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The processing of information is an indispensable property of living systemsrealized by networks of active processes with enormous complexity. They haveinspired many variants of modern machine learning one of them being reservoircomputing, in which stimulating a network of nodes with fading memory enablescomputations and complex predictions. Reservoirs are implemented on computerhardware, but also on unconventional physical substrates such as mechanicaloscillators, spins, or bacteria often summarized as physical reservoircomputing. Here we demonstrate physical reservoir computing with a syntheticactive microparticle system that self-organizes from an active and passivecomponent into inherently noisy nonlinear dynamical units. Theself-organization and dynamical response of the unit is the result of a delayedpropulsion of the microswimmer to a passive target. A reservoir of such unitswith a self-coupling via the delayed response can perform predictive tasksdespite the strong noise resulting from Brownian motion of the microswimmers.To achieve efficient noise suppression, we introduce a special architecturethat uses historical reservoir states for output. Our results pave the way forthe study of information processing in synthetic self-organized active particlesystems.

Harnessing Synthetic Active Particles for Physical Reservoir Computing