A model of ant route navigation driven by scene familiarity.
A model of ant route navigation driven by scene familiarity.
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DOI：
10.1371/journal.pcbi.1002336
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发表时间：
2012-01
影响因子：
4.3
通讯作者：
Philippides A
中科院分区：
文献类型：
--
作者：
Baddeley B;Graham P;Husbands P;Philippides A
在本文中，我们提出了一个模型的视觉引导的路线导航蚂蚁，捕获已知的属性的真实的行为，同时保留机械的简单性，从而生物兼容性。对于蚂蚁来说，运动和观察方向的耦合意味着熟悉的视图指定了熟悉的运动方向。由于沿着习惯路线所经历的视图将更熟悉，因此路线导航可以被重新转换为对熟悉视图的搜索。这种搜索可以用一个简单的扫描程序来执行，这是一种观察到的蚂蚁行为。我们在模拟中测试了这一建议的路线导航策略，通过学习一系列的路线，通过视觉上杂乱的环境组成的对象，只有对天空的轮廓区分。在第一种情况下，我们确定视图熟悉度与训练过程中经历的一组视图进行详尽的比较。在进一步的实验中，我们训练一个人工神经网络进行熟悉性歧视使用的训练意见。我们的研究结果表明，不仅是成功的方法，而且学习的路线显示出许多沙漠蚂蚁的路线的特点。因此，我们相信该模型代表了迄今为止唯一详细和完整的昆虫路线指导模型。更重要的是，该模型提供了一个一般的演示，视觉引导的路线可以产生简约的机制，不指定何时或学习什么，也没有单独的路线到序列的航点。昆虫导航的兴趣来自不同的学科，如心理学和工程学，在很大程度上是因为性能是用有限的脑力实现的。沙漠蚂蚁是特别令人印象深刻的导航员，能够快速学习长距离的视觉引导觅食路线。它们优雅的行为为仿生工程师提供了灵感，并为心理学家展示了复杂空间行为的最低机械要求。本着这种精神，我们已经开发了一个简约的路线导航模型，捕捉蚂蚁路线的许多已知属性。我们的模型使用了一个神经网络，该网络是用沿着经历的视觉场景训练的，以评估任何视图的熟悉度。随后的路线导航涉及一个简单的行为例程，其中模拟蚂蚁扫描世界并按照网络确定的最熟悉的方向移动。该算法使用相同的机制展示了地点搜索和路线导航。至关重要的是，在我们的模型中，没有必要指定何时学习或学习什么，也没有必要将路线划分为路点序列;从而提供了概念证明，即可以在没有这些元素的情况下实现路线导航。因此，我们认为它代表了迄今为止唯一详细和完整的昆虫路线指导模型。
In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints. The interest in insect navigation from diverse disciplines such as psychology and engineering is to a large extent because performance is achieved with such limited brain power. Desert ants are particularly impressive navigators, able to rapidly learn long, visually guided foraging routes. Their elegant behaviours provide inspiration to biomimetic engineers and for psychologists demonstrate the minimal mechanistic requirements for complex spatial behaviours. In this spirit, we have developed a parsimonious model of route navigation that captures many of the known properties of ants routes. Our model uses a neural network trained with the visual scenes experienced along a route to assess the familiarity of any view. Subsequent route navigation involves a simple behavioural routine, in which the simulated ant scans the world and moves in the most familiar direction, as determined by the network. The algorithm exhibits both place-search and route navigation using the same mechanism. Crucially, in our model it is not necessary to specify when or what to learn, nor separate routes into sequences of waypoints; thereby providing proof of concept that route navigation can be achieved without these elements. As such, we believe it represents the only detailed and complete model of insect route guidance to date.
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影响因子：
1.6
作者：
Baddeley, Bart;Graham, Paul;Husbands, Philip
通讯作者：
Husbands, Philip
影响因子：
4.3
作者：
Cruse H;Wehner R
通讯作者：
Wehner R
影响因子：
2.8
作者：
de Ibarra, Natalie Hempel;Philippides, Andrew;Collett, Thomas S.
通讯作者：
Collett, Thomas S.
影响因子：
1.9
作者：
Basten, Kai;Mallot, Hanspeter A.
通讯作者：
Mallot, Hanspeter A.
DOI：
10.1073/pnas.1001401107
发表时间：
2010-06-22
影响因子：
11.1
作者：
Collett, Matthew
通讯作者：
Collett, Matthew