智能活性物质中导航速度的信息界限
Information bound on navigation speed in smart active matter
AI总结：
本文提出了一种自适应活性粒子模型，通过结合间歇运动与Cramér-Rao不等式，推导出导航速度的信息界，揭示了认知系统中最优感知持续时间与噪声-可靠性权衡的关系。
摘要：
在类生命系统中，智能行为往往源于获取、处理和利用信息的能力。尽管活性物质为研究类生命动力学提供了框架，但通常忽略了内部的信息处理和决策机制。本文引入了一种自适应的活性粒子模型，该模型利用最小的信息处理能力以导航至远处的目标。通过结合基于更新的间歇运动与Cramér-Rao不等式，我们推导出一个适用于广泛信息处理策略的导航速度界。该框架捕捉了认知系统的典型特征，包括最优的感知持续时间和平衡噪声与可靠性的速度-准确性权衡。允许存储的信息在行动前退化表明，尽管退化会减缓导航，但这种权衡主要由外部方向噪声决定，并且对记忆退化表现出显著的不敏感性。
英文摘要：
Intelligent behavior in life-like systems often arises from the ability to gather, process, and act on information. While active matter provides a framework for studying life-like dynamics, it typically omits internal information-processing and decision-making. Here we introduce an adaptive active particle model that uses minimal information processing capabilities in order to navigate towards a distant target. By combining renewal-based intermittent motion with the Cramér-Rao inequality, we derive a bound on the navigation speed valid for a wide range of information processing strategies. The framework captures hallmark features of cognitive systems, including optimal sensing durations and a speed-accuracy trade-off that balances noise and reliability. Allowing stored information to degrade before action reveals that although deterioration slows navigation, the trade-off remains governed primarily by external orientational noise and is remarkably insensitive to memory decay.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
