The dynamics of landmark navigation in wood ants

有些蚂蚁遵循长的、视觉引导的觅食路线。研究表明，他们的路线以下，他们获得多个视觉记忆，检索他们适当的路线和指导他们的路径与存储的视觉信息。我们将通过分析每时每刻的里程碑式的指导，提高我们对这些过程的理解。为此，我们设计了一条沿着的路线，蚂蚁通过一个视觉特征来引导自己，我们可以在它们的路径中的定义点进行扰动。一个视频跟踪摄像机可以精确地记录蚂蚁的路径和身体方向，并让我们推断出蚂蚁视网膜上的地标位置。这种方法也开启了实验一个被忽视的，但重要的方面的地标指导。蚂蚁离地面很近，以至于小的颠簸经常会模糊它们对引导地标的看法，因此它们必须能够在只有间歇性视觉输入的情况下保持路径。蚂蚁如何在不平坦的地形上行走？通过记录蚂蚁对地标扰动的行为反应，我们将获得的信息将使我们更接近视觉引导的基本机制，因此将对神经科学家和研究自主机器人导航的计算机科学家有用。我们发现，蚂蚁会学习一条直线，找到放置在黑-白色垂直边缘底部的食物，在那里，黑色的一面慢慢地消失在周围实验竞技场墙壁的白色中。在这种情况下，蚂蚁的视觉任务只是在整个过程中保持眼睛前方的边缘。蚂蚁还将学习一条直线路线，以获得距离边缘很近的食物。这个任务是更加繁重的，因为在蚂蚁的方法期间，边缘的期望视网膜位置从眼睛的前部向周边移动。我们的数据表明，蚂蚁存储了一系列视觉记忆（边缘在视网膜上的期望位置），在其路线中，它检索了相应的记忆，并通过移动将边缘放置在当前期望的视网膜位置来引导自己。检索特定的所需的边缘位置似乎是提示的梯度的明显宽度，一个视觉参数，增加可靠的蚂蚁的方法，因此可以提供一个强大的检索信号。在LCD屏幕上显示梯度边缘的扰动研究将为我们提供数据来测试和改进这些假设。蚂蚁对边缘位置的突然变化的反应应该揭示当前期望的视网膜位置作为零点，其中在蚂蚁对不同幅度和方向的扰动的校正响应的方向上存在切换。我们将绘制所需边位置如何沿路线沿着变化的地图。它是连续变化的，还是像一系列离散记忆所预测的那样，以逐步的方式变化？通过改变梯度的宽度，但保持边缘位置不变，我们可以看到蚂蚁所需的边缘位置是否会按照梯度宽度决定蚂蚁当前活动记忆的概念所预测的那样发生变化。类似的实验可以告诉我们蚂蚁如何科普间歇性的视觉输入，以及运动学习在这一过程中的作用。因此，我们希望蚂蚁表现出“惯性”，并在边缘消失后继续朝着目标前进一段时间。运动轨迹的学习应该表现为惯性效应，随着经验的增加而增加，当蚂蚁跟踪振荡边缘时，惯性效应会增加。通过训练蚂蚁执行弯曲的轨迹到边缘，并在边缘消失时检查路径的曲率，将寻求轨迹学习的更强有力的证据。为了探索蚂蚁在更自然的条件下的行为，我们将分析蚂蚁在凹凸不平的地面上行走时的地标引导。

Some ants follow long, visually guided foraging routes. Study of their route following shows that they acquire multiple visual memories, retrieve them appropriately during the route and guide their paths with this stored visual information. We will improve our understanding of these processes by analysing landmark guidance on a moment-by-moment basis. For this purpose, we have developed a route along which ants guide themselves by means of a single visual feature that we can perturb at defined points during their path. A video-tracking camera gives a precise record of the ant's path and its body orientation, and lets us infer the landmark's position on the ant's retina. This methodology also opens to experiment a neglected but significant aspect of landmark guidance. Ants are so close to the ground that small bumps will frequently obscure their view of guiding landmarks so that they must be able to maintain a path with only intermittent access to visual input. How do ants navigate over uneven terrain? The information that we will obtain by recording the ant's behavioural responses to landmark perturbations will bring us closer to the underlying mechanisms of visual guidance, and so will be of use to neuroscientists and to computer scientists working on the navigation of autonomous robots. We have found that ants will learn a straight route to food placed at the base of a black-white vertical edge, where the black side fades slowly into the white of the walls of the surrounding experimental arena. In this case, the ant's visual task is simply to keep the edge at the front of the eye throughout its approach. Ants will also learn a straight route to food placed at a short distance to the side of the edge. This task is more taxing as the desired retinal position of the edge shifts from the front towards the periphery of the eye during the ant's approach. Our data suggest that the ant stores a sequence of visual memories (the desired positions of the edge on its retina), and that during its route it retrieves the appropriate memory and guides itself by moving to place the edge at the currently desired retinal position. The retrieval of particular desired edge positions seems to be cued by the gradient's apparent width, a visual parameter that increases reliably during the ant's approach and so can provide a robust retrieval signal. Perturbation studies with gradient edges displayed on an LCD screen will give us data to test and improve these hypotheses. The ant's reaction to abrupt changes in the position of the edge should reveal the current desired retinal position as the null point, where there is a switch in the direction of the ant's corrective response to perturbations of different magnitudes and directions. We will map how the desired edge position changes along the route. Does it change continuously or in a step-wise manner, as predicted by a sequence of discrete memories? By altering the width of the gradient, but keeping edge position constant, we can see whether the ant's desired edge position shifts as predicted by the notion that the width of the gradient determines the ant's currently active memory. Similar experiments can tell us how ants cope with intermittent visual input and the role of motor learning in this process. Thus, we expect ants to show 'inertia' and continue towards the goal for a while after the edge is made to disappear. The learning of a motor trajectory should show itself as inertial effects that increase with experience and an increased sluggishness when the ant tracks oscillating edges. Stronger evidence for trajectory learning will be sought by training ants to perform curved trajectories to an edge that always moves during their approach and examining the curvature of the path when the edge vanishes. To explore how ants behave in more natural conditions, we will analyse landmark guidance when ants walk over bumpy ground.