Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action.
Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action.
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通过行为作用塑造视觉输入的动力学，可以促进昆虫中的空间视觉。
DOI：
10.3389/fncir.2012.00108
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发表时间：
2012
影响因子：
3.5
通讯作者：
Lindemann JP
中科院分区：
文献类型：
--
作者：
Egelhaaf M;Boeddeker N;Kern R;Kurtz R;Lindemann JP
昆虫，如苍蝇或蜜蜂，凭借其微型大脑，能够控制高度特技飞行动作，并解决空间视觉任务，如避免与障碍物相撞，降落在物体上，甚至根据环境线索定位以前学习的不明显的目标。在解决此类空间任务方面，这些昆虫的表现仍然好于人造自主飞行系统。为了实现它们非凡的表现，苍蝇和蜜蜂通过它们独特的行为动作来主动塑造它们眼睛上的图像流(光流)的动态。通过扫视飞行和凝视策略将旋转光流分量与平移光流分量分离，极大地促进了关于环境空间布局信息的神经处理。因此，这种主动的视觉策略使神经系统能够以一种特别有效和节俭的方式解决明显复杂的空间视觉任务。这篇综述的关键思想是，生物制剂，如苍蝇或蜜蜂，通过与环境的主动相互作用，而不是简单地处理被动获得的关于世界的信息，获得了至少一部分作为自主系统的力量。这些智能体-环境的相互作用导致了在广泛复杂的环境中的适应性行为。即使是有微小大脑的动物，如昆虫，也能够通过最佳地利用闭合的动作-知觉环路在其行为环境中表现得非常好。模型模拟和机器人实施表明，运动计算和视觉引导飞行控制的智能生物机制可能有助于找到技术解决方案，例如，在设计携带小型化、低重量机载处理器的微型飞行器时。
Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes (“optic flow”). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action–perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor.
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