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[AP14]

David J Anderson and Pietro Perona. Toward a science of computational ethology. Neuron, 84(1):18–31, October 2014.

[BEY+20]

Praneet C Bala, Benjamin R Eisenreich, Seng Bum Michael Yoo, Benjamin Y Hayden, Hyun Soo Park, and Jan Zimmermann. Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio. Nat. Commun., 11(1):4560, September 2020.

[Bis06]

Christopher M Bishop. Pattern Recognition and Machine Learning. Springer, 2006. URL: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf.

[BNJ03]

David M Blei, Andrew Y Ng, and Michael I Jordan. Latent Dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003.

[BRB+09]

Kristin Branson, Alice A Robie, John Bender, Pietro Perona, and Michael H Dickinson. High-throughput ethomics in large groups of drosophila. Nat. Methods, 6(6):451–457, June 2009.

[CPW+18]

Adam S Charles, Mijung Park, J Patrick Weller, Gregory D Horwitz, and Jonathan W Pillow. Dethroning the Fano factor: A flexible, model-based approach to partitioning neural variability. Neural computation, 30(4):1012–1045, 2018.

[CMB+17]

Jason E Chung, Jeremy F Magland, Alex H Barnett, Vanessa M Tolosa, Angela C Tooker, Kye Y Lee, Kedar G Shah, Sarah H Felix, Loren M Frank, and Leslie F Greengard. A fully automated approach to spike sorting. Neuron, 95(6):1381–1394, 2017.

[DAB+19]

Sandeep Robert Datta, David J Anderson, Kristin Branson, Pietro Perona, and Andrew Leifer. Computational neuroethology: a call to action. Neuron, 104(1):11–24, October 2019.

[DA05]

Peter Dayan and Laurence F Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT press, 2005.

[DLK+15]

Xinyi Deng, Daniel F Liu, Kenneth Kay, Loren M Frank, and Uri T Eden. Clusterless decoding of position from multiunit activity using a marked point process filter. Neural computation, 27(7):1438–1460, 2015.

[DLJ08]

Chris HQ Ding, Tao Li, and Michael I Jordan. Convex and semi-nonnegative matrix factorizations. IEEE transactions on pattern analysis and machine intelligence, 32(1):45–55, 2008.

[DHS11]

John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2011.

[DMS+21]

Timothy W Dunn, Jesse D Marshall, Kyle S Severson, Diego E Aldarondo, David GC Hildebrand, Selmaan N Chettih, William L Wang, Amanda J Gellis, David E Carlson, Dmitriy Aronov, and others. Geometric deep learning enables 3d kinematic profiling across species and environments. Nature methods, 18(5):564–573, 2021.

[FH05]

Pedro F Felzenszwalb and Daniel P Huttenlocher. Pictorial structures for object recognition. International journal of computer vision, 61:55–79, 2005.

[FFL+15]

Jeremy Freeman, Greg D Field, Peter H Li, Martin Greschner, Deborah E Gunning, Keith Mathieson, Alexander Sher, Alan M Litke, Liam Paninski, Eero P Simoncelli, and others. Mapping nonlinear receptive field structure in primate retina at single cone resolution. Elife, 4:e05241, 2015.

[GFG+19]

Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L Gauthier, Pengcheng Zhou, Baljit S Khakh, David W Tank, Dmitri B Chklovskii, and Eftychios A Pnevmatikakis. CaImAn an open source tool for scalable calcium imaging data analysis. Elife, January 2019.

[GHKB06]

Carl Gold, Darrell A Henze, Christof Koch, and Gyorgy Buzsaki. On the origin of the extracellular action potential waveform: a modeling study. Journal of neurophysiology, 95(5):3113–3128, 2006.

[GBC16]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

[GB13]

Prem K Gopalan and David M Blei. Efficient discovery of overlapping communities in massive networks. Proceedings of the National Academy of Sciences, 110(36):14534–14539, 2013.

[GMS14]

Robbe LT Goris, J Anthony Movshon, and Eero P Simoncelli. Partitioning neuronal variability. Nature neuroscience, 17(6):858–865, 2014.

[GCN+19]

Jacob M Graving, Daniel Chae, Hemal Naik, Liang Li, Benjamin Koger, Blair R Costelloe, and Iain D Couzin. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. Elife, October 2019.

[HMGG17]

Kiah Hardcastle, Niru Maheswaranathan, Surya Ganguli, and Lisa M Giocomo. A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron, 94(2):375–387, 2017.

[HTFF09]

Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Volume 2. Springer, 2009.

[Haw71]

Alan G Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83–90, 1971.

[HZRS16]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778. openaccess.thecvf.com, 2016.

[HSS14]

Geoffrey Hinton, Nitish Srivasta, and Kevin Swerskey. Neural networks for machine learning lecture 6a. 2014. URL: https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.

[HH52]

Alan L Hodgkin and Andrew F Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4):500, 1952.

[IPA+16]

Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, and Bernt Schiele. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14, 34–50. Springer, 2016.

[JW18]

Sean Jewell and Daniela Witten. Exact spike train inference via $\ell _\0\$ optimization. Annals of Applied Statistics, 12(4):2457–2482, December 2018.

[JSS+17]

James J Jun, Nicholas A Steinmetz, Joshua H Siegle, Daniel J Denman, Marius Bauza, Brian Barbarits, Albert K Lee, Costas A Anastassiou, Alexandru Andrei, Çağatay Aydın, Mladen Barbic, Timothy J Blanche, Vincent Bonin, João Couto, Barundeb Dutta, Sergey L Gratiy, Diego A Gutnisky, Michael Häusser, Bill Karsh, Peter Ledochowitsch, Carolina Mora Lopez, Catalin Mitelut, Silke Musa, Michael Okun, Marius Pachitariu, Jan Putzeys, P Dylan Rich, Cyrille Rossant, Wei-Lung Sun, Karel Svoboda, Matteo Carandini, Kenneth D Harris, Christof Koch, John O'Keefe, and Timothy D Harris. Fully integrated silicon probes for high-density recording of neural activity. Nature, 551(7679):232–236, November 2017.

[KRD+21]

Pierre Karashchuk, Katie L Rupp, Evyn S Dickinson, Sarah Walling-Bell, Elischa Sanders, Eiman Azim, Bingni W Brunton, and John C Tuthill. Anipose: a toolkit for robust markerless 3d pose estimation. Cell reports, 36(13):109730, 2021.

[KB14]

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

[Kin92]

John Frank Charles Kingman. Poisson processes. Volume 3. Clarendon Press, 1992.

[LRP19]

Kenneth W Latimer, Fred Rieke, and Jonathan W Pillow. Inferring synaptic inputs from spikes with a conductance-based neural encoding model. Elife, 8:e47012, 2019.

[LZY+22]

Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, and others. Multi-animal pose estimation, identification and tracking with deeplabcut. Nature Methods, 19(4):496–504, 2022.

[LS99]

Daniel D Lee and H Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, 1999.

[LMS+20]

JinHyung Lee, Catalin Mitelut, Hooshmand Shokri, Ian Kinsella, Nishchal Dethe, Shenghao Wu, Kevin Li, Eduardo B Reyes, Denis Turcu, Eleanor Batty, and others. YASS: Yet another spike sorter applied to large-scale multi-electrode array recordings in primate retina. bioRxiv, 2020.

[LS16]

Michael Z Lin and Mark J Schnitzer. Genetically encoded indicators of neuronal activity. Nat. Neurosci., 19(9):1142–1153, August 2016.

[LA14]

Scott Linderman and Ryan Adams. Discovering latent network structure in point process data. In International conference on machine learning, 1413–1421. PMLR, 2014.

[LAP16]

Scott Linderman, Ryan P Adams, and Jonathan W Pillow. Bayesian latent structure discovery from multi-neuron recordings. Advances in neural information processing systems, 2016.

[Luo20]

Liqun Luo. Principles of Neurobiology. Garland Science, 2020.

[MDF+15]

Ana S Machado, Dana M Darmohray, João Fayad, Hugo G Marques, and Megan R Carey. A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice. Elife, October 2015.

[MGJ+23]

Jeffrey E Markowitz, Winthrop F Gillis, Maya Jay, Jeffrey Wood, Ryley W Harris, Robert Cieszkowski, Rebecca Scott, David Brann, Dorothy Koveal, Tomasz Kula, and others. Spontaneous behaviour is structured by reinforcement without explicit reward. Nature, pages 1–10, 2023.

[MAD+21]

Jesse D Marshall, Diego E Aldarondo, Timothy W Dunn, William L Wang, Gordon J Berman, and Bence P Ölveczky. Continuous whole-body 3d kinematic recordings across the rodent behavioral repertoire. Neuron, 109(3):420–437, 2021.

[MMC+18]

Alexander Mathis, Pranav Mamidanna, Kevin M Cury, Taiga Abe, Venkatesh N Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci., 21(9):1281–1289, August 2018.

[MN83]

Peter McCullagh and John Nelder. Generalized linear models. Routledge, 1983.

[MMN+16]

Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, and Stephen Baccus. Deep learning models of the retinal response to natural scenes. Advances in Neural Information Processing Systems, 2016.

[MS07]

Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. Advances in neural information processing systems, 2007.

[Mur23]

Kevin P. Murphy. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023. URL: http://probml.github.io/book2.

[NMC+19]

Tanmay Nath, Alexander Mathis, An Chi Chen, Amir Patel, Matthias Bethge, and Mackenzie Weygandt Mathis. Using deeplabcut for 3d markerless pose estimation across species and behaviors. Nature protocols, 14(7):2152–2176, 2019.

[PSS23]

Marius Pachitariu, Shashwat Sridhar, and Carsen Stringer. Solving the spike sorting problem with kilosort. bioRxiv, 2023.

[PSK+16]

Marius Pachitariu, Nicholas Steinmetz, Shabnam Kadir, Matteo Carandini, and others. Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels. BioRxiv, pages 061481, 2016.

[PSD+17]

Marius Pachitariu, Carsen Stringer, Mario Dipoppa, Sylvia Schröder, L Federico Rossi, Henry Dalgleish, Matteo Carandini, and Kenneth D Harris. Suite2p: Beyond 10,000 neurons with standard two-photon microscopy. BioRxiv, pages 061507, 2017.

[Pan04]

Liam Paninski. Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems, 15(4):243–262, 2004.

[PMHP14]

Il Memming Park, Miriam LR Meister, Alexander C Huk, and Jonathan W Pillow. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature neuroscience, 17(10):1395–1403, 2014.

[PTM+22]

Talmo D Pereira, Nathaniel Tabris, Arie Matsliah, David M Turner, Junyu Li, Shruthi Ravindranath, Eleni S Papadoyannis, Edna Normand, David S Deutsch, Z Yan Wang, and others. SLEAP: A deep learning system for multi-animal pose tracking. Nature methods, 19(4):486–495, 2022.

[PP+08]

Kaare Brandt Petersen, Michael Syskind Pedersen, and others. The matrix cookbook. Technical University of Denmark, 7(15):510, 2008. URL: https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf.

[PSP+08]

Jonathan W Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M Litke, EJ Chichilnisky, and Eero P Simoncelli. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207):995–999, 2008.

[PSG+16]

Eftychios A Pnevmatikakis, Daniel Soudry, Yuanjun Gao, Timothy A Machado, Josh Merel, David Pfau, Thomas Reardon, Yu Mu, Clay Lacefield, Weijian Yang, and others. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron, 2016.

[RJ86]

Lawrence Rabiner and Biinghwang Juang. An introduction to hidden Markov models. ieee assp magazine, 3(1):4–16, 1986.

[RP14]

Alexandro D Ramirez and Liam Paninski. Fast inference in generalized linear models via expected log-likelihoods. Journal of computational neuroscience, 36:215–234, 2014.

[RG99]

Sam Roweis and Zoubin Ghahramani. A unifying review of linear Gaussian models. Neural computation, 11(2):305–345, 1999.

[SAL+21]

Nicholas A Steinmetz, Cagatay Aydin, Anna Lebedeva, Michael Okun, Marius Pachitariu, Marius Bauza, Maxime Beau, Jai Bhagat, Claudia Böhm, Martijn Broux, and others. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science, 372(6539):eabf4588, 2021.

[SMDH13]

Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In International conference on machine learning, 1139–1147. PMLR, 2013.

[Tin63]

N Tinbergen. On aims and methods of ethology. Z. Tierpsychol., 20(4):410–433, 1963.

[TB99]

Michael E Tipping and Christopher M Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611–622, 1999.

[TSL+19]

Eric M Trautmann, Sergey D Stavisky, Subhaneil Lahiri, Katherine C Ames, Matthew T Kaufman, Daniel J O’Shea, Saurabh Vyas, Xulu Sun, Stephen I Ryu, Surya Ganguli, and others. Accurate estimation of neural population dynamics without spike sorting. Neuron, 103(2):292–308, 2019.

[TEF+05]

Wilson Truccolo, Uri T Eden, Matthew R Fellows, John P Donoghue, and Emery N Brown. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of neurophysiology, 93(2):1074–1089, 2005.

[VAS+12]

Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W Pillow, Jayant Kulkarni, Alan M Litke, EJ Chichilnisky, Eero Simoncelli, and Liam Paninski. Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. Journal of computational neuroscience, 33:97–121, 2012.

[VPM+10]

Joshua T Vogelstein, Adam M Packer, Timothy A Machado, Tanya Sippy, Baktash Babadi, Rafael Yuste, and Liam Paninski. Fast nonnegative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 104(6):3691–3704, 2010.

[WP17]

Alison I Weber and Jonathan W Pillow. Capturing the dynamical repertoire of single neurons with generalized linear models. Neural computation, 29(12):3260–3289, 2017.

[WBF+21]

Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J Miska, Jean-Paul Noel, and others. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS computational biology, 17(9):e1009439, 2021.

[WBW+20a]

Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, and others. Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. Advances in Neural Information Processing Systems, 33:6040–6052, 2020.

[WBW+20b]

Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, and Others. Deep graph pose: a semi-supervised deep graphical model for improved animal pose tracking. Adv. Neural Inf. Process. Syst., 2020.

[YPK+17]

Jacob L Yates, Il Memming Park, Leor N Katz, Jonathan W Pillow, and Alexander C Huk. Functional dissection of signal and noise in MT and LIP during decision-making. Nature neuroscience, 20(9):1285–1292, 2017.

[ZDM+21]

Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, and Scott Linderman. Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. In International Conference on Artificial Intelligence and Statistics, 2800–2808. PMLR, 2021.

[ZLF+19]

Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, and Fang Chen. Scalable inference for nonparametric Hawkes process using Pó$ lya-gamma augmentation. arXiv preprint arXiv:1910.13052, 2019.

[ZRRR+18]

Pengcheng Zhou, Shanna L Resendez, Jose Rodriguez-Romaguera, Jessica C Jimenez, Shay Q Neufeld, Andrea Giovannucci, Johannes Friedrich, Eftychios A Pnevmatikakis, Garret D Stuber, Rene Hen, and others. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. elife, 7:e28728, 2018.

[ZP18]

David Zoltowski and Jonathan W Pillow. Scaling the Poisson glm to massive neural datasets through polynomial approximations. Advances in neural information processing systems, 2018.