a model of ant route navigation driven by scene familiarity模型由现场熟悉ant路线导航.pdfVIP

A Model of Ant Route Navigation Driven by Scene Familiarity 1 2 1 1 Bart Baddeley *, Paul Graham , Philip Husbands , Andrew Philippides 1 Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Brighton, United Kingdom, 2 Centre for Computational Neuroscience and Robotics, School of Life Sciences, University of Sussex, Brighton, United Kingdom Abstract 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 gener