Paffhausen Benjamin H, Petrasch Julian, Wild Benjamin, Meurers Thierry, Schülke Tobias, Polster Johannes, Fuchs Inga, Drexler Helmut, Kuriatnyk Oleksandra, Menzel Randolf, Landgraf Tim
Department of Biology, Chemistry and Pharmacy, Institute of Neurobiology, Free University of Berlin, Berlin, Germany.
Dahlem Center for Machine Learning and Robotics, Department of Mathematics and Computer Science, Institute of Computer Science, Free University of Berlin, Berlin, Germany.
Front Behav Neurosci. 2021 Jul 20;15:690571. doi: 10.3389/fnbeh.2021.690571. eCollection 2021.
Navigating animals combine multiple perceptual faculties, learn during exploration, retrieve multi-facetted memory contents, and exhibit goal-directedness as an expression of their current needs and motivations. Navigation in insects has been linked to a variety of underlying strategies such as path integration, view familiarity, visual beaconing, and goal-directed orientation with respect to previously learned ground structures. Most works, however, study navigation either from a field perspective, analyzing purely behavioral observations, or combine computational models with neurophysiological evidence obtained from lab experiments. The honey bee () has long been a popular model in the search for neural correlates of complex behaviors and exhibits extraordinary navigational capabilities. However, the neural basis for bee navigation has not yet been explored under natural conditions. Here, we propose a novel methodology to record from the brain of a copter-mounted honey bee. This way, the animal experiences natural multimodal sensory inputs in a natural environment that is familiar to her. We have developed a miniaturized electrophysiology recording system which is able to record spikes in the presence of time-varying electric noise from the copter's motors and rotors, and devised an experimental procedure to record from mushroom body extrinsic neurons (MBENs). We analyze the resulting electrophysiological data combined with a reconstruction of the animal's visual perception and find that the neural activity of MBENs is linked to sharp turns, possibly related to the relative motion of visual features. This method is a significant technological step toward recording brain activity of navigating honey bees under natural conditions. By providing all system specifications in an online repository, we hope to close a methodological gap and stimulate further research informing future computational models of insect navigation.
导航动物整合多种感知能力，在探索过程中学习，检索多方面的记忆内容，并表现出目标导向性，以此表达它们当前的需求和动机。昆虫的导航与多种潜在策略相关联，如路径整合、视觉熟悉度、视觉信标以及相对于先前学习的地面结构的目标导向定位。然而，大多数研究要么从野外视角研究导航，纯粹分析行为观察结果，要么将计算模型与从实验室实验获得的神经生理学证据相结合。长期以来，蜜蜂一直是寻找复杂行为神经关联的热门模型，并且展现出非凡的导航能力。然而，蜜蜂导航的神经基础尚未在自然条件下得到探索。在此，我们提出一种从搭载在直升机上的蜜蜂大脑进行记录的新方法。通过这种方式，动物在其熟悉的自然环境中体验自然的多模态感官输入。我们开发了一种小型化电生理记录系统，该系统能够在存在来自直升机电机和旋翼的时变电噪声的情况下记录尖峰信号，并设计了一种从蘑菇体外部神经元（MBENs）进行记录的实验程序。我们结合动物视觉感知的重建分析所得的电生理数据，发现MBENs的神经活动与急转弯有关，可能与视觉特征的相对运动有关。这种方法是朝着在自然条件下记录导航蜜蜂大脑活动迈出的重要技术一步。通过在在线知识库中提供所有系统规格，我们希望填补方法学上的空白，并刺激进一步的研究，为未来昆虫导航的计算模型提供信息。
