Harnessing Synthetic Active Particles for Physical Reservoir Computing

TL;DR

This paper demonstrates that synthetic active microparticles can be used as physical reservoirs for computing, leveraging their self-organizing nonlinear dynamics and delayed responses to perform predictive tasks despite inherent noise.

Contribution

It introduces a novel physical reservoir computing system based on synthetic active particles, highlighting their self-organization and noise resilience for information processing.

Findings

Reservoir of active particles can perform predictive tasks.

Self-organization enables nonlinear dynamical units.

Historical states improve noise suppression and computation accuracy.

Abstract

The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit is the result of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.