Visual navigation in ants: from visual ecology to brain

所有的动物都有基本的方向感，但大多数动物也可以学习环境中的线索，使它们能够在熟悉的地点之间导航。对于人类和蚂蚁来说，这些学习到的引导线索主要是视觉的，正在开发的自主机器人也使用类似的线索。我们的目标是了解蚂蚁的小大脑是如何能够支持导航壮举-没有GPS -这是上级于任何目前的机器人，往往比人类更好。蚂蚁有强大的进化压力，成为成功的导航员，因为为了生存，它们需要寻找食物并将其带回巢穴。他们已经被证明可以快速学习巢穴和食物位置周围的视觉线索，并通过这些关键位置之间的复杂地形发展出长路线的记忆。但是记忆中储存的是什么，又是如何用来指导行为的呢？备选假设包括：A）它们从特定的角度存储周围场景的“快照”，并在返回同一地点时尝试匹配这些快照; B）它们检测突出的地标或特征，并使用这些位置来三角测量它们的位置; C）、他们将视觉场景处理成一个紧凑而强大的内部表示，他们可以灵活地使用它来识别他们的当前位置，并确定正确的路线，我们将使用混合实验和木蚁模型来研究这些可能性。我们的方法将包括“自上而下”的分析，什么信息是真正可用的自然景观的木蚁栖息地的导航，和自下而上的调查，大脑区域似乎是至关重要的导航任务。后一种方法的动机是来自其他昆虫的证据，即模式记忆（与假设A一致）存储在称为蘑菇体的大脑区域中，而抽象的方向信息（与假设C一致）则在不同的区域进行处理，即中央复合体。我们将进行有史以来第一次实验，以测试这些区域的大脑病变是否会影响蚂蚁的视觉导航。我们还将开发一种新颖的跑步机系统，其中蚂蚁可以放置在球体上，自由行走，同时补偿它们的向前和旋转运动，以保持它们处于相同的位置和方向。这将使我们能够在虚拟世界中测试蚂蚁，在虚拟世界中，我们可以独立地操纵视觉场景的不同部分或属性，并跟踪对导航决策的直接影响。这项工作将通过开发一个等效的“虚拟蚂蚁”模拟来补充，该模拟可以用相同的实验刺激进行测试。我们的虚拟蚂蚁的大脑将包含与上述假设相对应的计算算法，这样我们就可以预测如果假设是正确的，真实的蚂蚁应该做什么。我们还将测试与损伤实验中测试的大脑回路相对应的神经网络模型。了解蚂蚁的大脑应该让我们深入了解其他动物（包括人类）使用的导航机制，并为技术提供新的解决方案。

All animals have a basic sense of direction, but most can also learn cues in their environment to enable them to navigate between familiar locations. For both humans and ants, these learnt guidance cues are primarily visual, and autonomous robots are being developed that use similar cues. Our goal is to understand how the tiny brain of the ant is capable of supporting navigational feats - without GPS - that are superior to any current robots, and often better than humans. Ants have strong evolutionary pressure to be successful navigators, as to survive they need to forage for food and bring it back to their nest. They have been shown to rapidly learn visual cues surrounding nest and food locations, and to develop memories for long routes through complex terrain between these key locations. But what is stored in memory, and how is used to guide behaviour? Alternative hypotheses include: A) they store 'snapshots' of the surrounding scene from a particular point of view and try to match these when returning to the same place; B) they detect prominent landmarks or features, and use those locations to triangulate their position; C) they process the visual scene into a compact and robust internal representation that they can use flexibly to recognise their current location and to determine the correct course to a goal.We will use a mixture of experiments and modelling on wood ants to investigate these possibilities. Our approaches will include 'top-down' analysis of what information is actually available in the natural scenery of the wood ant habitat for navigation, and bottom-up investigation of which brain areas seem to be crucial in navigational tasks. The latter approach is motivated by evidence from other insects that memory of patterns (consistent with hypothesis A) are stored in a brain area called the mushroom bodies, whereas abstracted directional information (consistent with hypothesis C) is processed in a different area, the central complex. We will carry out the first ever experiments to test if brain lesions in these areas affect visual navigation in ants. We will also develop a novel treadmill system in which ants can be placed on a sphere, and walk freely while their forward and rotational motion is compensated to keep them in the same position and orientation. This will allow us to test ants in a virtual world in which we can independently manipulate different parts or properties of the visual scene, and track the immediate effect on navigational decisions. This work will be complemented by developing an equivalent 'virtual ant' simulation that can be tested with the same experimental stimuli. The brain of our virtual ant will contain computational algorithms corresponding to the hypotheses above, so that we can predict what the real ant should do if that hypothesis is correct. We will also test neural network models corresponding to the brain circuits tested in the lesioning experiments. Understanding the ant brain should give us insight into navigational mechanisms used by other animals, including humans, and also suggest new solutions for technology.