小柯机器人

以色列耶路撒冷希伯来大学Ran Nathan、David Shohami以及特拉维夫大学Sivan Toledo研究组合作取得最新进展。他们利用新的高通量追踪系统揭示野生蝙蝠基于认知图的导航。该研究于2020年7月10日发表于《科学》。

使用同时高精度和高分辨率地追踪数十只动物的系统，他们收集了172个大型觅食埃及果蝠的大型数据集，其中包括在4年的3449个夜蝙蝠中收集的1800万种以上当地信息。详细的航迹分析，结合易位实验和详尽的果树制图，发现野蝙蝠很少表现出随机搜索，而是在目标频繁，长而直的飞行中反复觅食，其中包括频繁的捷径。

通过模拟、时滞嵌入和其他轨迹分析，排除了基于地图的替代策略。他们的结果与认知地图（如导航）的预期相符，并支持先前从圈养蝙蝠获得的神经生物学证据。

据悉，关于“认知图”（以空间为中心的表示）的七十年研究取得了关键的神经生物学进展，但仍然缺乏自由放养的野生动物的现场证据。

附：英文原文

Title: Cognitive map–based navigation in wild bats revealed by a new high-throughput tracking system

Author: Sivan Toledo, David Shohami, Ingo Schiffner, Emmanuel Lourie, Yotam Orchan, Yoav Bartan, Ran Nathan

Issue&Volume: 2020/07/10

Abstract: Seven decades of research on the “cognitive map,” the allocentric representation of space, have yielded key neurobiological insights, yet field evidence from free-ranging wild animals is still lacking. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18 million localizations collected over 3449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments and exhaustive mapping of fruit trees, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long, and straight flights that include frequent shortcuts. Alternative, non–map-based strategies were ruled out by simulations, time-lag embedding, and other trajectory analyses. Our results are consistent with expectations from cognitive map–like navigation and support previous neurobiological evidence from captive bats.

DOI: 10.1126/science.aax6904

Source: https://science.sciencemag.org/content/369/6500/188