Allocentric flocking

Mohammad Salahshour

doi:10.1038/s41467-025-64676-5

Allocentric flocking

2025-10-13

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Mohammad Salahshour *

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Iain D. Couzin

DOI：10.1038/s41467-025-64676-5

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Understanding how group-level dynamics arise from individual interactions remains a core challenge in collective behavior research. Traditional models assume animals follow simple behavioral rules, like explicitly aligning with neighbors. We present here an alternative theoretical framework that considers collective behavior to be grounded in neurobiological principles—particularly that animals employ ring attractor networks to encode bearings towards objects in space in an allocentric (i.e., with respect to a fixed external reference frame, such as a stable landmark) and/or egocentric (i.e., the angle relative to the animal’s heading) neural coding. We find collective motion can emerge spontaneously when individuals act as sensory inputs to each other’s networks, but only if individuals employ allocentric bearings to neighbors. Rapid switching between both representations can, however, enhance coordination. Collective motion can, therefore, emerge directly from navigational circuits, and thus may readily evolve, without requiring explicit alignment, or additional rules of interaction. Animals are often thought to follow simple alignment rules, but this study explores how collective behavior could instead emerge from neural ring-attractor networks encoding allocentric and egocentric bearings. The results show that group motion arises spontaneously when allocentric bearings are used, with rapid switching between the two representations further boosting coordination.

Keywords:

Animal migration

Behavioural ecology

Computational models

Dynamical systems

Statistical physics

Science

Humanities and Social Sciences

multidisciplinary

Allocentric flocking

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