发现活性物质计算的鲁棒最优动态，提升混沌信号预测能力。
Robustly optimal dynamics for active matter reservoir computing
- 系统在临界阻尼略下方实现多阶段弛豫，具高效信息处理能力。
- 该动态在多种物理参数与任务下均表现最优，且可单粒子读出。
- 适用于研究非平衡多体物理中的非常规计算与学习机制。
在储层计算（RC）范式下研究活性物质的信息处理能力，以推断混沌信号的未来状态。我们发现了一个此前被忽视的异常动力学区域，该区域在多种条件下均表现出鲁棒最优性能，为物理系统计算提供了重要启示。有效信息处理的关键在于系统的内在弛豫能力，这些能力在不强制特定推理目标的情况下被探测。实现最优计算的动力学区域位于临界阻尼阈值略下方，具有多阶段弛豫特征，可在单粒子层面读取。在多体层面，该系统对不同物理参数和推理任务均展现出鲁棒最优的储层计算基底。系统在此区域内对高度波动的驱动力表现出丰富的动态机制，个体动力学相关性揭示了响应系统与驱动力间的紧密关联。由于该模型具有物理解释性，有助于以新的视角重新思考非平衡多体物理中的学习与非常规计算问题。
原文摘要 · Abstract (English)
Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system's intrinsic relaxation abilities. These are probed without actually enforcing a specific inference goal. The dynamical regime that achieves optimal computation is located just below a critical damping threshold, involving a relaxation with multiple stages, and is readable at the single-particle level. At the many-body level, it yields substrates robustly optimal for RC across varying physical parameters and inference tasks. A system in this regime exhibits a strong diversity of dynamic mechanisms under highly fluctuating driving forces. Correlations of agent dynamics can express a tight relationship between the responding system and the fluctuating forces driving it. As this model is interpretable in physical terms, it facilitates re-framing inquiries regarding learning and unconventional computing with a fresh rationale for many-body physics out of equilibrium.
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