and Robie, Alice A and Bender, John and Perona,
              Pietro and Dickinson, Michael H",
  abstract = "We present a camera-based method for automatically quantifying
              the individual and social behaviors of fruit flies, Drosophila
              melanogaster, interacting in a planar arena. Our system includes
              machine-vision algorithms that accurately track many individuals
              without swapping identities and classification algorithms that
              detect behaviors. The data may be represented as an ethogram that
              plots the time course of behaviors exhibited by each fly or as a
              vector that concisely captures the statistical properties of all
              behaviors displayed in a given period. We found that behavioral
              differences between individuals were consistent over time and
              were sufficient to accurately predict gender and genotype. In
              addition, we found that the relative positions of flies during
              social interactions vary according to gender, genotype and social
              environment. We expect that our software, which permits
              high-throughput screening, will complement existing molecular
              methods available in Drosophila, facilitating new investigations
              into the genetic and cellular basis of behavior.",
  journal  = "Nat. Methods",
  volume   =  6,
  number   =  6,
  pages    = "451--457",
  month    =  jun,
  year     =  2009,
  language = "en"
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@INPROCEEDINGS{He2016-rp,
  title     = "Deep residual learning for image recognition",
  booktitle = "Proceedings of the {IEEE} conference on computer vision and
               pattern recognition",
  author    = "He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian",
  abstract  = "Deeper neural networks are more difficult to train. We present a
               residual learning framework to ease the training of networks
               that are substantially deeper than those used previously. We
               explicitly reformulate the layers as learning residual functions
               with reference to the layer …",
  publisher = "openaccess.thecvf.com",
  pages     = "770--778",
  year      =  2016
}

@article{petersen2008matrix,
  title={The matrix cookbook},
  author={Petersen, Kaare Brandt and Pedersen, Michael Syskind and others},
  journal={Technical University of Denmark},
  volume={7},
  number={15},
  pages={510},
  year={2008},
  url={https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf}
}

@inproceedings{sutskever2013importance,
  title={On the importance of initialization and momentum in deep learning},
  author={Sutskever, Ilya and Martens, James and Dahl, George and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1139--1147},
  year={2013},
  organization={PMLR}
}

@article{duchi2011adaptive,
  title={Adaptive subgradient methods for online learning and stochastic optimization.},
  author={Duchi, John and Hazan, Elad and Singer, Yoram},
  journal={Journal of Machine Learning Research},
  volume={12},
  number={7},
  year={2011}
}

@article{kingma2014adam,
  title={Adam: {A} method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@misc{rmsprop,
  title={Neural	Networks for Machine Learning Lecture 6a},
  author={Hinton, Geoffrey and Srivasta, Nitish and Swerskey, Kevin},
  url={https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf},
  year=2014,
}

@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@article{marshall2021continuous,
  title={Continuous whole-body 3D kinematic recordings across the rodent behavioral repertoire},
  author={Marshall, Jesse D and Aldarondo, Diego E and Dunn, Timothy W and Wang, William L and Berman, Gordon J and {\"O}lveczky, Bence P},
  journal={Neuron},
  volume={109},
  number={3},
  pages={420--437},
  year={2021},
  publisher={Elsevier}
}

@article{felzenszwalb2005pictorial,
  title={Pictorial structures for object recognition},
  author={Felzenszwalb, Pedro F and Huttenlocher, Daniel P},
  journal={International journal of computer vision},
  volume={61},
  pages={55--79},
  year={2005},
  publisher={Springer}
}

@inproceedings{insafutdinov2016deepercut,
  title={Deepercut: {A} deeper, stronger, and faster multi-person pose estimation model},
  author={Insafutdinov, Eldar and Pishchulin, Leonid and Andres, Bjoern and Andriluka, Mykhaylo and Schiele, Bernt},
  booktitle={Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14},
  pages={34--50},
  year={2016},
  organization={Springer}
}

@article{dunn2021geometric,
  title={Geometric deep learning enables 3D kinematic profiling across species and environments},
  author={Dunn, Timothy W and Marshall, Jesse D and Severson, Kyle S and Aldarondo, Diego E and Hildebrand, David GC and Chettih, Selmaan N and Wang, William L and Gellis, Amanda J and Carlson, David E and Aronov, Dmitriy and others},
  journal={Nature methods},
  volume={18},
  number={5},
  pages={564--573},
  year={2021},
  publisher={Nature Publishing Group US New York}
}

@article{karashchuk2021anipose,
  title={Anipose: a toolkit for robust markerless 3D pose estimation},
  author={Karashchuk, Pierre and Rupp, Katie L and Dickinson, Evyn S and Walling-Bell, Sarah and Sanders, Elischa and Azim, Eiman and Brunton, Bingni W and Tuthill, John C},
  journal={Cell reports},
  volume={36},
  number={13},
  pages={109730},
  year={2021},
  publisher={Elsevier}
}

@article{nath2019using,
  title={Using DeepLabCut for 3D markerless pose estimation across species and behaviors},
  author={Nath, Tanmay and Mathis, Alexander and Chen, An Chi and Patel, Amir and Bethge, Matthias and Mathis, Mackenzie Weygandt},
  journal={Nature protocols},
  volume={14},
  number={7},
  pages={2152--2176},
  year={2019},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{zhang2021animal,
  title={Animal pose estimation from video data with a hierarchical von {M}ises-{F}isher-{G}aussian model},
  author={Zhang, Libby and Dunn, Tim and Marshall, Jesse and Olveczky, Bence and Linderman, Scott},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={2800--2808},
  year={2021},
  organization={PMLR}
}

@article{wu2020deep,
  title={Deep {G}raph {P}ose: a semi-supervised deep graphical model for improved animal pose tracking},
  author={Wu, Anqi and Buchanan, Estefany Kelly and Whiteway, Matthew and Schartner, Michael and Meijer, Guido and Noel, Jean-Paul and Rodriguez, Erica and Everett, Claire and Norovich, Amy and Schaffer, Evan and others},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6040--6052},
  year={2020}
}

@article{whiteway2021partitioning,
  title={Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders},
  author={Whiteway, Matthew R and Biderman, Dan and Friedman, Yoni and Dipoppa, Mario and Buchanan, E Kelly and Wu, Anqi and Zhou, John and Bonacchi, Niccol{\`o} and Miska, Nathaniel J and Noel, Jean-Paul and others},
  journal={PLoS computational biology},
  volume={17},
  number={9},
  pages={e1009439},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{lauer2022multi,
  title={Multi-animal pose estimation, identification and tracking with DeepLabCut},
  author={Lauer, Jessy and Zhou, Mu and Ye, Shaokai and Menegas, William and Schneider, Steffen and Nath, Tanmay and Rahman, Mohammed Mostafizur and Di Santo, Valentina and Soberanes, Daniel and Feng, Guoping and others},
  journal={Nature Methods},
  volume={19},
  number={4},
  pages={496--504},
  year={2022},
  publisher={Nature Publishing Group US New York}
}

@article{markowitz2023spontaneous,
  title={Spontaneous behaviour is structured by reinforcement without explicit reward},
  author={Markowitz, Jeffrey E and Gillis, Winthrop F and Jay, Maya and Wood, Jeffrey and Harris, Ryley W and Cieszkowski, Robert and Scott, Rebecca and Brann, David and Koveal, Dorothy and Kula, Tomasz and others},
  journal={Nature},
  pages={1--10},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{pillow2008spatio,
  title={Spatio-temporal correlations and visual signalling in a complete neuronal population},
  author={Pillow, Jonathan W and Shlens, Jonathon and Paninski, Liam and Sher, Alexander and Litke, Alan M and Chichilnisky, EJ and Simoncelli, Eero P},
  journal={Nature},
  volume={454},
  number={7207},
  pages={995--999},
  year={2008},
  publisher={Nature Publishing Group UK London}
}

@article{mcintosh2016deep,
  title={Deep learning models of the retinal response to natural scenes},
  author={McIntosh, Lane and Maheswaranathan, Niru and Nayebi, Aran and Ganguli, Surya and Baccus, Stephen},
  journal={Advances in Neural Information Processing Systems},
  volume={29},
  year={2016}
}

@article{goris2014partitioning,
  title={Partitioning neuronal variability},
  author={Goris, Robbe LT and Movshon, J Anthony and Simoncelli, Eero P},
  journal={Nature neuroscience},
  volume={17},
  number={6},
  pages={858--865},
  year={2014},
  publisher={Nature Publishing Group US New York}
}

@article{charles2018dethroning,
  title={Dethroning the {F}ano factor: {A} flexible, model-based approach to partitioning neural variability},
  author={Charles, Adam S and Park, Mijung and Weller, J Patrick and Horwitz, Gregory D and Pillow, Jonathan W},
  journal={Neural computation},
  volume={30},
  number={4},
  pages={1012--1045},
  year={2018},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@book{mccullagh2019generalized,
  title={Generalized linear models},
  author={McCullagh, Peter and Nelder, John},
  year={1983},
  publisher={Routledge}
}

@article{paninski2004maximum,
  title={Maximum likelihood estimation of cascade point-process neural encoding models},
  author={Paninski, Liam},
  journal={Network: Computation in Neural Systems},
  volume={15},
  number={4},
  pages={243--262},
  year={2004},
  publisher={Taylor \& Francis}
}

@article{truccolo2005point,
  title={A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects},
  author={Truccolo, Wilson and Eden, Uri T and Fellows, Matthew R and Donoghue, John P and Brown, Emery N},
  journal={Journal of neurophysiology},
  volume={93},
  number={2},
  pages={1074--1089},
  year={2005},
  publisher={American Physiological Society}
}

@article{park2014encoding,
  title={Encoding and decoding in parietal cortex during sensorimotor decision-making},
  author={Park, Il Memming and Meister, Miriam LR and Huk, Alexander C and Pillow, Jonathan W},
  journal={Nature neuroscience},
  volume={17},
  number={10},
  pages={1395--1403},
  year={2014},
  publisher={Nature Publishing Group US New York}
}

@article{yates2017functional,
  title={Functional dissection of signal and noise in {MT} and {LIP} during decision-making},
  author={Yates, Jacob L and Park, Il Memming and Katz, Leor N and Pillow, Jonathan W and Huk, Alexander C},
  journal={Nature neuroscience},
  volume={20},
  number={9},
  pages={1285--1292},
  year={2017},
  publisher={Nature Publishing Group US New York}
}

@article{hardcastle2017multiplexed,
  title={A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex},
  author={Hardcastle, Kiah and Maheswaranathan, Niru and Ganguli, Surya and Giocomo, Lisa M},
  journal={Neuron},
  volume={94},
  number={2},
  pages={375--387},
  year={2017},
  publisher={Elsevier}
}

@article{vidne2012modeling,
  title={Modeling the impact of common noise inputs on the network activity of retinal ganglion cells},
  author={Vidne, Michael and Ahmadian, Yashar and Shlens, Jonathon and Pillow, Jonathan W and Kulkarni, Jayant and Litke, Alan M and Chichilnisky, EJ and Simoncelli, Eero and Paninski, Liam},
  journal={Journal of computational neuroscience},
  volume={33},
  pages={97--121},
  year={2012},
  publisher={Springer}
}

@article{freeman2015mapping,
  title={Mapping nonlinear receptive field structure in primate retina at single cone resolution},
  author={Freeman, Jeremy and Field, Greg D and Li, Peter H and Greschner, Martin and Gunning, Deborah E and Mathieson, Keith and Sher, Alexander and Litke, Alan M and Paninski, Liam and Simoncelli, Eero P and others},
  journal={Elife},
  volume={4},
  pages={e05241},
  year={2015},
  publisher={eLife Sciences Publications, Ltd}
}

@article{ramirez2014fast,
  title={Fast inference in generalized linear models via expected log-likelihoods},
  author={Ramirez, Alexandro D and Paninski, Liam},
  journal={Journal of computational neuroscience},
  volume={36},
  pages={215--234},
  year={2014},
  publisher={Springer}
}

@article{zoltowski2018scaling,
  title={Scaling the {P}oisson GLM to massive neural datasets through polynomial approximations},
  author={Zoltowski, David and Pillow, Jonathan W},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{linderman2016bayesian,
  title={Bayesian latent structure discovery from multi-neuron recordings},
  author={Linderman, Scott and Adams, Ryan P and Pillow, Jonathan W},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@article{weber2017capturing,
  title={Capturing the dynamical repertoire of single neurons with generalized linear models},
  author={Weber, Alison I and Pillow, Jonathan W},
  journal={Neural computation},
  volume={29},
  number={12},
  pages={3260--3289},
  year={2017},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@article{latimer2019inferring,
  title={Inferring synaptic inputs from spikes with a conductance-based neural encoding model},
  author={Latimer, Kenneth W and Rieke, Fred and Pillow, Jonathan W},
  journal={Elife},
  volume={8},
  pages={e47012},
  year={2019},
  publisher={eLife Sciences Publications, Ltd}
}

@article{hawkes1971spectra,
  title={Spectra of some self-exciting and mutually exciting point processes},
  author={Hawkes, Alan G},
  journal={Biometrika},
  volume={58},
  number={1},
  pages={83--90},
  year={1971},
  publisher={Oxford University Press}
}

@inproceedings{linderman2014discovering,
  title={Discovering latent network structure in point process data},
  author={Linderman, Scott and Adams, Ryan},
  booktitle={International conference on machine learning},
  pages={1413--1421},
  year={2014},
  organization={PMLR}
}

@article{zhou2019scalable,
  title={Scalable Inference for Nonparametric {H}awkes Process Using {P}\'{o}$ lya-gamma Augmentation},
  author={Zhou, Feng and Li, Zhidong and Fan, Xuhui and Wang, Yang and Sowmya, Arcot and Chen, Fang},
  journal={arXiv preprint arXiv:1910.13052},
  year={2019}
}

@book{kingman1992poisson,
  title={Poisson processes},
  author={Kingman, John Frank Charles},
  volume={3},
  year={1992},
  publisher={Clarendon Press}
}

@article{rabiner1986introduction,
  title={An introduction to hidden {M}arkov models},
  author={Rabiner, Lawrence and Juang, Biinghwang},
  journal={ieee assp magazine},
  volume={3},
  number={1},
  pages={4--16},
  year={1986},
  publisher={IEEE}
}