paper.bib

@software{vispy,
  author    = {Luke Campagnola and
               Eric Larson and
               Almar Klein and
               David Hoese and
               Siddharth and
               Cyrille Rossant and
               Adam Griffiths and
               Nicolas P. Rougier and
               asnt and
               Kai Mühlbauer and
               Alexander Taylor and
               MSS and
               Talley Lambert and
               sylm21 and
               Alex J. Champandard and
               Max Hunter and
               Thomas Robitaille and
               Mustafa Furkan Kaptan and
               Elliott Sales de Andrade and
               Karl Czajkowski and
               Lorenzo Gaifas and
               Alessandro Bacchini and
               Guillaume Favelier and
               Etienne Combrisson and
               ThenTech and
               fschill and
               Mark Harfouche and
               Michael Aye and
               Casper van Elteren and
               Cedric GESTES},
  title     = {vispy/vispy: Version 0.11.0},
  month     = jul,
  year      = 2022,
  publisher = {Zenodo},
  version   = {v0.11.0},
  doi       = {10.5281/zenodo.6795163},
  url       = {https://doi.org/10.5281/zenodo.6795163}
}



@article{bento,
  abstract      = {The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation---a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.},
  article_type  = {journal},
  author        = {Segalin, Cristina and Williams, Jalani and Karigo, Tomomi and Hui, May and Zelikowsky, Moriel and Sun, Jennifer J and Perona, Pietro and Anderson, David J and Kennedy, Ann},
  citation      = {eLife 2021;10:e63720},
  date-modified = {2022-10-05 13:18:49 -0400},
  doi           = {10.7554/eLife.63720},
  editor        = {Berman, Gordon J and Wassum, Kate M and Gal, Asaf},
  issn          = {2050-084X},
  journal       = {eLife},
  keywords      = {social behavior, pose estimation, machine learning, computer vision, microendoscopic imaging, software},
  month         = {nov},
  pages         = {e63720},
  pub_date      = {2021-11-30},
  publisher     = {eLife Sciences Publications, Ltd},
  title         = {The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice},
  url           = {https://doi.org/10.7554/eLife.63720},
  volume        = 10,
  year          = 2021,
  bdsk-url-1    = {https://doi.org/10.7554/eLife.63720}
}

@article {Stringer2023.07.25.550571,
 author = {Carsen Stringer and Lin Zhong and Atika Syeda and Fengtong Du and Maria Kesa and Marius Pachitariu},
 title = {Rastermap: a discovery method for neural population recordings},
 elocation-id = {2023.07.25.550571},
 year = {2023},
 doi = {10.1101/2023.07.25.550571},
 publisher = {Cold Spring Harbor Laboratory},
 URL = {https://www.biorxiv.org/content/early/2023/08/07/2023.07.25.550571},
 eprint = {https://www.biorxiv.org/content/early/2023/08/07/2023.07.25.550571.full.pdf},
 journal = {bioRxiv}
}

@software{vidio,
  author = {Bohnslav, Jim},
  title = {VidIO: Simple, performant video reading and writing in python},
  url = {https://github.com/jbohnslav/vidio},
  version = {0.0.4},
  date = {2024-03-11},
}

@article{umap, 
   doi = {10.21105/joss.00861},
   url = {https://doi.org/10.21105/joss.00861},
   year = {2018}, publisher = {The Open Journal},
   volume = {3},
   number = {29},
   pages = {861},
   author = {Leland McInnes and John Healy and Nathaniel Saul and Lukas Großberger},
   title = {UMAP: Uniform Manifold Approximation and Projection},
   journal = {Journal of Open Source Software}
} 

@misc{petrucco_2020_3925903,
  author    = {Petrucco, Luigi},
  title     = {Mouse head schema},
  month     = jul,
  year      = 2020,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.3925903},
  url       = {https://doi.org/10.5281/zenodo.3925903}
}


@article{NWB,
  article_type = {journal},
  title        = {The Neurodata Without Borders ecosystem for neurophysiological data science},
  author       = {Rübel, Oliver and Tritt, Andrew and Ly, Ryan and Dichter, Benjamin K and Ghosh, Satrajit and Niu, Lawrence and Baker, Pamela and Soltesz, Ivan and Ng, Lydia and Svoboda, Karel and Frank, Loren and Bouchard, Kristofer E},
  editor       = {Colgin, Laura L and Jadhav, Shantanu P},
  volume       = 11,
  year         = 2022,
  month        = {oct},
  pub_date     = {2022-10-04},
  pages        = {e78362},
  citation     = {eLife 2022;11:e78362},
  doi          = {10.7554/eLife.78362},
  url          = {https://doi.org/10.7554/eLife.78362},
  abstract     = {The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.},
  keywords     = {Neurophysiology, data ecosystem, data language, data standard, FAIR data, archive},
  journal      = {eLife},
  issn         = {2050-084X},
  publisher    = {eLife Sciences Publications, Ltd}
}

@dataset{weinreb_2024_10578025,
  author       = {Weinreb, Caleb and
                  Osman, Mohammed Abdal Monium and
                  Jay, Maya and
                  Datta, Sandeep Robert},
  title        = {Systems Neuro Browser (SNUB) example datasets},
  month        = jan,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.10578025},
  url          = {https://doi.org/10.5281/zenodo.10578025}
}