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Large-Scale Patterns in a Minimal Cognitive Flocking Model: Incidental Leaders, Nematic Patterns, and Aggregates

Lucas Barberis and Fernando Peruani
Phys. Rev. Lett. 117, 248001 – Published 6 December 2016
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Abstract

We study a minimal cognitive flocking model, which assumes that the moving entities navigate using the available instantaneous visual information exclusively. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force, which acts inside a vision cone (VC), and lack velocity-velocity alignment. We show that this active system can exhibit—due to the VC that breaks Newton’s third law—various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or millinglike patterns, the formation of moving—locally polar—files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and nonlinear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and nonliving active systems.

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  • Received 11 June 2016

DOI:https://doi.org/10.1103/PhysRevLett.117.248001

© 2016 American Physical Society

Physics Subject Headings (PhySH)

Polymers & Soft MatterPhysics of Living SystemsInterdisciplinary Physics

Synopsis

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Flocks Without Memory

Published 8 December 2016

Moving particles with no memory can group together in complex flock configurations using only instantaneous cues.  

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Authors & Affiliations

Lucas Barberis1,2 and Fernando Peruani1,*

  • 1Université Côte d’Azur, Laboratoire J.A. Dieudonné, UMR 7351 CNRS, Parc Valrose, F-06108 Nice Cedex 02, France
  • 2IFEG, FaMAF, CONICET, UNC, X5000HUA Córdoba, Argentina

  • *peruani@unice.fr

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Issue

Vol. 117, Iss. 24 — 9 December 2016

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