Active Particles Bound by Information Flows

TL;DR

This paper explores how active microparticles can self-organize through real-time feedback based on their positions, leading to complex structures influenced by information flows, with implications for understanding collective behavior and designing adaptive systems.

Contribution

It introduces a novel approach where information-based feedback controls active particles, enabling new self-organization phenomena and potential applications in machine learning and swarm intelligence.

Findings

Particles form frustrated geometries under feedback control

Structures exhibit internal dynamical degrees of freedom

Feedback systems can be designed with arbitrary information processing

Abstract

Self-organization is the generation of order out of local interactions in non-equilibrium [1]. It is deeply connected to all fields of science from physics, chemistry to biology where functional living structures self-assemble[2] and constantly evolve[3] all based on physical interactions. The emergence of collective animal behavior[4], of society or language are the results of self-organization processes as well though they involve abstract interactions arising from sensory inputs, information processing, storage and feedback[5-7]. Resulting collective behaviors are found for example in crowds of people, flocks of birds, schools of fish or swarms of bacteria[8,9]. Here we introduce such information based interactions to the behavior of active microparticles. A real time feedback of active particle positions controls the propulsion direction these active particles. The emerging structures are bound by dissipation and reveal frustrated geometries due to confinement to two dimensions. They diffuse like passive clusters of colloids, but possess internal dynamical degrees of freedom that are determined by the feed- back and the noise in the system. As the information processing in the feedback loops can be designed almost arbitrarily, new perspectives for self-organization studies involving coupled feedback systems with separate timescales, machine learning and swarm intelligence arise.