标题： 鲁棒最优动力学用于活性物质 reservoir 计算机 显示英文标题

标题： Robustly optimal dynamics for active matter reservoir computing

摘要： 在储池计算（RC）范式下研究了活性物质的信息处理能力，以推断混沌信号的未来状态。 我们揭示了一种之前被忽视的代理动力学的卓越状态。 在许多条件下，它表现得异常优异，从而为更普遍地理解物理系统的计算提供了宝贵的见解。 形成有效信息处理机制的关键在于系统内在的松弛能力。 这些能力在没有实际强制特定推断目标的情况下被探测到。 实现最优计算的动力学状态位于临界阻尼阈值之下，涉及多阶段的松弛过程，并且可以在单粒子水平上读取。 在多体水平上，它为储池计算提供了在不同物理参数和推断任务下的鲁棒优化基质。 处于这一状态的系统在高度波动的驱动下表现出强烈的动态机制多样性。 代理动力学的相关性可以表达响应系统与驱动它的波动力之间的紧密关系。 由于该模型可以用物理术语解释，它有助于重新审视关于学习和非常规计算的问题，并为非平衡多体物理学提供新的理由。 显示英文摘要

摘要： 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.