Pietro DeLellis, Giovanni Polverino, Gozde Ustuner, Nicole Abaid, Simone Macrì, Erik M Bollt, Maurizio Porfiri
Author Information
Pietro DeLellis: 1] Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples 80125, Italy [2] Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, Brooklyn, New York 11201, USA.
Giovanni Polverino: Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, Brooklyn, New York 11201, USA.
Gozde Ustuner: Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, Brooklyn, New York 11201, USA.
Nicole Abaid: Department of Engineering Science and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.
Simone Macrì: Section of Behavioural Neuroscience, Department of Cell Biology and Neuroscience, Istituto Superiore di Sanità, Roma 00161, Italy.
Erik M Bollt: Department of Mathematics, Clarkson University, Potsdam, New York 13699, USA.
Maurizio Porfiri: Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, Brooklyn, New York 11201, USA.
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.
References
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Feb;87(2):022818
[PMID: 23496579]
