Bayesian issues in ant navigation

蚂蚁导航中的贝叶斯问题

• 批准号: BB/I014543/1
• 负责人: Barbara Webb
• 金额: $ 41.53万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI014543%2F1
• 关键词: Bayesian; issues; ant; navigation

Our brains have to deal with ambiguity and uncertainty, and an increasingly popular explanation of how they do so is based on Bayesian reasoning. In essence, this says we estimate the probability of a certain state of affairs (such as 'I am at home') on the basis of both current sensory inputs ('This looks like my house') and prior expectations ('Given my starting location, and the speed I was travelling, I wouldn't expect to be home yet'). Bayes theorem tells us how we should combine these factors to obtain the best estimate of our current state. But is this form of reasoning universal? An ideal way to investigate this issue is to look at 'simple' animals that have to solve analogous problems. And an effective way to test our understanding of what these animals do is to implement and test our hypotheses in robot models that operate in the same sensory environment. A clear example of an animal solving such problems is found in desert ants, who forage individually and without the use of chemical trails, yet can efficiently relocate their nest or a food source over long distances in barren or complex environments. Recent studies have shown that ants can individually learn and recall specific routes through cluttered environments that force detours and prevent the use of distant landmarks. Ant navigation depends on two main mechanisms: they can keep track of how far they have moved and in which direction from the nest and continuously update a vector that points back home; and they can recognise familiar visual surroundings and use these to determine which way to go. Do they integrate these cues in an optimal fashion? What if one or other cue is more or less variable? Can they use one of these cues to disambiguate the other? We can make the investigation of these issues rigorous and quantitative by drawing on methods developed for robot navigation. We will first determine what ants actually see as they develop new routes, by following ants as they forage, and capturing images from the ant's eye point of view. We will feed this information into algorithms that should be able to learn a map of the area. We can systematically vary the type of information available, its reliability, and the computational methods used to update the map, and compare the performance to ants. Further experiments to see what the ants do when the same variables are manipulated will serve to evaluate the models. Finally, the models will also be tested in the real world by implementing them on a small robot able to navigate in the ant environment.

我们的大脑必须处理模糊性和不确定性，一个越来越流行的解释是基于贝叶斯推理。从本质上讲，这意味着我们根据当前的感官输入（“这看起来像我的房子”）和先前的预期（“考虑到我的出发地点和我旅行的速度，我不希望回家”）来估计某种状态（例如“我在家”）的概率。贝叶斯定理告诉我们应该如何将这些因素联合收割机组合起来，以获得对我们当前状态的最佳估计。但是，这种推理形式具有普遍性吗？研究这个问题的一个理想方法是观察那些必须解决类似问题的“简单”动物。测试我们对这些动物行为的理解的一个有效方法是在机器人模型中实现和测试我们的假设，这些机器人模型在相同的感官环境中运行。动物解决这些问题的一个明显例子是沙漠蚂蚁，它们单独觅食，不使用化学痕迹，但可以在贫瘠或复杂的环境中有效地长距离重新安置它们的巢穴或食物来源。最近的研究表明，蚂蚁可以单独学习和回忆特定的路线，通过混乱的环境，迫使绕道而行，并防止使用遥远的地标。蚂蚁的导航依赖于两个主要机制：它们可以跟踪自己移动了多远，以及从巢穴向哪个方向移动，并不断更新指向家的矢量;它们可以识别熟悉的视觉环境，并利用这些来确定走哪条路。他们是否以最佳方式整合这些线索？如果一个或另一个线索或多或少是可变的呢？他们能用其中一个线索来消除另一个线索的歧义吗？我们可以通过借鉴为机器人导航开发的方法来对这些问题进行严格和定量的调查。我们将首先确定蚂蚁在开辟新路线时实际看到的是什么，方法是跟踪蚂蚁觅食，并从蚂蚁的视角捕捉图像。我们将把这些信息输入算法，算法应该能够学习该地区的地图。我们可以系统地改变可用信息的类型，其可靠性以及用于更新地图的计算方法，并将其性能与蚂蚁进行比较。进一步的实验，看看蚂蚁做什么时，同样的变量被操纵将有助于评估模型。最后，这些模型也将在真实的世界中进行测试，方法是在一个能够在蚂蚁环境中导航的小型机器人上实现它们。

项目成果

Reconstructing the entire visual history of individual desert ants

重建个体沙漠蚂蚁的整个视觉历史

• 期刊: Conference Presentation
• 作者: Mangan, M.

Using insects to understand the minimum cognitive requirements for route navigation

利用昆虫了解路线导航的最低认知要求

• 发表时间: 2013
• 期刊: Conference Presentation
• 作者: Mangan, M.

Biomimetic and Biohybrid Systems - 4th International Conference, Living Machines 2015, Barcelona, Spain, July 28 - 31, 2015, Proceedings

仿生和生物混合系统 - 第四届国际会议，Living Machines 2015，西班牙巴塞罗那，2015 年 7 月 28 - 31 日，会议记录

• DOI: 10.1007/978-3-319-22979-9_46
• 发表时间: 2015
• 作者: Martinez-Hernandez U

How variation in head pitch could affect image matching algorithms for ant navigation.

• DOI: 10.1007/s00359-015-1005-8
• 发表时间: 2015-06
• 期刊: Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology
• 作者: Ardin P;Mangan M;Wystrach A;Webb B
• 通讯作者: Webb B

Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments.

• DOI: 10.1371/journal.pcbi.1004683
• 发表时间: 2016-02
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Ardin P;Peng F;Mangan M;Lagogiannis K;Webb B
• 通讯作者: Webb B