@article{2e2994ee0a5a46a099b273e6164603d9,
title = "Collective behaviour across animal species",
abstract = "We posit a new geometric perspective to define, detect, and classify inherent patterns of collective behaviour across a variety of animal species. We show that machine learning techniques, and specifically the isometric mapping algorithm, allow the identification and interpretation of different types of collective behaviour in five social animal species. These results offer a first glimpse at the transformative potential of machine learning for ethology, similar to its impact on robotics, where it enabled robots to recognize objects and navigate the environment.",
author = "Pietro Delellis and Giovanni Polverino and Gozde Ustuner and Nicole Abaid and Simone Macr{\`i} and Bollt, {Erik M.} and Maurizio Porfiri",
note = "Funding Information: This work was supported by the National Science Foundation under Grant Nos. CMMI-1129820, CMMI-1129859, and CMMI-0745753. The authors would like to thank Dr. Maria Luisa Scattoni, for the insightful discussions and advice on the experiments, Dr. Kurt Becker, for the careful reading of the manuscript and the valuable feedback, and members of the Department of Mechanical and Aerospace Department of NYU-Poly, for participating in the experiments as human observers. Copyright: Copyright 2014 Elsevier B.V., All rights reserved.",
year = "2014",
month = jan,
day = "16",
doi = "10.1038/srep03723",
language = "English (US)",
volume = "4",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
}