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.

翻译：信息处理是生命系统不可或缺的属性，由具有极大复杂性的活性过程网络实现。这启发了现代机器学习的多种变体，其中之一是储备池计算——通过刺激具有衰减记忆的节点网络，能够实现计算和复杂预测。储备池不仅在计算机硬件上实现，也基于非常规物理基底（如机械振荡器、自旋或细菌）实现，通常被称为物理储备池计算。本文中，我们展示了一种合成活性微粒子系统的物理储备池计算，该系统由活性组分与非活性组分自组织形成固有噪声的非线性动力学单元。该单元的自组织与动力学响应源于微型游泳器对被动目标的延迟推进。通过延迟响应实现自耦合的此类单元储备池，尽管存在由微型游泳器布朗运动引起的强烈噪声，仍能执行预测任务。为实现高效噪声抑制，我们引入了一种利用历史储备池状态进行输出的特殊架构。我们的结果为研究合成自组织活性粒子系统中的信息处理开辟了道路。