A model of ant navigation based on visual prediction

A model of visual navigation in ants is presented which is based on a simple network predicting the changes of a visual scene under translatory movements. The model contains two behavioral components: the acquisition of multiple snapshots in different orientations during a learning walk, and the selection of a movement direction by a scanning behavior where the ant searches through different headings. Both components fit with observations in experiments with desert ants. The model is in most aspects biologically plausible with respect to the equivalent neural networks, and it produces reliable homing behavior in a simulated environment with a complex random surface texture. The model is closely related to the algorithmic min-warping method for visual robot navigation which shows good homing performance in real-world environments.

► We present a novel, holistic model of visual navigation in ants. ► Navigation of ants could be based on the prediction of image changes. ► Navigation behavior reduces the complexity of the required neural networks. ► Learning walks and scanning behavior can be explained by the snapshot concept. ► Holistic models are biologically more plausible than correspondence models.