# Harnessing Synthetic Active Particles for Physical Reservoir Computing

arXiv: https://arxiv.org/abs/2307.15010 · HTML: https://mlsanity.com/paper/2307.15010/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 65 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning](https://mlsanity.com/paper/1711.05826/) (2017)
- [Dimension of Reservoir Computers](https://mlsanity.com/paper/1912.06472/) (2019)
- [Functional importance of noise in neuronal information processing](https://mlsanity.com/paper/1812.09897/) (2018)

## Built on by (most cited first, 3 of 3)

- [Training deep physical neural networks with local physical information bottleneck](https://mlsanity.com/paper/2602.09569/) (2026)
- [OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing](https://mlsanity.com/paper/2604.07423/) (2026)
- [Vestibular reservoir computing](https://mlsanity.com/paper/2604.09943/) (2026)

## Abstract (arXiv)

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.