Spatial learning strategies, visual information acquisition and efficiency of local navigation

空间学习策略、视觉信息获取和局部导航效率

• 批准号: 60188378
• 负责人: Professor Dr. Martin Egelhaaf
• 金额: --
• 依托单位: Arbeitsgruppe Neurobiologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2012-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/60188378?language=en
• 关键词: Spatial; learning; strategies; visual; information

Some insects, such as bees or ants, demonstrate an extraordinary ability to learn the spatial location of their food sites and to return to these places. In bees the visual information relevant for navigation in the vicinity of the goal is actively organized by structured learning flights. Although it is clear that learning flights play an important role in establishing a visual representation of the goal location, it is still largely unknown (1) which components of this behavior are relevant to finding the goal, (2) what mechanisms bees employ to extract the relevant information from the complex spatio-temporal retinal input, (3) how bees use this information to establish spatial representations and to control behavior and (4) how these mechanisms can cope with unpredictable changes in the environment. To answer these fundamental questions will be the main objective of the proposed project. Novel approaches will be employed to separate behavioral sequences monitored with high-speed cameras into distinctive components and to reconstruct what the bee has seen during her learning and goal-seeking flights. The behavioral analysis will be complemented in a next project phase by modeling and implementing biological principles of local navigation in a gantry robot moving an artificial vision system in 3D space. Hence, this project, though mainly concerned with basic research, is, in the long run, likely to be relevant for artificial autonomous navigation systems.

一些昆虫，如蜜蜂或蚂蚁，表现出非凡的能力，学习他们的食物网站的空间位置，并返回这些地方。在蜜蜂的视觉信息相关的导航在附近的目标是积极组织的结构化学习飞行。虽然很明显，学习飞行在建立目标位置的视觉表示中起着重要作用，但在很大程度上仍然未知（1）这种行为的哪些组成部分与寻找目标相关，（2）蜜蜂采用什么机制从复杂的时空视网膜输入中提取相关信息，（3）蜜蜂如何利用这些信息建立空间表征和控制行为，以及（4）这些机制如何科普环境中不可预测的变化。回答这些基本问题将是拟议项目的主要目标。将采用新的方法将高速摄像机监测的行为序列分离为独特的组件，并重建蜜蜂在学习和寻找目标的飞行过程中所看到的。行为分析将在下一个项目阶段的补充建模和实施生物学原理的局部导航的龙门机器人移动人工视觉系统在3D空间。因此，该项目虽然主要涉及基础研究，但从长远来看，可能与人工自主导航系统有关。