Harnessing Synthetic Active Particles for Physical Reservoir Computing
Xiangzun Wang Frank Cichos
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.