Harnessing synthetic active particles for physical reservoir computing

Frank Cichos; Xiangzun Wang

doi:10.1117/12.3027975

4 October 2024 Harnessing synthetic active particles for physical reservoir computing

Frank Cichos, Xiangzun Wang

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180C (2024) https://doi.org/10.1117/12.3027975
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

Information processing is vital for living systems and involves complex networks of active processes. These systems have influenced various forms of modern machine learning, including reservoir computing. Reservoir computing utilizes networks of nodes with fading memory to perform computations and make complex predictions. Reservoirs can be implemented on computer hardware or unconventional physical substrates like mechanical oscillators, spins, or bacteria, known as physical reservoir computing. 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 the Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce an architecture that uses historical reservoir states for output. We discuss the node and collective reservoir dynamics.

Conference Presentation

Citation Download Citation

Frank Cichos and Xiangzun Wang "Harnessing synthetic active particles for physical reservoir computing", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180C (4 October 2024); https://doi.org/10.1117/12.3027975

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