抗噪声的自适应跑-翻策略导航
Noise-robust navigation from an adaptive run-and-tumble policy
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中文总结 AI 辅助
研究针对噪声信号下的生物体导航问题，提出源于最优性原理的跑-翻策略，该策略自带的方差适应机制可在噪声增大时维持趋化漂移有限，虽会降低安静环境性能，但为抗噪声导航提供了新方案。
中文摘要 AI 辅助
当引导生物体导航的信号存在噪声时，生物体如何进行导航？方差适应（即对噪声的敏感性进行重新缩放）在感觉系统中很常见，但其在导航中的作用尚未被探索。我们引入了一种最小活性布朗粒子，其跑-翻策略源于最优性原理，方差适应是该策略的一部分。当噪声增大时，适应机制使趋化漂移保持有限，而非适应粒子的趋化漂移会呈指数级崩溃。此外，适应还存在成本，会降低安静环境下的性能，且需要对适应敏感性进行调谐。
英文摘要
How do organisms navigate when the signals guiding them are noisy? Variance adaptation, the rescaling of sensitivity to noise, is common in sensory systems, but its role in navigation is unexplored. We introduce a minimal active Brownian particle whose run-and-tumble policy follows from an optimality principle. Variance adaptation emerges as part of this policy. Adaptation keeps chemotactic drift finite as noise grows, while a non-adaptive particle's collapses exponentially. Adaptation also carries a cost, degrading performance in quiet environments and requiring a tuned adaptation sensitivity.
